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
General linear model (GLM) is the most popular method for functional magnetic resource imaging (fMRI) data analysis . However, its theory is imperfect. The key of this model is how to constitute the design-matrix to m...General linear model (GLM) is the most popular method for functional magnetic resource imaging (fMRI) data analysis . However, its theory is imperfect. The key of this model is how to constitute the design-matrix to model the interesting effects better and separate noises better. For the purpose of detecting brain function activation , according to the principle of GLM,a new convolution model is presented by a new dynamic function convolving with design-matrix,which combining with t-test can be used to detect brain active signal. The fMRI imaging result of visual stimulus experiment indicates that brain activities mainly concentrate among v1and v2 areas of visual cortex, and also verified the validity of this technique.展开更多
This paper provides further contributions to the theory of linear sufficiency in the general Gauss-Markov model E(y)=Xβ,Var (y)=V.The notion of linear sufficiency introduced by Baksalary and Kala(1981) and Drygas(198...This paper provides further contributions to the theory of linear sufficiency in the general Gauss-Markov model E(y)=Xβ,Var (y)=V.The notion of linear sufficiency introduced by Baksalary and Kala(1981) and Drygas(1983) is extended for any specific estimable function c′β.Some general results with respect to the extended concept are obtained.An essential result concerning the former notion is a direct consequence of this paper.展开更多
In this paper, we give the representation of the best linear unbiased predictor（BLUP）of the new observations under Mrf. Through the representation, we give necessary and sufficient conditions that the estimators, OL...In this paper, we give the representation of the best linear unbiased predictor（BLUP）of the new observations under Mrf. Through the representation, we give necessary and sufficient conditions that the estimators, OLSEs（ordinary least squares estimators） and BLUEs（best linear unbiased estimators）, under Mf and Mrf, and the predictor, BLUP, under Mf continue to be the BLUP under Mrf, respectively.展开更多
We study the quasi likelihood equation in Generalized Linear Models(GLM)with adaptive design∑(i=1)^n xi(yi-h(x'iβ))=0,where yi is a q=vector,and xi is a p×q random matrix.Under some assumptions,it is shown ...We study the quasi likelihood equation in Generalized Linear Models(GLM)with adaptive design∑(i=1)^n xi(yi-h(x'iβ))=0,where yi is a q=vector,and xi is a p×q random matrix.Under some assumptions,it is shown that the Quasi-Likelihood equation for the GLM has a solution which is asymptotic normal.展开更多
The study of the development cost of general aviation aircraft is limited by small samples with many cost-driven factors. This paper investigates a parametric modeling method for prediction of the development cost of ...The study of the development cost of general aviation aircraft is limited by small samples with many cost-driven factors. This paper investigates a parametric modeling method for prediction of the development cost of general aviation aircraft. The proposed technique depends on some principal components, acquired by utilizing P value analysis and gray correlation analysis. According to these principal components, the corresponding linear regression and BP neural network models are established respectively. The feasibility and accuracy of the P value analysis are verified by comparing results of model fitting and prediction. A sensitivity analysis related to model precision and suitability is discussed in detail. Results obtained in this study show that the proposed method not only has a certain degree of versatility, but also provides a preliminary prediction of the development cost of general aviation aircraft.展开更多
In this paper, we propose the test statistic to check whether the nonparametric function in partially linear models is linear or not. We estimate the nonparametric function in alternative by using the local linear met...In this paper, we propose the test statistic to check whether the nonparametric function in partially linear models is linear or not. We estimate the nonparametric function in alternative by using the local linear method, and then estimate the parameters by the two stage method. The test statistic under the null hypothesis is calculated, and it is shown to be asymptotically normal.展开更多
We propose the test statistic to check whether the nonpararnetric functions in two partially linear models are equality or not in this paper. We estimate the nonparametric function both in null hypothesis and the alte...We propose the test statistic to check whether the nonpararnetric functions in two partially linear models are equality or not in this paper. We estimate the nonparametric function both in null hypothesis and the alternative by the local linear method, where we ignore the parametric components, and then estimate the parameters by the two stage method. The test statistic is derived, and it is shown to be asymptotically normal under the null hypothesis.展开更多
