We focus on the development of model selection criteria in linear mixed models. In particular, we propose the model selection criteria following the Mallows’ Conceptual Predictive Statistic (Cp) [1] [2] in linear mix...We focus on the development of model selection criteria in linear mixed models. In particular, we propose the model selection criteria following the Mallows’ Conceptual Predictive Statistic (Cp) [1] [2] in linear mixed models. When correlation exists between the observations in data, the normal Gauss discrepancy in univariate case is not appropriate to measure the distance between the true model and a candidate model. Instead, we define a marginal Gauss discrepancy which takes the correlation into account in the mixed models. The model selection criterion, marginal Cp, called MCp, serves as an asymptotically unbiased estimator of the expected marginal Gauss discrepancy. An improvement of MCp, called IMCp, is then derived and proved to be a more accurate estimator of the expected marginal Gauss discrepancy than MCp. The performance of the proposed criteria is investigated in a simulation study. The simulation results show that in small samples, the proposed criteria outperform the Akaike Information Criteria (AIC) [3] [4] and Bayesian Information Criterion (BIC) [5] in selecting the correct model;in large samples, their performance is competitive. Further, the proposed criteria perform significantly better for highly correlated response data than for weakly correlated data.展开更多
WOMBAT is a software package for quantitative genetic analyses of continuous traits, fitting a linear, mixed model; estimates of covariance components and the resulting genetic parameters are obtained by restricted ma...WOMBAT is a software package for quantitative genetic analyses of continuous traits, fitting a linear, mixed model; estimates of covariance components and the resulting genetic parameters are obtained by restricted maximum likelihood. A wide range of models, comprising numerous traits, multiple fixed and random effects, selected genetic covariance structures, random regression models and reduced rank estimation are accommodated. WOMBAT employs up-to-date numerical and computational methods. Together with the use of efficient compilers, this generates fast executable programs, suitable for large scale analyses. Use of WOMBAT is illustrated for a bivariate analysis. The package consists of the executable program, available for LINUX and WINDOWS environments, manual and a set of worked example, and can be downloaded free of charge from http：//agbu. une.edu.au/-kmeyer/wombat.html展开更多
The traditional method of mechanical gear driving simulation includes gear pair method and solid to solid contact method. The former has higher solving efficiency but lower results accuracy; the latter usually obtains...The traditional method of mechanical gear driving simulation includes gear pair method and solid to solid contact method. The former has higher solving efficiency but lower results accuracy; the latter usually obtains higher precision of results while the calculation process is complex, also it is not easy to converge. Currently, most of the researches are focused on the description of geometric models and the definition of boundary conditions. However, none of them can solve the problems fundamentally. To improve the simulation efficiency while ensure the results with high accuracy, a mixed model method which uses gear tooth profiles to take the place of the solid gear to simulate gear movement is presented under these circumstances. In the process of modeling, build the solid models of the mechanism in the SolidWorks firstly; Then collect the point coordinates of outline curves of the gear using SolidWorks API and create fit curves in Adams based on the point coordinates; Next, adjust the position of those fitting curves according to the position of the contact area; Finally, define the loading conditions, boundary conditions and simulation parameters. The method provides gear shape information by tooth profile curves; simulates the mesh process through tooth profile curve to curve contact and offer mass as well as inertia data via solid gear models. This simulation process combines the two models to complete the gear driving analysis. In order to verify the validity of the method presented, both theoretical derivation and numerical simulation on a runaway escapement are conducted. The results show that the computational efficiency of the mixed model method is 1.4 times over the traditional method which contains solid to solid contact. Meanwhile, the simulation results are more closely to theoretical calculations. Consequently, mixed model method has a high application value regarding to the study of the dynamics of gear mechanism.展开更多
In this article, the problem of estimating the covariance matrix in general linear mixed models is considered. Two new classes of estimators obtained by shrinking the eigenvalues towards the origin and the arithmetic ...In this article, the problem of estimating the covariance matrix in general linear mixed models is considered. Two new classes of estimators obtained by shrinking the eigenvalues towards the origin and the arithmetic mean, respectively, are proposed. It is shown that these new estimators dominate the unbiased estimator under the squared error loss function. Finally, some simulation results to compare the performance of the proposed estimators with that of the unbiased estimator are reported. The simulation results indicate that these new shrinkage estimators provide a substantial improvement in risk under most situations.展开更多
Gravity gradients acquired by the Gravity field and steady-state Ocean Circulation Explorer(GOCE) do not cover the entire earth because of its sun-synchronous orbit leaving data gaps with a radius of about 6.5° i...Gravity gradients acquired by the Gravity field and steady-state Ocean Circulation Explorer(GOCE) do not cover the entire earth because of its sun-synchronous orbit leaving data gaps with a radius of about 6.5° in the polar regions.Previous studies showed that the loss of data in the polar regions deteriorates the accuracy of the low order(or near zonal) coefficients of the earth gravity model,which is the so-called polar gap problem in geodesy.In order to find a stable solution for the earth gravity model from the GOCE gravity gradients,three models,i.e.the Gauss-Markov model,light constraint model and the mixed model,are compared and evaluated numerically with the gravity gradient simulated with the EGM2008.The comparison shows that the Best Linear Uniformly Unbiased Estimation(BLUUE) estimator of the mixed model can solve the polar gap problem as effectively as the light constraint model;furthermore,the mixed model is more rigorous in dealing with the supplementary information and leads to a better accuracy in determining the global geoid.展开更多
Several computer packages have been developed to accomplish improved programs for animal breeding and genetic selection. This paper described most of the currant software and provided suggestions for improved software...Several computer packages have been developed to accomplish improved programs for animal breeding and genetic selection. This paper described most of the currant software and provided suggestions for improved software. Khon Kaen University, Thailand, will provide free of charge the new software developed at Khon Kaen University by the author of this paper. The contact for requesting the software is listed: monchai@kku.ac.th.展开更多
