Traffic forecasting with high precision aids Intelligent Transport Systems(ITS)in formulating and optimizing traffic management strategies.The algorithms used for tuning the hyperparameters of the deep learning models...Traffic forecasting with high precision aids Intelligent Transport Systems(ITS)in formulating and optimizing traffic management strategies.The algorithms used for tuning the hyperparameters of the deep learning models often have accurate results at the expense of high computational complexity.To address this problem,this paper uses the Tree-structured Parzen Estimator(TPE)to tune the hyperparameters of the Long Short-term Memory(LSTM)deep learning framework.The Tree-structured Parzen Estimator(TPE)uses a probabilistic approach with an adaptive searching mechanism by classifying the objective function values into good and bad samples.This ensures fast convergence in tuning the hyperparameter values in the deep learning model for performing prediction while still maintaining a certain degree of accuracy.It also overcomes the problem of converging to local optima and avoids timeconsuming random search and,therefore,avoids high computational complexity in prediction accuracy.The proposed scheme first performs data smoothing and normalization on the input data,which is then fed to the input of the TPE for tuning the hyperparameters.The traffic data is then input to the LSTM model with tuned parameters to perform the traffic prediction.The three optimizers:Adaptive Moment Estimation(Adam),Root Mean Square Propagation(RMSProp),and Stochastic Gradient Descend with Momentum(SGDM)are also evaluated for accuracy prediction and the best optimizer is then chosen for final traffic prediction in TPE-LSTM model.Simulation results verify the effectiveness of the proposed model in terms of accuracy of prediction over the benchmark schemes.展开更多
BACKGROUND Serum cytokeratin 18 fragment(CK18F)has been developed as a new noninvasive test(NIT)for risk assessment of steatotic liver disease(SLD);however,there are few reports on its relationship with existing NITs ...BACKGROUND Serum cytokeratin 18 fragment(CK18F)has been developed as a new noninvasive test(NIT)for risk assessment of steatotic liver disease(SLD);however,there are few reports on its relationship with existing NITs and association with cardiometabolic risk factors(CMRFs).AIM To clarify the relationship among CK18F,NITs,and CMRF.METHODS We included 125 patients who were assessed for SLD and had CK18F measured in cross-sectional study.The fibrosis-4 index(FIB-4),steatosis-associated fibrosis estimator(SAFE)score,liver stiffness(LS),controlled attenuation parameter,and FibroScan-aspartate aminotransferase(FAST)score were compared with CK18F as existing NITs.RESULTS CK18F was associated with aspartate aminotransferase,alanine aminotransferase,and triglyceride(TG).FAST and SAFE score were associated with high CK18F(>260 U/L),but not FIB-4 or LS.The cut-off values for TG and high-density lipoprotein(HDL)cholesterol used to determine high CK18F using receiver operating characteristics analysis were 126 mg/dL and 56 mg/dL respectively.High TG(>126 mg/dL)and low HDL(<56 mg/dL)were associated with high CK18F.The risk of high CK18F was higher when high TG and low HDL were combined than when each was present alone.CMRF was higher in the high CK18F group,but was not associated with CK18F levels.However,when the TG and HDL criteria for CMRF were replaced by TG>126 mg/mL and HDL<56 mg/dL,modified CMRF(mCMRF)was associated with CK18F levels,with a higher risk of high CK18F than CMRF.CONCLUSION CK18F is a new NIT associated with SAFE score and FAST.High TG,low HDL,and mCMRF are associated with high CK18F.展开更多
Recently,machine learning has become a powerful tool for predicting nuclear charge radius RC,providing novel insights into complex physical phenomena.This study employs a continuous Bayesian probability(CBP)estimator ...Recently,machine learning has become a powerful tool for predicting nuclear charge radius RC,providing novel insights into complex physical phenomena.This study employs a continuous Bayesian probability(CBP)estimator and Bayesian model averaging(BMA)to optimize the predictions of RCfrom sophisticated theoretical models.The CBP estimator treats the residual between the theoretical and experimental values of RCas a continuous variable and derives its posterior probability density function(PDF)from Bayesian theory.The BMA method assigns weights to models based on their predictive performance for benchmark nuclei,thereby accounting for the unique strengths of each model.In global optimization,the CBP estimator improved the predictive accuracy of the three theoretical models by approximately 60%.The extrapolation analyses consistently achieved an improvement rate of approximately 45%,demonstrating the robustness of the CBP estimator.Furthermore,the combination of the CBP and BMA methods reduces the standard deviation to below 0.02 fm,effectively reproducing the pronounced shell effects on RCof the Ca and Sr isotope chains.The studies in this paper propose an efficient method to accurately describe RCof unknown nuclei,with potential applications in research on other nuclear properties.展开更多