针对长江中游城市群洪涝灾害应急转移人口预测中存在的精度瓶颈问题,提出了广义线性模型(generalized linear model,GLM)-空间滞后模型(spatial autoregressive model,SAR)联合预测框架,通过log-link函数与负二项分布解决灾害数据的右...针对长江中游城市群洪涝灾害应急转移人口预测中存在的精度瓶颈问题,提出了广义线性模型(generalized linear model,GLM)-空间滞后模型(spatial autoregressive model,SAR)联合预测框架,通过log-link函数与负二项分布解决灾害数据的右偏分布与过离散问题,构建了以水位超警幅度为核心解释变量的单维度预测模型,并引入空间滞后模型(SAR)量化流域内应急响应的协同效应。基于长江中游三省2019—2023年面板数据的实证结果表明:该模型在跨区域预测中的误差率≤9.2%,较传统线性回归模型降低了8.2个百分点,极端事件的预测误差率压缩至6.7%;揭示了地理异质性对参数的驱动规律,并通过参数迁移模型实现了跨区域适配;通过单维度设计,基层响应效率得到了显著提升,决策链条缩短了4.5 h。研究的创新性体现在“分布修正-空间耦合-动态适配”三重机制,为高波动右偏态灾害数据建模提供了兼具解释力与稳健性的新范式,并为长江中游差异化防灾策略提供了科学支撑。展开更多
This paper considers the problem of delay-dependent robust optimal H<sub>∞</sub> control for a class of uncertain two-dimensional (2-D) discrete state delay systems described by the general model (GM). Th...This paper considers the problem of delay-dependent robust optimal H<sub>∞</sub> control for a class of uncertain two-dimensional (2-D) discrete state delay systems described by the general model (GM). The parameter uncertainties are assumed to be norm-bounded. A linear matrix inequality (LMI)-based sufficient condition for the existence of delay-dependent g-suboptimal state feedback robust H<sub>∞</sub> controllers which guarantees not only the asymptotic stability of the closed-loop system, but also the H<sub>∞</sub> noise attenuation g over all admissible parameter uncertainties is established. Furthermore, a convex optimization problem is formulated to design a delay-dependent state feedback robust optimal H<sub>∞</sub> controller which minimizes the H<sub>∞</sub> noise attenuation g of the closed-loop system. Finally, an illustrative example is provided to demonstrate the effectiveness of the proposed method.展开更多
This paper investigates the problem of robust optimal H<sub>∞</sub> control for uncertain two-dimensional (2-D) discrete state-delayed systems described by the general model (GM) with norm-bounded uncerta...This paper investigates the problem of robust optimal H<sub>∞</sub> control for uncertain two-dimensional (2-D) discrete state-delayed systems described by the general model (GM) with norm-bounded uncertainties. A sufficient condition for the existence of g-suboptimal robust H<sub><sub></sub></sub><sub>∞</sub> state feedback controllers is established, based on linear matrix inequality (LMI) approach. Moreover, a convex optimization problem is developed to design a robust optimal state feedback controller which minimizes the H<sub><sub><sub></sub></sub></sub><sub>∞</sub> noise attenuation level of the resulting closed-loop system. Finally, two illustrative examples are given to demonstrate the effectiveness of the proposed method.展开更多
Regression models are often transformed into certain alternative forms in statistical inference theory.In this paper,we assume that a general linear model(GLM)is transformed into two diferent forms,and our aim is to s...Regression models are often transformed into certain alternative forms in statistical inference theory.In this paper,we assume that a general linear model(GLM)is transformed into two diferent forms,and our aim is to study some comparison problems under the two transformed general linear models(TGLMs).We frst construct a general vector composed of all unknown parameters under the two diferent TGLMs,derive exact expressions of best linear minimum bias predictors(BLMBPs)by solving a constrained quadratic matrix-valued function optimization problem in the L¨owner partial ordering,and describe a variety of mathematical and statistical properties and performances of the BLMBPs.We then approach some algebraic characterization problems concerning relationships between the BLMBPs under two diferent TGLMs.As applications,two specifc cases are presented to illustrate the main contributions in the study.展开更多
Traditional methods for water table prediction have such defects as extensive calculation and reliance on the presupposition of a homogeneous and regular aquifer.Based on the fundamentals of the general regression neu...Traditional methods for water table prediction have such defects as extensive calculation and reliance on the presupposition of a homogeneous and regular aquifer.Based on the fundamentals of the general regression neural network(GRNN),this article sets up a GRNN model for water level prediction.Case study indicates that this model,even with limited information,has satisfactory prediction accuracy,which,coupled with a simple model structure and relatively high calculation efficiency,mean a vast application prospect for the model.展开更多
In this paper, necessary and sufficient conditions for equalities betweenα~2y^1(I-P_X)y and under the general linear model, whereand α~2 is a known positive number, are derived. Furthermore, when the Gauss-Markovest...In this paper, necessary and sufficient conditions for equalities betweenα~2y^1(I-P_X)y and under the general linear model, whereand α~2 is a known positive number, are derived. Furthermore, when the Gauss-Markovestimators and the ordinary least squares estimators are identical, we obtain a simpleequivalent condition.展开更多