Mixed model analysis procedure was used to analyze the effect of fertilizer application on the Fresh Fruit Bunch (FFB) yield of oil palm. This was with a view to achieve the most appropriate and a robust model for ana...Mixed model analysis procedure was used to analyze the effect of fertilizer application on the Fresh Fruit Bunch (FFB) yield of oil palm. This was with a view to achieve the most appropriate and a robust model for analyzing yield response for fertilizer application in oil palm. In this study, a mixed model analysis procedure was used to analyze yield data obtained from a fertilizer trial conducted between 1997 and 2005. In mixed effect model, replicates and years were used as block. In contrast the fixed effect ANOVA model usually lumped up replicates and years as a random error. In the model replicates were used as block with no block interaction, replicates as block with allowance for block-fertilizer interaction, years as block with allowance for block-fertilizer interaction, and years and replicates as block with allowance for year fertilizer and replicate-fertilizer interaction. Mixed model theory was also used to provide the explicit description of the design matrices in the models. Also, hypotheses relevant to each model were formulated and used to test for specific effects in the models such as, fixed part, random part and interacting parts using appropriate error terms as determined by the derived Expected Mean Squares (EMS). The results revealed that at 5% significant level (p展开更多
Today, Linear Mixed Models (LMMs) are fitted, mostly, by assuming that random effects and errors have Gaussian distributions, therefore using Maximum Likelihood (ML) or REML estimation. However, for many data sets, th...Today, Linear Mixed Models (LMMs) are fitted, mostly, by assuming that random effects and errors have Gaussian distributions, therefore using Maximum Likelihood (ML) or REML estimation. However, for many data sets, that double assumption is unlikely to hold, particularly for the random effects, a crucial component </span></span><span style="font-family:Verdana;"><span style="font-family:Verdana;"><span style="font-family:Verdana;">in </span></span></span><span style="font-family:Verdana;"><span style="font-family:Verdana;"><span style="font-family:Verdana;">which assessment of magnitude is key in such modeling. Alternative fitting methods not relying on that assumption (as ANOVA ones and Rao</span></span></span><span style="font-family:Verdana;"><span style="font-family:Verdana;"><span style="font-family:Verdana;">’</span></span></span><span style="font-family:Verdana;"><span style="font-family:Verdana;"><span style="font-family:Verdana;">s MINQUE) apply, quite often, only to the very constrained class of variance components models. In this paper, a new computationally feasible estimation methodology is designed, first for the widely used class of 2-level (or longitudinal) LMMs with only assumption (beyond the usual basic ones) that residual errors are uncorrelated and homoscedastic, with no distributional assumption imposed on the random effects. A major asset of this new approach is that it yields nonnegative variance estimates and covariance matrices estimates which are symmetric and, at least, positive semi-definite. Furthermore, it is shown that when the LMM is, indeed, Gaussian, this new methodology differs from ML just through a slight variation in the denominator of the residual variance estimate. The new methodology actually generalizes to LMMs a well known nonparametric fitting procedure for standard Linear Models. Finally, the methodology is also extended to ANOVA LMMs, generalizing an old method by Henderson for ML estimation in such models under normality.展开更多
A linear mixed model is used to determine the explaining infant mortality rate data of United Nations countries. The HDI （human development index） has a significant negative linear relationship with infant mortality...A linear mixed model is used to determine the explaining infant mortality rate data of United Nations countries. The HDI （human development index） has a significant negative linear relationship with infant mortality rate. United Nations data shows that the infant mortality rate has a descending trend over the period 1990-2010. This study aims to assess the value of the HDI as a predictor of infant mortality rate. Findings in the paper suggest that significant percentage reductions in infant mortality might be possible for countries for controlling the HDI.展开更多
The selection of superior genotypes based on the simultaneous response to several characteristics of agronomic importance is a key strategy to overcome the scarcity of available varieties of papaya. This study aimed t...The selection of superior genotypes based on the simultaneous response to several characteristics of agronomic importance is a key strategy to overcome the scarcity of available varieties of papaya. This study aimed to apply the combined selection by using distinct selection indexes based on both the genetic values obtained by the REML/BLUP methodology and the real measured values to select agronomically superior genotypes of papaya within backcross progenies. The combined selection was carried out based on genetic and phenotypic values, original and standardized, multiplied by the agronomic weights. The results of the analysis of genetic parameters indicate that the evaluated progenies have expressive genetic variability for the considered traits, and that there are real possibilities of genetic progress with the selection. Among the analyzed indexes, the one based on standardized genetic value presented greater consistency in the ranking of genetic material, demonstrating the advantage of data standardization. Five progenies belonging to the BC1 generation, and five to the BC3 generation were selected using this index. A total of 27 plants ag-ronomically superior were selected within the top five progenies and recommended for generation advance, 23 being selected by combined selection and 4 using the direct selection for the four mainly characters in papaya breeding program: production, pulp and fruit firmness and soluble solids. Beyond the selection of superior genotypes for the development of future inbred lines, this study also allowed defining the best strategy to apply the combined selection in papaya using pre-dicted breeding values obtained by BLUP. This strategy may allow higher accuracy in the selection process, thus increasing the chances of success of the breeding programs.展开更多
This paper presents an application of the simulated annealing algorithm to solve level schedules in mixed model assembly line. Solving production sequences with both number of setups and material usage rates to the mi...This paper presents an application of the simulated annealing algorithm to solve level schedules in mixed model assembly line. Solving production sequences with both number of setups and material usage rates to the minimum rate will optimize the level schedule. Miltenburg algorithm (1989) is first used to get seed sequence to optimize further. For this the utility time of the line and setup time requirement on each station is considered. This seed sequence is optimized by simulated annealing. This investigation helps to understand the importance of utility in the assembly line. Up to 15 product sequences are taken and constructed by using randomizing method and find the objective function value for this. For a sequence optimization, a meta-heuristic seems much more promising to guide the search into feasible regions of the solution space. Simulated annealing is a stochastic local search meta-heuristic, which bases the acceptance of a modified neighboring solution on a probabilistic scheme inspired by thermal processes for obtaining low-energy states in heat baths. Experimental results show that the simulated annealing approach is favorable and competitive compared to Miltenburg’s constructive algorithm for the problems set considered. It is proposed to found 16,985 solutions, the time taken for computation is 23.47 to 130.35, and the simulated annealing improves 49.33% than Miltenberg.展开更多