In this paper, we define a new class of biased linear estimators of the vector of unknown parameters in the deficient_rank linear model based on the spectral decomposition expression of the best linear minimun bias es...In this paper, we define a new class of biased linear estimators of the vector of unknown parameters in the deficient_rank linear model based on the spectral decomposition expression of the best linear minimun bias estimator. Some important properties are discussed. By appropriate choices of bias parameters, we construct many interested and useful biased linear estimators, which are the extension of ordinary biased linear estimators in the full_rank linear model to the deficient_rank linear model. At last, we give a numerical example in geodetic adjustment.展开更多
In this paper we investigate the estimator for the rth power of the scale parameter in a class of exponential family under symmetric entropy loss L（θ, δ） = v（θ/δ ＋ δ/θ - 2）. An exact form of the minimum ris...In this paper we investigate the estimator for the rth power of the scale parameter in a class of exponential family under symmetric entropy loss L（θ, δ） = v（θ/δ ＋ δ/θ - 2）. An exact form of the minimum risk equivariant estimator under symmetric entropy loss is given, and the minimaxity of the minimum risk equivariant estimator is proved. The results with regard to admissibility and inadmissibility of a class of linear estimators of the form cT（X） ＋ d are given, where T（X） Gamma（v, θ）.展开更多
In estimation theory,the researchers have put their efforts to develop some estimators of population mean which may give more precise results when adopting ordinary least squares(OLS)method or robust regression techni...In estimation theory,the researchers have put their efforts to develop some estimators of population mean which may give more precise results when adopting ordinary least squares(OLS)method or robust regression techniques for estimating regression coefficients.But when the correlation is negative and the outliers are presented,the results can be distorted and the OLS-type estimators may give misleading estimates or highly biased estimates.Hence,this paper mainly focuses on such issues through the use of non-conventional measures of dispersion and a robust estimation method.Precisely,we have proposed generalized estimators by using the ancillary information of non-conventional measures of dispersion(Gini’s mean difference,Downton’s method and probabilityweighted moment)using ordinary least squares and then finally adopting the Huber M-estimation technique on the suggested estimators.The proposed estimators are investigated in the presence of outliers in both situations of negative and positive correlation between study and auxiliary variables.Theoretical comparisons and real data application are provided to show the strength of the proposed generalized estimators.It is found that the proposed generalized Huber-M-type estimators are more efficient than the suggested generalized estimators under the OLS estimation method considered in this study.The new proposed estimators will be useful in the future for data analysis and making decisions.展开更多
The paper presents a two-layer,disturbance-resistant,and fault-tolerant affine formation maneuver control scheme that accomplishes the surrounding of a dynamic target with multiple underactuated Quadrotor Unmanned Aer...The paper presents a two-layer,disturbance-resistant,and fault-tolerant affine formation maneuver control scheme that accomplishes the surrounding of a dynamic target with multiple underactuated Quadrotor Unmanned Aerial Vehicles(QUAVs).This scheme mainly consists of predefinedtime estimators and fixed-time tracking controllers,with a hybrid Laplacian matrix describing the communication among these QUAVs.At the first layer,we devise predefined time estimators for leading and following QUAVs,enabling accurate estimation of desired information.In the second layer,we initially devise a fixed-time hybrid observer to estimate unknown disturbances and actuator faults.Fixedtime translational tracking controllers are then proposed,and the intermediary control input from these controllers is used to extract the desired attitude and angular velocities for the fixed-time rotational tracking controllers.We employ an exact tracking differentiator to handle variables that are challenging to differentiate directly.The paper includes a demonstration of the control system stability through mathematical proof,as well as the presentation of simulation results and comparative simulations.展开更多