In this paper, we explore some weakly consistent properties of quasi-maximum likelihood estimates (QMLE) concerning the quasi-likelihood equation $ \sum\nolimits_{i = 1}^n {X_i (y_i - \mu (X_i^\prime \beta ))} $ for u...In this paper, we explore some weakly consistent properties of quasi-maximum likelihood estimates (QMLE) concerning the quasi-likelihood equation $ \sum\nolimits_{i = 1}^n {X_i (y_i - \mu (X_i^\prime \beta ))} $ for univariate generalized linear model E(y|X) = μ(X′β). Given uncorrelated residuals {e i = Y i ? μ(X i ′ β0), 1 ? i ? n} and other conditions, we prove that $$ \hat \beta _n - \beta _0 = O_p (\underset{\raise0.3em\hbox{$\smash{\scriptscriptstyle-}$}}{\lambda } _n^{ - 1/2} ) $$ holds, where $ \hat \beta _n $ is a root of the above equation, β 0 is the true value of parameter β and $$ \underset{\raise0.3em\hbox{$\smash{\scriptscriptstyle-}$}}{\lambda } _n $$ denotes the smallest eigenvalue of the matrix S n = ∑ i=1 n X i X i ′ . We also show that the convergence rate above is sharp, provided independent non-asymptotically degenerate residual sequence and other conditions. Moreover, paralleling to the elegant result of Drygas (1976) for classical linear regression models, we point out that the necessary condition guaranteeing the weak consistency of QMLE is S n ?1 → 0, as the sample size n → ∞.展开更多
Abstract Comparison is made between the MINQUE and simple estimate of the error variance in the normal linear model under the mean square errors criterion, where the model matrix need not have full rank and the disper...Abstract Comparison is made between the MINQUE and simple estimate of the error variance in the normal linear model under the mean square errors criterion, where the model matrix need not have full rank and the dispersion matrix can be singular. Our results show that any one of both estimates cannot be always superior to the other. Some sufficient criteria for any one of them to be better than the other are established. Some interesting relations between these two estimates are also given.展开更多
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 National Natural Science Foundation of China (No.90208003, 30200059), the 973 Project (No. 2003CB716106), Doctor training Fund of MOE, P.R.C., and Fok Ying Tong Education Foundation (No.91041)
文摘General linear model (GLM) is the most popular method for functional magnetic resource imaging (fMRI) data analysis . However, its theory is imperfect. The key of this model is how to constitute the design-matrix to model the interesting effects better and separate noises better. For the purpose of detecting brain function activation , according to the principle of GLM,a new convolution model is presented by a new dynamic function convolving with design-matrix,which combining with t-test can be used to detect brain active signal. The fMRI imaging result of visual stimulus experiment indicates that brain activities mainly concentrate among v1and v2 areas of visual cortex, and also verified the validity of this technique.
基金the Natural Science Foundation of Guangdong Province(0 1 0 4 86 )
文摘This paper provides further contributions to the theory of linear sufficiency in the general Gauss-Markov model E(y)=Xβ,Var (y)=V.The notion of linear sufficiency introduced by Baksalary and Kala(1981) and Drygas(1983) is extended for any specific estimable function c′β.Some general results with respect to the extended concept are obtained.An essential result concerning the former notion is a direct consequence of this paper.
基金Supported by the Talent Program of Anhui Science and Technology University(Grant No.XXYJ201703)
文摘In this paper, we give the representation of the best linear unbiased predictor（BLUP）of the new observations under Mrf. Through the representation, we give necessary and sufficient conditions that the estimators, OLSEs（ordinary least squares estimators） and BLUEs（best linear unbiased estimators）, under Mf and Mrf, and the predictor, BLUP, under Mf continue to be the BLUP under Mrf, respectively.
基金Supported by the National Natural Science Foundation of China(10371092)
文摘We study the quasi likelihood equation in Generalized Linear Models(GLM)with adaptive design∑(i=1)^n xi(yi-h(x'iβ))=0,where yi is a q=vector,and xi is a p×q random matrix.Under some assumptions,it is shown that the Quasi-Likelihood equation for the GLM has a solution which is asymptotic normal.
基金supported by the National Postdoctoral Program for Innovative Talents, Postdoctoral Science Foundation of China (No. 2017M610740)supports from Hefei General Aviation Research Institute, Beihang University
文摘The study of the development cost of general aviation aircraft is limited by small samples with many cost-driven factors. This paper investigates a parametric modeling method for prediction of the development cost of general aviation aircraft. The proposed technique depends on some principal components, acquired by utilizing P value analysis and gray correlation analysis. According to these principal components, the corresponding linear regression and BP neural network models are established respectively. The feasibility and accuracy of the P value analysis are verified by comparing results of model fitting and prediction. A sensitivity analysis related to model precision and suitability is discussed in detail. Results obtained in this study show that the proposed method not only has a certain degree of versatility, but also provides a preliminary prediction of the development cost of general aviation aircraft.