Recurrent event data with a terminal event are commonly encountered in longitudinal followup studies.In this paper,we investigate regression analysis of the weighted composite endpoint of recurrent and terminal events...Recurrent event data with a terminal event are commonly encountered in longitudinal followup studies.In this paper,we investigate regression analysis of the weighted composite endpoint of recurrent and terminal events with a semiparametric mixed model.Particularly,the weighted composite endpoint is constructed by the severity of all events while leaving the dependence structure among the recurrent and terminal events unspecified.The semiparametric mixed model is flexible since it allows the covariate effects on the rate function of the weighted composite endpoint to be proportional or convergent.For inference on the model parameters,the estimating equation approach and the inverse probability weighting technique are developed.The asymptotic properties of the resulting estimators are established and the finite sample performance of the proposed procedure is evaluated through Monte Carlo simulation studies.We apply the proposed method to a real data set on a medical cost study of chronic heart failure patients for illustration.展开更多
DNA microarray technology is an extremely effective technique for studying gene expression patterns in cells, and the main challenge currently faced by this technology is how to analyze the large amount of gene expres...DNA microarray technology is an extremely effective technique for studying gene expression patterns in cells, and the main challenge currently faced by this technology is how to analyze the large amount of gene expression data generated. To address this, this paper employs a mixed-effects model to analyze gene expression data. In terms of data selection, 1176 genes from the white mouse gene expression dataset under two experimental conditions were chosen, setting up two conditions: pneumococcal infection and no infection, and constructing a mixed-effects model. After preprocessing the gene chip information, the data were imported into the model, preliminary results were calculated, and permutation tests were performed to biologically validate the preliminary results using GSEA. The final dataset consists of 20 groups of gene expression data from pneumococcal infection, which categorizes functionally related genes based on the similarity of their expression profiles, facilitating the study of genes with unknown functions.展开更多
Satellite Component Layout Optimization(SCLO) is crucial in satellite system design.This paper proposes a novel Satellite Three-Dimensional Component Assignment and Layout Optimization(3D-SCALO) problem tailored to en...Satellite Component Layout Optimization(SCLO) is crucial in satellite system design.This paper proposes a novel Satellite Three-Dimensional Component Assignment and Layout Optimization(3D-SCALO) problem tailored to engineering requirements, aiming to optimize satellite heat dissipation while considering constraints on static stability, 3D geometric relationships between components, and special component positions. The 3D-SCALO problem is a challenging bilevel combinatorial optimization task, involving the optimization of discrete component assignment variables in the outer layer and continuous component position variables in the inner layer,with both influencing each other. To address this issue, first, a Mixed Integer Programming(MIP) model is proposed, which reformulates the original bilevel problem into a single-level optimization problem, enabling the exploration of a more comprehensive optimization space while avoiding iterative nested optimization. Then, to model the 3D geometric relationships between components within the MIP framework, a linearized 3D Phi-function method is proposed, which handles non-overlapping and safety distance constraints between cuboid components in an explicit and effective way. Subsequently, the Finite-Rectangle Method(FRM) is proposed to manage 3D geometric constraints for complex-shaped components by approximating them with a finite set of cuboids, extending the applicability of the geometric modeling approach. Finally, the feasibility and effectiveness of the proposed MIP model are demonstrated through two numerical examples"and a real-world engineering case, which confirms its suitability for complex-shaped components and real engineering applications.展开更多
Industrial ebullated-bed is an important device for promoting the cleaning and upgrading of oil products. The lumped kinetic model is a powerful tool for predicting the product yield of the ebullated-bed residue hydro...Industrial ebullated-bed is an important device for promoting the cleaning and upgrading of oil products. The lumped kinetic model is a powerful tool for predicting the product yield of the ebullated-bed residue hydrogenation (EBRH) unit, However, during the long-term operation of the device, there are phenomena such as low frequency of material property analysis leading to limited operating data and diverse operating modes at the same time scale, which poses a huge challenge to building an accurate product yield prediction model. To address these challenges, a data augmentation-based eleven lumped reaction kinetics mechanism model was constructed. This model combines generative adversarial networks, outlier elimination, and L2 norm data filtering to expand the dataset and utilizes kernel principal component analysis-fuzzy C-means for operating condition partitioning. Based on the hydrogenation reaction mechanism, a single and sub operating condition eleven lumped reaction kinetics model of an ebullated-bed residue hydrogenation unit, comprising 55 reaction paths and 110 parameters, was constructed before and after data augmentation. Compared to the single model before data enhancement, the average absolute error of the sub-models under data enhancement division was reduced by 23%. Thus, these findings can help guide the operation and optimization of the production process.展开更多
The paper concerns a flood/drought prediction model involving the continuation of time series of a predictand and the physical factors influencing the change of predictand.Attempt is made to construct the model by the...The paper concerns a flood/drought prediction model involving the continuation of time series of a predictand and the physical factors influencing the change of predictand.Attempt is made to construct the model by the neural network scheme for the nonlinear mapping relation based on multi-input and single output.The model is found of steadily higher predictive accuracy by testing the output from one and multiple stepwise predictions against observations and comparing the results to those from a traditional statistical model.展开更多