Necessary and sufficient conditions for equalities between a 2 y′(I-P Xx)y and minimum norm quadratic unbiased estimator of variance under the general linear model, where a 2 is a known positive number, are...Necessary and sufficient conditions for equalities between a 2 y′(I-P Xx)y and minimum norm quadratic unbiased estimator of variance under the general linear model, where a 2 is a known positive number, are derived. Further, when the Gauss? Markov estimators and the ordinary least squares estimator are identical, a relative simply equivalent condition is obtained. At last, this condition is applied to an interesting example.展开更多
In this paper, we introduce a generalized Liu estimator and jackknifed Liu estimator in a linear regression model with correlated or heteroscedastic errors. Therefore, we extend the Liu estimator. Under the mean squar...In this paper, we introduce a generalized Liu estimator and jackknifed Liu estimator in a linear regression model with correlated or heteroscedastic errors. Therefore, we extend the Liu estimator. Under the mean square error（MSE）, the jackknifed estimator is superior to the Liu estimator and the jackknifed ridge estimator. We also give a method to select the biasing parameter for d. Furthermore, a numerical example is given to illustvate these theoretical results.展开更多
In this paper, we present two relative efficiency of the weighted mixed estimator in respect of least squares estimator. We also derive the lower and upper bounds of those relative efficiencies.
A kernel density estimator is proposed when tile data are subject to censorship in multivariate case. The asymptotic normality, strong convergence and asymptotic optimal bandwidth which minimize the mean square error ...A kernel density estimator is proposed when tile data are subject to censorship in multivariate case. The asymptotic normality, strong convergence and asymptotic optimal bandwidth which minimize the mean square error of the estimator are studied.展开更多
In this paper, we propose a stochastic restricted s-K estimator in the linear model with additional stochastic linear restrictions by combining the ordinary mixed estimator （OME） with the s-K estimator. It is shown ...In this paper, we propose a stochastic restricted s-K estimator in the linear model with additional stochastic linear restrictions by combining the ordinary mixed estimator （OME） with the s-K estimator. It is shown that the proposed estimator is superior to the OME and the s-K estimator under the mean squared error matrix criterion under some conditions. Finally, a numerical example and a Monte Carlo simulation study are given to verify the theoretical results.展开更多
The study proposes, along the line of [1], six separate-type estimators for estimating the population ratio of two variables in post-stratified sampling, using variable transformation. Properties of the proposed estim...The study proposes, along the line of [1], six separate-type estimators for estimating the population ratio of two variables in post-stratified sampling, using variable transformation. Properties of the proposed estimators were obtained up to first order approximations, both for achieved sample configurations (conditional argument) and over repeated samples of fixed size n (unconditional argument). Efficiency conditions, under which the proposed separate-type estimators would perform better than the associated customary separate-type estimators in terms of having smaller mean squared errors, were obtained. Furthermore, conditions under which some of the proposed separate-type estimators would perform better than other proposed separate-type estimators were also obtained. The optimum estimators among the proposed separate-type estimators were obtained and an empirical illustration confirmed the theoretical results.展开更多
Our purpose is twofold: to present a prototypical example of the conditioning technique to obtain the best estimator of a parameter and to show that th</span><span style="font-family:Verdana;">is...Our purpose is twofold: to present a prototypical example of the conditioning technique to obtain the best estimator of a parameter and to show that th</span><span style="font-family:Verdana;">is technique resides in the structure of an inner product space. Th</span><span style="font-family:Verdana;">e technique uses conditioning </span></span><span style="font-family:Verdana;">of</span><span style="font-family:Verdana;"> an unbiased estimator </span><span style="font-family:Verdana;">on</span><span style="font-family:Verdana;"> a sufficient statistic. This procedure is founded upon the conditional variance formula, which leads to an inner product space and a geometric interpretation. The example clearly illustrates the dependence on the sampling methodology. These advantages show the power and centrality of this process.展开更多