Although genome-wide association studies are widely used to mine genes for quantitative traits,the effects to be estimated are confounded,and the methodologies for detecting interactions are imperfect.To address these...Although genome-wide association studies are widely used to mine genes for quantitative traits,the effects to be estimated are confounded,and the methodologies for detecting interactions are imperfect.To address these issues,the mixed model proposed here first estimates the genotypic effects for AA,Aa,and aa,and the genotypic polygenic background replaces additive and dominance polygenic backgrounds.Then,the estimated genotypic effects are partitioned into additive and dominance effects using a one-way analysis of variance model.This strategy was further expanded to cover QTN-by-environment interactions(QEIs)and QTN-by-QTN interactions(QQIs)using the same mixed-model framework.Thus,a three-variance-component mixed model was integrated with our multi-locus random-SNP-effect mixed linear model(mrMLM)method to establish a new methodological framework,3VmrMLM,that detects all types of loci and estimates their effects.In Monte Carlo studies,3VmrMLM correctly detected all types of loci and almost unbiasedly estimated their effects,with high powers and accuracies and a low false positive rate.In re-analyses of 10 traits in 1439 rice hybrids,detection of 269 known genes,45 known gene-by-environment interactions,and 20 known gene-by-gene interactions strongly validated 3VmrMLM.Further analyses of known genes showed more small(67.49%),minor-allele-frequency(35.52%),and pleiotropic(30.54%)genes,with higher repeatability across datasets(54.36%)and more dominance loci.In addition,a heteroscedasticity mixed model in multiple environments and dimension reduction methods in quite a number of environments were developed to detect QEIs,and variable selection under a polygenic background was proposed for QQI detection.This study provides a new approach for revealing the genetic architecture of quantitative traits.展开更多
For the linear mixed model with skew-normal random effects, this paper gives the density function, moment generating function and independence conditions. The noncentral skew chi-square distribution is defined and its...For the linear mixed model with skew-normal random effects, this paper gives the density function, moment generating function and independence conditions. The noncentral skew chi-square distribution is defined and its density function is shown. The necessary and sufficient conditions under which a quadratic form is distributed as noncentral skew chi-square distribution are obtained. Also, a version of Cochran＇s theorem is given~ which modifies the result of Wang et al. （2009） and is used to set up exact tests for fixed effects and variance components of the proposed model. For illustration, our main results are applied to a real data problem.展开更多
Background:We used mixed models with random components to develop height-diameter(h-d) functions for mixed,uneven-aged stands in northwestern Durango(Mexico),considering the breast height diameter(d) and stand variabl...Background:We used mixed models with random components to develop height-diameter(h-d) functions for mixed,uneven-aged stands in northwestern Durango(Mexico),considering the breast height diameter(d) and stand variables as predictors.Methods:The data were obtained from 44 permanent plots used to monitor stand growth under forest management in the study area.Results:The generalized Bertalanffy-Richards model performed better than the other generalized models in predicting the total height of the species under study.For the genera Pinus and Quercus,the models were successfully calibrated by measuring the height of a subsample of three randomly selected trees close to the mean d,whereas for species of the genera Cupressus,Arbutus and Alnus,three trees were also selected,but they are specifically the maximum,minimum and mean d trees.Conclusions:The presented equations represent a new tool for the evaluation and management of natural forest in the region.展开更多
Climate change is one of the critical determinants affecting life cycles and transmission of most infectious agents,including malaria,cholera,dengue fever,hand,foot,and mouth disease(HFMD),and the recent Corona-virus ...Climate change is one of the critical determinants affecting life cycles and transmission of most infectious agents,including malaria,cholera,dengue fever,hand,foot,and mouth disease(HFMD),and the recent Corona-virus pandemic.HFMD has been associated with a growing number of outbreaks resulting in fatal complications since the late 1990s.The outbreaks may result from a combination of rapid population growth,climate change,socioeconomic changes,and other lifestyle changes.However,the modeling of climate variability and HFMD remains unclear,particularly in statistical theory development.The statistical relationship between HFMD and climate factors has been widely studied using generalized linear and additive modeling.When dealing with time-series data with clustered variables such as HFMD with clustered states,the independence principle of both modeling approaches may be violated.Thus,a Generalized Additive Mixed Model(GAMM)is used to investigate the relationship between HFMD and climate factors in Malaysia.The model is improved by using a first-order autoregressive term and treating all Malaysian states as a random effect.This method is preferred as it allows states to be modeled as random effects and accounts for time series data autocorrelation.The findings indicate that climate variables such as rainfall and wind speed affect HFMD cases in Malaysia.The risk of HFMD increased in the subsequent two weeks with rainfall below 60 mm and decreased with rainfall exceeding 60 mm.Besides,a two-week lag in wind speeds between 2 and 5 m/s reduced HFMD's chances.The results also show that HFMD cases rose in Malaysia during the inter-monsoon and southwest monsoon seasons but fell during the northeast monsoon.The study's outcomes can be used by public health officials and the general public to raise awareness,and thus,implement effective preventive measures.展开更多
文摘We focus on the development of model selection criteria in linear mixed models. In particular, we propose the model selection criteria following the Mallows’ Conceptual Predictive Statistic (Cp) [1] [2] in linear mixed models. When correlation exists between the observations in data, the normal Gauss discrepancy in univariate case is not appropriate to measure the distance between the true model and a candidate model. Instead, we define a marginal Gauss discrepancy which takes the correlation into account in the mixed models. The model selection criterion, marginal Cp, called MCp, serves as an asymptotically unbiased estimator of the expected marginal Gauss discrepancy. An improvement of MCp, called IMCp, is then derived and proved to be a more accurate estimator of the expected marginal Gauss discrepancy than MCp. The performance of the proposed criteria is investigated in a simulation study. The simulation results show that in small samples, the proposed criteria outperform the Akaike Information Criteria (AIC) [3] [4] and Bayesian Information Criterion (BIC) [5] in selecting the correct model;in large samples, their performance is competitive. Further, the proposed criteria perform significantly better for highly correlated response data than for weakly correlated data.
基金Project (No. BFGEN.100B) supported by the Meat and LivestockLtd., Australia (MLA)
文摘WOMBAT is a software package for quantitative genetic analyses of continuous traits, fitting a linear, mixed model; estimates of covariance components and the resulting genetic parameters are obtained by restricted maximum likelihood. A wide range of models, comprising numerous traits, multiple fixed and random effects, selected genetic covariance structures, random regression models and reduced rank estimation are accommodated. WOMBAT employs up-to-date numerical and computational methods. Together with the use of efficient compilers, this generates fast executable programs, suitable for large scale analyses. Use of WOMBAT is illustrated for a bivariate analysis. The package consists of the executable program, available for LINUX and WINDOWS environments, manual and a set of worked example, and can be downloaded free of charge from http：//agbu. une.edu.au/-kmeyer/wombat.html
基金supported by The 11th Five-year Defense Pre-research Fund of China (Grant No. 51305010387)
文摘The traditional method of mechanical gear driving simulation includes gear pair method and solid to solid contact method. The former has higher solving efficiency but lower results accuracy; the latter usually obtains higher precision of results while the calculation process is complex, also it is not easy to converge. Currently, most of the researches are focused on the description of geometric models and the definition of boundary conditions. However, none of them can solve the problems fundamentally. To improve the simulation efficiency while ensure the results with high accuracy, a mixed model method which uses gear tooth profiles to take the place of the solid gear to simulate gear movement is presented under these circumstances. In the process of modeling, build the solid models of the mechanism in the SolidWorks firstly; Then collect the point coordinates of outline curves of the gear using SolidWorks API and create fit curves in Adams based on the point coordinates; Next, adjust the position of those fitting curves according to the position of the contact area; Finally, define the loading conditions, boundary conditions and simulation parameters. The method provides gear shape information by tooth profile curves; simulates the mesh process through tooth profile curve to curve contact and offer mass as well as inertia data via solid gear models. This simulation process combines the two models to complete the gear driving analysis. In order to verify the validity of the method presented, both theoretical derivation and numerical simulation on a runaway escapement are conducted. The results show that the computational efficiency of the mixed model method is 1.4 times over the traditional method which contains solid to solid contact. Meanwhile, the simulation results are more closely to theoretical calculations. Consequently, mixed model method has a high application value regarding to the study of the dynamics of gear mechanism.
基金supported by the Funding Project for Academic Human Resources Development in Institutions of Higher Learning Under the Jurisdiction of Beijing Municipality (0506011200702)National Natural Science Foundation of China+2 种基金Tian Yuan Special Foundation (10926059)Foundation of Zhejiang Educational Committee (Y200803920)Scientific Research Foundation of Hangzhou Dianzi University(KYS025608094)
文摘In this article, the problem of estimating the covariance matrix in general linear mixed models is considered. Two new classes of estimators obtained by shrinking the eigenvalues towards the origin and the arithmetic mean, respectively, are proposed. It is shown that these new estimators dominate the unbiased estimator under the squared error loss function. Finally, some simulation results to compare the performance of the proposed estimators with that of the unbiased estimator are reported. The simulation results indicate that these new shrinkage estimators provide a substantial improvement in risk under most situations.
基金Supported by the National Natural Science Foundation of China (No.41004007)
文摘Gravity gradients acquired by the Gravity field and steady-state Ocean Circulation Explorer(GOCE) do not cover the entire earth because of its sun-synchronous orbit leaving data gaps with a radius of about 6.5° in the polar regions.Previous studies showed that the loss of data in the polar regions deteriorates the accuracy of the low order(or near zonal) coefficients of the earth gravity model,which is the so-called polar gap problem in geodesy.In order to find a stable solution for the earth gravity model from the GOCE gravity gradients,three models,i.e.the Gauss-Markov model,light constraint model and the mixed model,are compared and evaluated numerically with the gravity gradient simulated with the EGM2008.The comparison shows that the Best Linear Uniformly Unbiased Estimation(BLUUE) estimator of the mixed model can solve the polar gap problem as effectively as the light constraint model;furthermore,the mixed model is more rigorous in dealing with the supplementary information and leads to a better accuracy in determining the global geoid.