The present paper proposes a new robust estimator for Poisson regression models. We used the weighted maximum likelihood estimators which are regarded as Mallows-type estimators. We perform a Monte Carlo simulation st...The present paper proposes a new robust estimator for Poisson regression models. We used the weighted maximum likelihood estimators which are regarded as Mallows-type estimators. We perform a Monte Carlo simulation study to assess the performance of a suggested estimator compared to the maximum likelihood estimator and some robust methods. The result shows that, in general, all robust methods in this paper perform better than the classical maximum likelihood estimators when the model contains outliers. The proposed estimators showed the best performance compared to other robust estimators.展开更多
Machine learning methods, one type of methods used in artificial intelligence, are now widely used to analyze two-dimensional (2D) images in various fields. In these analyses, estimating the boundary between two regio...Machine learning methods, one type of methods used in artificial intelligence, are now widely used to analyze two-dimensional (2D) images in various fields. In these analyses, estimating the boundary between two regions is basic but important. If the model contains stochastic factors such as random observation errors, determining the boundary is not easy. When the probability distributions are mis-specified, ordinal methods such as probit and logit maximum likelihood estimators (MLE) have large biases. The grouping estimator is a semiparametric estimator based on the grouping of data that does not require specific probability distributions. For 2D images, the grouping is simple. Monte Carlo experiments show that the grouping estimator clearly improves the probit MLE in many cases. The grouping estimator essentially makes the resolution density lower, and the present findings imply that methods using low-resolution image analyses might not be the proper ones in high-density image analyses. It is necessary to combine and compare the results of high- and low-resolution image analyses. The grouping estimator may provide theoretical justifications for such analysis.展开更多
In this article, the restricted almost unbiased ridge logistic estimator (RAURLE) is proposed to estimate the parameter in a logistic regression model with exact linear re-strictions when there exists multicollinearit...In this article, the restricted almost unbiased ridge logistic estimator (RAURLE) is proposed to estimate the parameter in a logistic regression model with exact linear re-strictions when there exists multicollinearity among explanatory variables. The performance of the proposed estimator over the maximum likelihood estimator (MLE), ridge logistic estimator (RLE), almost unbiased ridge logistic estimator (AURLE), and restricted maximum likelihood estimator (RMLE) with respect to different ridge parameters is investigated through a simulation study in terms of scalar mean square error.展开更多
Let fn be a non-parametric kernel density estimator based on a kernel function K. and a sequence of independent and identically distributed random variables taking values in R. The goal of this article is to prove mod...Let fn be a non-parametric kernel density estimator based on a kernel function K. and a sequence of independent and identically distributed random variables taking values in R. The goal of this article is to prove moderate deviations and large deviations for the statistic sup ｜fn（x） - fn（-x） ｜.展开更多
This paper deals with the problems of consistency and strong consistency of the maximum likelihood estimators of the mean and variance of the drift fractional Brownian motions observed at discrete time instants. Both ...This paper deals with the problems of consistency and strong consistency of the maximum likelihood estimators of the mean and variance of the drift fractional Brownian motions observed at discrete time instants. Both the central limit theorem and the Berry-Ess′een bounds for these estimators are obtained by using the Stein’s method via Malliavin calculus.展开更多