文摘Several computer packages have been developed to accomplish improved programs for animal breeding and genetic selection. This paper described most of the currant software and provided suggestions for improved software. Khon Kaen University, Thailand, will provide free of charge the new software developed at Khon Kaen University by the author of this paper. The contact for requesting the software is listed: monchai@kku.ac.th.
文摘Mixed model analysis procedure was used to analyze the effect of fertilizer application on the Fresh Fruit Bunch (FFB) yield of oil palm. This was with a view to achieve the most appropriate and a robust model for analyzing yield response for fertilizer application in oil palm. In this study, a mixed model analysis procedure was used to analyze yield data obtained from a fertilizer trial conducted between 1997 and 2005. In mixed effect model, replicates and years were used as block. In contrast the fixed effect ANOVA model usually lumped up replicates and years as a random error. In the model replicates were used as block with no block interaction, replicates as block with allowance for block-fertilizer interaction, years as block with allowance for block-fertilizer interaction, and years and replicates as block with allowance for year fertilizer and replicate-fertilizer interaction. Mixed model theory was also used to provide the explicit description of the design matrices in the models. Also, hypotheses relevant to each model were formulated and used to test for specific effects in the models such as, fixed part, random part and interacting parts using appropriate error terms as determined by the derived Expected Mean Squares (EMS). The results revealed that at 5% significant level (p
文摘Today, Linear Mixed Models (LMMs) are fitted, mostly, by assuming that random effects and errors have Gaussian distributions, therefore using Maximum Likelihood (ML) or REML estimation. However, for many data sets, that double assumption is unlikely to hold, particularly for the random effects, a crucial component </span></span><span style="font-family:Verdana;"><span style="font-family:Verdana;"><span style="font-family:Verdana;">in </span></span></span><span style="font-family:Verdana;"><span style="font-family:Verdana;"><span style="font-family:Verdana;">which assessment of magnitude is key in such modeling. Alternative fitting methods not relying on that assumption (as ANOVA ones and Rao</span></span></span><span style="font-family:Verdana;"><span style="font-family:Verdana;"><span style="font-family:Verdana;">’</span></span></span><span style="font-family:Verdana;"><span style="font-family:Verdana;"><span style="font-family:Verdana;">s MINQUE) apply, quite often, only to the very constrained class of variance components models. In this paper, a new computationally feasible estimation methodology is designed, first for the widely used class of 2-level (or longitudinal) LMMs with only assumption (beyond the usual basic ones) that residual errors are uncorrelated and homoscedastic, with no distributional assumption imposed on the random effects. A major asset of this new approach is that it yields nonnegative variance estimates and covariance matrices estimates which are symmetric and, at least, positive semi-definite. Furthermore, it is shown that when the LMM is, indeed, Gaussian, this new methodology differs from ML just through a slight variation in the denominator of the residual variance estimate. The new methodology actually generalizes to LMMs a well known nonparametric fitting procedure for standard Linear Models. Finally, the methodology is also extended to ANOVA LMMs, generalizing an old method by Henderson for ML estimation in such models under normality.
文摘A linear mixed model is used to determine the explaining infant mortality rate data of United Nations countries. The HDI （human development index） has a significant negative linear relationship with infant mortality rate. United Nations data shows that the infant mortality rate has a descending trend over the period 1990-2010. This study aims to assess the value of the HDI as a predictor of infant mortality rate. Findings in the paper suggest that significant percentage reductions in infant mortality might be possible for countries for controlling the HDI.
基金the Fundacao de Amparo a Pesquisa do Estado do Rio de Janeiro(FAPERJ)for providing the Master’s scholarshipthe Empresa Caliman Agrícola S/A(Caliman)for the financial and logistical support.
文摘The selection of superior genotypes based on the simultaneous response to several characteristics of agronomic importance is a key strategy to overcome the scarcity of available varieties of papaya. This study aimed to apply the combined selection by using distinct selection indexes based on both the genetic values obtained by the REML/BLUP methodology and the real measured values to select agronomically superior genotypes of papaya within backcross progenies. The combined selection was carried out based on genetic and phenotypic values, original and standardized, multiplied by the agronomic weights. The results of the analysis of genetic parameters indicate that the evaluated progenies have expressive genetic variability for the considered traits, and that there are real possibilities of genetic progress with the selection. Among the analyzed indexes, the one based on standardized genetic value presented greater consistency in the ranking of genetic material, demonstrating the advantage of data standardization. Five progenies belonging to the BC1 generation, and five to the BC3 generation were selected using this index. A total of 27 plants ag-ronomically superior were selected within the top five progenies and recommended for generation advance, 23 being selected by combined selection and 4 using the direct selection for the four mainly characters in papaya breeding program: production, pulp and fruit firmness and soluble solids. Beyond the selection of superior genotypes for the development of future inbred lines, this study also allowed defining the best strategy to apply the combined selection in papaya using pre-dicted breeding values obtained by BLUP. This strategy may allow higher accuracy in the selection process, thus increasing the chances of success of the breeding programs.
文摘This paper presents an application of the simulated annealing algorithm to solve level schedules in mixed model assembly line. Solving production sequences with both number of setups and material usage rates to the minimum rate will optimize the level schedule. Miltenburg algorithm (1989) is first used to get seed sequence to optimize further. For this the utility time of the line and setup time requirement on each station is considered. This seed sequence is optimized by simulated annealing. This investigation helps to understand the importance of utility in the assembly line. Up to 15 product sequences are taken and constructed by using randomizing method and find the objective function value for this. For a sequence optimization, a meta-heuristic seems much more promising to guide the search into feasible regions of the solution space. Simulated annealing is a stochastic local search meta-heuristic, which bases the acceptance of a modified neighboring solution on a probabilistic scheme inspired by thermal processes for obtaining low-energy states in heat baths. Experimental results show that the simulated annealing approach is favorable and competitive compared to Miltenburg’s constructive algorithm for the problems set considered. It is proposed to found 16,985 solutions, the time taken for computation is 23.47 to 130.35, and the simulated annealing improves 49.33% than Miltenberg.