In order to compensate for the disturbance of wide variation in rotor demanded torque on power turbine speed and realize the fast response control of turboshaft engine during variable rotor speed,a cascade PID control...In order to compensate for the disturbance of wide variation in rotor demanded torque on power turbine speed and realize the fast response control of turboshaft engine during variable rotor speed,a cascade PID control method based on the acceleration estimator of gas turbine speed(Ngdot)and rotor predicted torque feedforward is proposed.Firstly,a two-speed Dual Clutch Transmission(DCT)model is applied in the integrated rotor/turboshaft engine system to achieve variable rotor speed.Then,an online estimation method of Ngdot based on the Linear Quadratic Gaussian with Loop Transfer Recovery(LQG/LTR)is proposed for power turbine speed cascade control.Finally,according to the cascade PID controller based on Ngdot estimator,a rotor demanded torque predicted method based on the Min-batch Gradient Descent-Neural Network(MGD-NN)is put forward to compromise the influence of rotor torque interference.The simulation results show that compared with cascade PID controller based on Ngdot estimator and the one combined with collective pitch feedforward control,the novel control method proposed can reduce the overshoot of power turbine speed by more than 20%,which possesses faster response,superior dynamic effect and satisfactory robustness performance.The control method proposed can realize the fast response control of turboshaft engine with variable rotor speed better.展开更多
文摘Traffic forecasting with high precision aids Intelligent Transport Systems(ITS)in formulating and optimizing traffic management strategies.The algorithms used for tuning the hyperparameters of the deep learning models often have accurate results at the expense of high computational complexity.To address this problem,this paper uses the Tree-structured Parzen Estimator(TPE)to tune the hyperparameters of the Long Short-term Memory(LSTM)deep learning framework.The Tree-structured Parzen Estimator(TPE)uses a probabilistic approach with an adaptive searching mechanism by classifying the objective function values into good and bad samples.This ensures fast convergence in tuning the hyperparameter values in the deep learning model for performing prediction while still maintaining a certain degree of accuracy.It also overcomes the problem of converging to local optima and avoids timeconsuming random search and,therefore,avoids high computational complexity in prediction accuracy.The proposed scheme first performs data smoothing and normalization on the input data,which is then fed to the input of the TPE for tuning the hyperparameters.The traffic data is then input to the LSTM model with tuned parameters to perform the traffic prediction.The three optimizers:Adaptive Moment Estimation(Adam),Root Mean Square Propagation(RMSProp),and Stochastic Gradient Descend with Momentum(SGDM)are also evaluated for accuracy prediction and the best optimizer is then chosen for final traffic prediction in TPE-LSTM model.Simulation results verify the effectiveness of the proposed model in terms of accuracy of prediction over the benchmark schemes.
文摘BACKGROUND Serum cytokeratin 18 fragment(CK18F)has been developed as a new noninvasive test(NIT)for risk assessment of steatotic liver disease(SLD);however,there are few reports on its relationship with existing NITs and association with cardiometabolic risk factors(CMRFs).AIM To clarify the relationship among CK18F,NITs,and CMRF.METHODS We included 125 patients who were assessed for SLD and had CK18F measured in cross-sectional study.The fibrosis-4 index(FIB-4),steatosis-associated fibrosis estimator(SAFE)score,liver stiffness(LS),controlled attenuation parameter,and FibroScan-aspartate aminotransferase(FAST)score were compared with CK18F as existing NITs.RESULTS CK18F was associated with aspartate aminotransferase,alanine aminotransferase,and triglyceride(TG).FAST and SAFE score were associated with high CK18F(>260 U/L),but not FIB-4 or LS.The cut-off values for TG and high-density lipoprotein(HDL)cholesterol used to determine high CK18F using receiver operating characteristics analysis were 126 mg/dL and 56 mg/dL respectively.High TG(>126 mg/dL)and low HDL(<56 mg/dL)were associated with high CK18F.The risk of high CK18F was higher when high TG and low HDL were combined than when each was present alone.CMRF was higher in the high CK18F group,but was not associated with CK18F levels.However,when the TG and HDL criteria for CMRF were replaced by TG>126 mg/mL and HDL<56 mg/dL,modified CMRF(mCMRF)was associated with CK18F levels,with a higher risk of high CK18F than CMRF.CONCLUSION CK18F is a new NIT associated with SAFE score and FAST.High TG,low HDL,and mCMRF are associated with high CK18F.
基金supported by the National Natural Science Foundation of China(Nos.12475135,12035011,and 12475119)the Shandong Provincial Natural Science Foundation,China(No.ZR2020MA096)the Fundamental Research Funds for the Central Universities(No.22CX03017A)。
文摘Recently,machine learning has become a powerful tool for predicting nuclear charge radius RC,providing novel insights into complex physical phenomena.This study employs a continuous Bayesian probability(CBP)estimator and Bayesian model averaging(BMA)to optimize the predictions of RCfrom sophisticated theoretical models.The CBP estimator treats the residual between the theoretical and experimental values of RCas a continuous variable and derives its posterior probability density function(PDF)from Bayesian theory.The BMA method assigns weights to models based on their predictive performance for benchmark nuclei,thereby accounting for the unique strengths of each model.In global optimization,the CBP estimator improved the predictive accuracy of the three theoretical models by approximately 60%.The extrapolation analyses consistently achieved an improvement rate of approximately 45%,demonstrating the robustness of the CBP estimator.Furthermore,the combination of the CBP and BMA methods reduces the standard deviation to below 0.02 fm,effectively reproducing the pronounced shell effects on RCof the Ca and Sr isotope chains.The studies in this paper propose an efficient method to accurately describe RCof unknown nuclei,with potential applications in research on other nuclear properties.
文摘In this paper, we define a new class of biased linear estimators of the vector of unknown parameters in the deficient_rank linear model based on the spectral decomposition expression of the best linear minimun bias estimator. Some important properties are discussed. By appropriate choices of bias parameters, we construct many interested and useful biased linear estimators, which are the extension of ordinary biased linear estimators in the full_rank linear model to the deficient_rank linear model. At last, we give a numerical example in geodetic adjustment.
基金The SRFDPHE(20070183023)the NSF(10571073,J0630104)of China
文摘In this paper we investigate the estimator for the rth power of the scale parameter in a class of exponential family under symmetric entropy loss L（θ, δ） = v（θ/δ ＋ δ/θ - 2）. An exact form of the minimum risk equivariant estimator under symmetric entropy loss is given, and the minimaxity of the minimum risk equivariant estimator is proved. The results with regard to admissibility and inadmissibility of a class of linear estimators of the form cT（X） ＋ d are given, where T（X） Gamma（v, θ）.
基金The authors extend their appreciation to Deanship of Scientific Research at King Khalid University for funding this work through Research Groups Program under grant number R.G.P.2/82/42.I.M.A.who received the grant,www.kku.edu.sa.
文摘In estimation theory,the researchers have put their efforts to develop some estimators of population mean which may give more precise results when adopting ordinary least squares(OLS)method or robust regression techniques for estimating regression coefficients.But when the correlation is negative and the outliers are presented,the results can be distorted and the OLS-type estimators may give misleading estimates or highly biased estimates.Hence,this paper mainly focuses on such issues through the use of non-conventional measures of dispersion and a robust estimation method.Precisely,we have proposed generalized estimators by using the ancillary information of non-conventional measures of dispersion(Gini’s mean difference,Downton’s method and probabilityweighted moment)using ordinary least squares and then finally adopting the Huber M-estimation technique on the suggested estimators.The proposed estimators are investigated in the presence of outliers in both situations of negative and positive correlation between study and auxiliary variables.Theoretical comparisons and real data application are provided to show the strength of the proposed generalized estimators.It is found that the proposed generalized Huber-M-type estimators are more efficient than the suggested generalized estimators under the OLS estimation method considered in this study.The new proposed estimators will be useful in the future for data analysis and making decisions.
基金supported by Natural Science Basic Research Plan in Shaanxi Province of China(No.2023-JC-QN-0733)Guangdong Basic and Applied Basic Research Foundation,China(No.2022A1515110753)+2 种基金China Postdoctoral Science Foundation(No.2022M722583)China Industry-UniversityResearch Innovation Foundation(No.2022IT188)National Key Laboratory of Air-based Information Perception and Fusion and the Aeronautic Science Foundation of China(No.20220001068001)。
文摘The paper presents a two-layer,disturbance-resistant,and fault-tolerant affine formation maneuver control scheme that accomplishes the surrounding of a dynamic target with multiple underactuated Quadrotor Unmanned Aerial Vehicles(QUAVs).This scheme mainly consists of predefinedtime estimators and fixed-time tracking controllers,with a hybrid Laplacian matrix describing the communication among these QUAVs.At the first layer,we devise predefined time estimators for leading and following QUAVs,enabling accurate estimation of desired information.In the second layer,we initially devise a fixed-time hybrid observer to estimate unknown disturbances and actuator faults.Fixedtime translational tracking controllers are then proposed,and the intermediary control input from these controllers is used to extract the desired attitude and angular velocities for the fixed-time rotational tracking controllers.We employ an exact tracking differentiator to handle variables that are challenging to differentiate directly.The paper includes a demonstration of the control system stability through mathematical proof,as well as the presentation of simulation results and comparative simulations.