基金supported by the Science and Technology Program of Guangzhou of China(No.202102010512)the National Nature Science Foundation of China(No.11901128)the Nature Science Foundation of Guangdong Province of China(No.2021A1515010044)。
文摘Recurrent event data with a terminal event are commonly encountered in longitudinal followup studies.In this paper,we investigate regression analysis of the weighted composite endpoint of recurrent and terminal events with a semiparametric mixed model.Particularly,the weighted composite endpoint is constructed by the severity of all events while leaving the dependence structure among the recurrent and terminal events unspecified.The semiparametric mixed model is flexible since it allows the covariate effects on the rate function of the weighted composite endpoint to be proportional or convergent.For inference on the model parameters,the estimating equation approach and the inverse probability weighting technique are developed.The asymptotic properties of the resulting estimators are established and the finite sample performance of the proposed procedure is evaluated through Monte Carlo simulation studies.We apply the proposed method to a real data set on a medical cost study of chronic heart failure patients for illustration.
文摘DNA microarray technology is an extremely effective technique for studying gene expression patterns in cells, and the main challenge currently faced by this technology is how to analyze the large amount of gene expression data generated. To address this, this paper employs a mixed-effects model to analyze gene expression data. In terms of data selection, 1176 genes from the white mouse gene expression dataset under two experimental conditions were chosen, setting up two conditions: pneumococcal infection and no infection, and constructing a mixed-effects model. After preprocessing the gene chip information, the data were imported into the model, preliminary results were calculated, and permutation tests were performed to biologically validate the preliminary results using GSEA. The final dataset consists of 20 groups of gene expression data from pneumococcal infection, which categorizes functionally related genes based on the similarity of their expression profiles, facilitating the study of genes with unknown functions.
基金supported by the National Natural Science Foundation of China(No.92371206)the Postgraduate Scientific Research Innovation Project of Hunan Province,China(No.CX2023063).
文摘Satellite Component Layout Optimization(SCLO) is crucial in satellite system design.This paper proposes a novel Satellite Three-Dimensional Component Assignment and Layout Optimization(3D-SCALO) problem tailored to engineering requirements, aiming to optimize satellite heat dissipation while considering constraints on static stability, 3D geometric relationships between components, and special component positions. The 3D-SCALO problem is a challenging bilevel combinatorial optimization task, involving the optimization of discrete component assignment variables in the outer layer and continuous component position variables in the inner layer,with both influencing each other. To address this issue, first, a Mixed Integer Programming(MIP) model is proposed, which reformulates the original bilevel problem into a single-level optimization problem, enabling the exploration of a more comprehensive optimization space while avoiding iterative nested optimization. Then, to model the 3D geometric relationships between components within the MIP framework, a linearized 3D Phi-function method is proposed, which handles non-overlapping and safety distance constraints between cuboid components in an explicit and effective way. Subsequently, the Finite-Rectangle Method(FRM) is proposed to manage 3D geometric constraints for complex-shaped components by approximating them with a finite set of cuboids, extending the applicability of the geometric modeling approach. Finally, the feasibility and effectiveness of the proposed MIP model are demonstrated through two numerical examples"and a real-world engineering case, which confirms its suitability for complex-shaped components and real engineering applications.
基金supported by National Natural Science Foundation of China(Basic Science Center Program:61988101)National Natural Science Foundation of China(62394345,62373155,62173147)the Major Science and Technology Project of Xinjiang(No.2022A01006-4).
文摘Industrial ebullated-bed is an important device for promoting the cleaning and upgrading of oil products. The lumped kinetic model is a powerful tool for predicting the product yield of the ebullated-bed residue hydrogenation (EBRH) unit, However, during the long-term operation of the device, there are phenomena such as low frequency of material property analysis leading to limited operating data and diverse operating modes at the same time scale, which poses a huge challenge to building an accurate product yield prediction model. To address these challenges, a data augmentation-based eleven lumped reaction kinetics mechanism model was constructed. This model combines generative adversarial networks, outlier elimination, and L2 norm data filtering to expand the dataset and utilizes kernel principal component analysis-fuzzy C-means for operating condition partitioning. Based on the hydrogenation reaction mechanism, a single and sub operating condition eleven lumped reaction kinetics model of an ebullated-bed residue hydrogenation unit, comprising 55 reaction paths and 110 parameters, was constructed before and after data augmentation. Compared to the single model before data enhancement, the average absolute error of the sub-models under data enhancement division was reduced by 23%. Thus, these findings can help guide the operation and optimization of the production process.
基金the Excellent Talent Foundation of the State Education Commission.
文摘The paper concerns a flood/drought prediction model involving the continuation of time series of a predictand and the physical factors influencing the change of predictand.Attempt is made to construct the model by the neural network scheme for the nonlinear mapping relation based on multi-input and single output.The model is found of steadily higher predictive accuracy by testing the output from one and multiple stepwise predictions against observations and comparing the results to those from a traditional statistical model.
基金supported by the National Natural Science Foundation of China(32070557 and 31871242)the Fundamental Research Funds for the Central Universities(2662020ZKPY017)+1 种基金the Huazhong Agricultural University Scientific&Technological Self-Innovation Foundation(2014RC020)the State Key Laboratory of Cotton Biology Open Fund(CB2021B01).