文摘Necessary and sufficient conditions for equalities between a 2 y′(I-P Xx)y and minimum norm quadratic unbiased estimator of variance under the general linear model, where a 2 is a known positive number, are derived. Further, when the Gauss? Markov estimators and the ordinary least squares estimator are identical, a relative simply equivalent condition is obtained. At last, this condition is applied to an interesting example.
基金Supported by the National Natural Science Foundation of China(11071022)Science and Technology Project of Hubei Provincial Department of Education(Q20122202)
文摘In this paper, we introduce a generalized Liu estimator and jackknifed Liu estimator in a linear regression model with correlated or heteroscedastic errors. Therefore, we extend the Liu estimator. Under the mean square error（MSE）, the jackknifed estimator is superior to the Liu estimator and the jackknifed ridge estimator. We also give a method to select the biasing parameter for d. Furthermore, a numerical example is given to illustvate these theoretical results.
基金Supported by the National Natural Science Foundation of China(Grant Nos.1150107211501254)+1 种基金the Natural Science Foundation Project of CQ CSTC(Grant No.cstc2015jcyj A00001)the Scientific and Technological Research Program of Chongqing Municipal Education Commission(Grant No.KJ1501114)
文摘In this paper, we present two relative efficiency of the weighted mixed estimator in respect of least squares estimator. We also derive the lower and upper bounds of those relative efficiencies.
文摘A kernel density estimator is proposed when tile data are subject to censorship in multivariate case. The asymptotic normality, strong convergence and asymptotic optimal bandwidth which minimize the mean square error of the estimator are studied.
基金Supported by the National Natural Science Foundation of China(Grant No.11201005)the Natural Science Foundation of Anhui Province(Grant No.1308085QA13)+1 种基金the Key Project of Distinguished Young Talents of Universities in Anhui Province(Grant No.2012SQRL028ZD)the Key Project of Natural Science Foundation of Universities in Anhui Province(Grant No.KJ2012A135)
文摘In this paper, we propose a stochastic restricted s-K estimator in the linear model with additional stochastic linear restrictions by combining the ordinary mixed estimator （OME） with the s-K estimator. It is shown that the proposed estimator is superior to the OME and the s-K estimator under the mean squared error matrix criterion under some conditions. Finally, a numerical example and a Monte Carlo simulation study are given to verify the theoretical results.
文摘The study proposes, along the line of [1], six separate-type estimators for estimating the population ratio of two variables in post-stratified sampling, using variable transformation. Properties of the proposed estimators were obtained up to first order approximations, both for achieved sample configurations (conditional argument) and over repeated samples of fixed size n (unconditional argument). Efficiency conditions, under which the proposed separate-type estimators would perform better than the associated customary separate-type estimators in terms of having smaller mean squared errors, were obtained. Furthermore, conditions under which some of the proposed separate-type estimators would perform better than other proposed separate-type estimators were also obtained. The optimum estimators among the proposed separate-type estimators were obtained and an empirical illustration confirmed the theoretical results.
文摘Our purpose is twofold: to present a prototypical example of the conditioning technique to obtain the best estimator of a parameter and to show that th</span><span style="font-family:Verdana;">is technique resides in the structure of an inner product space. Th</span><span style="font-family:Verdana;">e technique uses conditioning </span></span><span style="font-family:Verdana;">of</span><span style="font-family:Verdana;"> an unbiased estimator </span><span style="font-family:Verdana;">on</span><span style="font-family:Verdana;"> a sufficient statistic. This procedure is founded upon the conditional variance formula, which leads to an inner product space and a geometric interpretation. The example clearly illustrates the dependence on the sampling methodology. These advantages show the power and centrality of this process.