文摘Although genome-wide association studies are widely used to mine genes for quantitative traits,the effects to be estimated are confounded,and the methodologies for detecting interactions are imperfect.To address these issues,the mixed model proposed here first estimates the genotypic effects for AA,Aa,and aa,and the genotypic polygenic background replaces additive and dominance polygenic backgrounds.Then,the estimated genotypic effects are partitioned into additive and dominance effects using a one-way analysis of variance model.This strategy was further expanded to cover QTN-by-environment interactions(QEIs)and QTN-by-QTN interactions(QQIs)using the same mixed-model framework.Thus,a three-variance-component mixed model was integrated with our multi-locus random-SNP-effect mixed linear model(mrMLM)method to establish a new methodological framework,3VmrMLM,that detects all types of loci and estimates their effects.In Monte Carlo studies,3VmrMLM correctly detected all types of loci and almost unbiasedly estimated their effects,with high powers and accuracies and a low false positive rate.In re-analyses of 10 traits in 1439 rice hybrids,detection of 269 known genes,45 known gene-by-environment interactions,and 20 known gene-by-gene interactions strongly validated 3VmrMLM.Further analyses of known genes showed more small(67.49%),minor-allele-frequency(35.52%),and pleiotropic(30.54%)genes,with higher repeatability across datasets(54.36%)and more dominance loci.In addition,a heteroscedasticity mixed model in multiple environments and dimension reduction methods in quite a number of environments were developed to detect QEIs,and variable selection under a polygenic background was proposed for QQI detection.This study provides a new approach for revealing the genetic architecture of quantitative traits.
基金supported by National Natural Science Foundation of China(Grant No.11401148)Ministry of Education of China,Humanities and Social Science Projects(Grant Nos.14YJC910005,10YJC790184)+2 种基金Zhejiang Provincial Natural Science Foundation of China(Grant No.LY14A010030)Zhejiang Provincial Philosophy and Social Science Planning Project of China(Grant No.13NDJC089YB)Houji Scholar Fund of Northwest A and F University,China
文摘For the linear mixed model with skew-normal random effects, this paper gives the density function, moment generating function and independence conditions. The noncentral skew chi-square distribution is defined and its density function is shown. The necessary and sufficient conditions under which a quadratic form is distributed as noncentral skew chi-square distribution are obtained. Also, a version of Cochran＇s theorem is given~ which modifies the result of Wang et al. （2009） and is used to set up exact tests for fixed effects and variance components of the proposed model. For illustration, our main results are applied to a real data problem.
基金financially supported by the"Programa de Mejoramiento del Profesorado"(project:Seguimiento y Evaluacion de Sitios Permanentes de Investigación Forestal y el Impacto Socioeconómico delManejo Forestal en Norte de México)supported by"Programa Banco Santander-USC"(becas para estancias predoctorales destinadas a docentes e investigadores de America Latina)
文摘Background:We used mixed models with random components to develop height-diameter(h-d) functions for mixed,uneven-aged stands in northwestern Durango(Mexico),considering the breast height diameter(d) and stand variables as predictors.Methods:The data were obtained from 44 permanent plots used to monitor stand growth under forest management in the study area.Results:The generalized Bertalanffy-Richards model performed better than the other generalized models in predicting the total height of the species under study.For the genera Pinus and Quercus,the models were successfully calibrated by measuring the height of a subsample of three randomly selected trees close to the mean d,whereas for species of the genera Cupressus,Arbutus and Alnus,three trees were also selected,but they are specifically the maximum,minimum and mean d trees.Conclusions:The presented equations represent a new tool for the evaluation and management of natural forest in the region.
基金This work was supported by the Ministry of Higher Education,Malaysia under the Fundamental Research Grant Scheme FRGS/1/2020/STG06/UTM/02/3(5F311)Research University Grant with vote no:QJ130000.3854.19J58Zamalah UTM Scholarship under Universiti Teknologi Malaysia.
文摘Climate change is one of the critical determinants affecting life cycles and transmission of most infectious agents,including malaria,cholera,dengue fever,hand,foot,and mouth disease(HFMD),and the recent Corona-virus pandemic.HFMD has been associated with a growing number of outbreaks resulting in fatal complications since the late 1990s.The outbreaks may result from a combination of rapid population growth,climate change,socioeconomic changes,and other lifestyle changes.However,the modeling of climate variability and HFMD remains unclear,particularly in statistical theory development.The statistical relationship between HFMD and climate factors has been widely studied using generalized linear and additive modeling.When dealing with time-series data with clustered variables such as HFMD with clustered states,the independence principle of both modeling approaches may be violated.Thus,a Generalized Additive Mixed Model(GAMM)is used to investigate the relationship between HFMD and climate factors in Malaysia.The model is improved by using a first-order autoregressive term and treating all Malaysian states as a random effect.This method is preferred as it allows states to be modeled as random effects and accounts for time series data autocorrelation.The findings indicate that climate variables such as rainfall and wind speed affect HFMD cases in Malaysia.The risk of HFMD increased in the subsequent two weeks with rainfall below 60 mm and decreased with rainfall exceeding 60 mm.Besides,a two-week lag in wind speeds between 2 and 5 m/s reduced HFMD's chances.The results also show that HFMD cases rose in Malaysia during the inter-monsoon and southwest monsoon seasons but fell during the northeast monsoon.The study's outcomes can be used by public health officials and the general public to raise awareness,and thus,implement effective preventive measures.