文摘The present paper proposes a new robust estimator for Poisson regression models. We used the weighted maximum likelihood estimators which are regarded as Mallows-type estimators. We perform a Monte Carlo simulation study to assess the performance of a suggested estimator compared to the maximum likelihood estimator and some robust methods. The result shows that, in general, all robust methods in this paper perform better than the classical maximum likelihood estimators when the model contains outliers. The proposed estimators showed the best performance compared to other robust estimators.
文摘Machine learning methods, one type of methods used in artificial intelligence, are now widely used to analyze two-dimensional (2D) images in various fields. In these analyses, estimating the boundary between two regions is basic but important. If the model contains stochastic factors such as random observation errors, determining the boundary is not easy. When the probability distributions are mis-specified, ordinal methods such as probit and logit maximum likelihood estimators (MLE) have large biases. The grouping estimator is a semiparametric estimator based on the grouping of data that does not require specific probability distributions. For 2D images, the grouping is simple. Monte Carlo experiments show that the grouping estimator clearly improves the probit MLE in many cases. The grouping estimator essentially makes the resolution density lower, and the present findings imply that methods using low-resolution image analyses might not be the proper ones in high-density image analyses. It is necessary to combine and compare the results of high- and low-resolution image analyses. The grouping estimator may provide theoretical justifications for such analysis.
文摘In this article, the restricted almost unbiased ridge logistic estimator (RAURLE) is proposed to estimate the parameter in a logistic regression model with exact linear re-strictions when there exists multicollinearity among explanatory variables. The performance of the proposed estimator over the maximum likelihood estimator (MLE), ridge logistic estimator (RLE), almost unbiased ridge logistic estimator (AURLE), and restricted maximum likelihood estimator (RMLE) with respect to different ridge parameters is investigated through a simulation study in terms of scalar mean square error.
基金Research supported by the National Natural Science Foundation of China (10271091)
文摘Let fn be a non-parametric kernel density estimator based on a kernel function K. and a sequence of independent and identically distributed random variables taking values in R. The goal of this article is to prove moderate deviations and large deviations for the statistic sup ｜fn（x） - fn（-x） ｜.
基金supported by the National Science Foundations (DMS0504783 DMS0604207)National Science Fund for Distinguished Young Scholars of China (70825005)
文摘This paper deals with the problems of consistency and strong consistency of the maximum likelihood estimators of the mean and variance of the drift fractional Brownian motions observed at discrete time instants. Both the central limit theorem and the Berry-Ess′een bounds for these estimators are obtained by using the Stein’s method via Malliavin calculus.
基金co-supported by the National Natural Science Foundation of China,China(Nos.51576096 and 51906102)Qing Lan and 333 Project,the Fundamental Research Funds for the Central Universities,China(No.NT2019004)+3 种基金National Science and Technology Major Project China(No.2017-V-0004-0054)Research on the Basic Problem of Intelligent Aero-engine,China(No.2017-JCJQ-ZD-04721)China Postdoctoral Science Foundation Funded Project,China(No.2019M661835)Aeronautics Power Foundation,China(No.6141B09050385)。
文摘In order to compensate for the disturbance of wide variation in rotor demanded torque on power turbine speed and realize the fast response control of turboshaft engine during variable rotor speed,a cascade PID control method based on the acceleration estimator of gas turbine speed(Ngdot)and rotor predicted torque feedforward is proposed.Firstly,a two-speed Dual Clutch Transmission(DCT)model is applied in the integrated rotor/turboshaft engine system to achieve variable rotor speed.Then,an online estimation method of Ngdot based on the Linear Quadratic Gaussian with Loop Transfer Recovery(LQG/LTR)is proposed for power turbine speed cascade control.Finally,according to the cascade PID controller based on Ngdot estimator,a rotor demanded torque predicted method based on the Min-batch Gradient Descent-Neural Network(MGD-NN)is put forward to compromise the influence of rotor torque interference.The simulation results show that compared with cascade PID controller based on Ngdot estimator and the one combined with collective pitch feedforward control,the novel control method proposed can reduce the overshoot of power turbine speed by more than 20%,which possesses faster response,superior dynamic effect and satisfactory robustness performance.The control method proposed can realize the fast response control of turboshaft engine with variable rotor speed better.