文摘In this paper, we propose the test statistic to check whether the nonparametric function in partially linear models is linear or not. We estimate the nonparametric function in alternative by using the local linear method, and then estimate the parameters by the two stage method. The test statistic under the null hypothesis is calculated, and it is shown to be asymptotically normal.
文摘We propose the test statistic to check whether the nonpararnetric functions in two partially linear models are equality or not in this paper. We estimate the nonparametric function both in null hypothesis and the alternative by the local linear method, where we ignore the parametric components, and then estimate the parameters by the two stage method. The test statistic is derived, and it is shown to be asymptotically normal under the null hypothesis.
文摘This paper considers the problem of delay-dependent robust optimal H<sub>∞</sub> control for a class of uncertain two-dimensional (2-D) discrete state delay systems described by the general model (GM). The parameter uncertainties are assumed to be norm-bounded. A linear matrix inequality (LMI)-based sufficient condition for the existence of delay-dependent g-suboptimal state feedback robust H<sub>∞</sub> controllers which guarantees not only the asymptotic stability of the closed-loop system, but also the H<sub>∞</sub> noise attenuation g over all admissible parameter uncertainties is established. Furthermore, a convex optimization problem is formulated to design a delay-dependent state feedback robust optimal H<sub>∞</sub> controller which minimizes the H<sub>∞</sub> noise attenuation g of the closed-loop system. Finally, an illustrative example is provided to demonstrate the effectiveness of the proposed method.
文摘This paper investigates the problem of robust optimal H<sub>∞</sub> control for uncertain two-dimensional (2-D) discrete state-delayed systems described by the general model (GM) with norm-bounded uncertainties. A sufficient condition for the existence of g-suboptimal robust H<sub><sub></sub></sub><sub>∞</sub> state feedback controllers is established, based on linear matrix inequality (LMI) approach. Moreover, a convex optimization problem is developed to design a robust optimal state feedback controller which minimizes the H<sub><sub><sub></sub></sub></sub><sub>∞</sub> noise attenuation level of the resulting closed-loop system. Finally, two illustrative examples are given to demonstrate the effectiveness of the proposed method.
文摘Regression models are often transformed into certain alternative forms in statistical inference theory.In this paper,we assume that a general linear model(GLM)is transformed into two diferent forms,and our aim is to study some comparison problems under the two transformed general linear models(TGLMs).We frst construct a general vector composed of all unknown parameters under the two diferent TGLMs,derive exact expressions of best linear minimum bias predictors(BLMBPs)by solving a constrained quadratic matrix-valued function optimization problem in the L¨owner partial ordering,and describe a variety of mathematical and statistical properties and performances of the BLMBPs.We then approach some algebraic characterization problems concerning relationships between the BLMBPs under two diferent TGLMs.As applications,two specifc cases are presented to illustrate the main contributions in the study.
文摘Traditional methods for water table prediction have such defects as extensive calculation and reliance on the presupposition of a homogeneous and regular aquifer.Based on the fundamentals of the general regression neural network(GRNN),this article sets up a GRNN model for water level prediction.Case study indicates that this model,even with limited information,has satisfactory prediction accuracy,which,coupled with a simple model structure and relatively high calculation efficiency,mean a vast application prospect for the model.
基金Supported by China Mathematics Tian Yuan Youth Foundation (10226024) and China Postdoctoral Science Foundation.
文摘In this paper, necessary and sufficient conditions for equalities betweenα~2y^1(I-P_X)y and under the general linear model, whereand α~2 is a known positive number, are derived. Furthermore, when the Gauss-Markovestimators and the ordinary least squares estimators are identical, we obtain a simpleequivalent condition.
基金supported by the President Foundation (Grant No. Y1050)the Scientific Research Foundation(Grant No. KYQD200502) of GUCAS
文摘In this paper, we explore some weakly consistent properties of quasi-maximum likelihood estimates (QMLE) concerning the quasi-likelihood equation $ \sum\nolimits_{i = 1}^n {X_i (y_i - \mu (X_i^\prime \beta ))} $ for univariate generalized linear model E(y|X) = μ(X′β). Given uncorrelated residuals {e i = Y i ? μ(X i ′ β0), 1 ? i ? n} and other conditions, we prove that $$ \hat \beta _n - \beta _0 = O_p (\underset{\raise0.3em\hbox{$\smash{\scriptscriptstyle-}$}}{\lambda } _n^{ - 1/2} ) $$ holds, where $ \hat \beta _n $ is a root of the above equation, β 0 is the true value of parameter β and $$ \underset{\raise0.3em\hbox{$\smash{\scriptscriptstyle-}$}}{\lambda } _n $$ denotes the smallest eigenvalue of the matrix S n = ∑ i=1 n X i X i ′ . We also show that the convergence rate above is sharp, provided independent non-asymptotically degenerate residual sequence and other conditions. Moreover, paralleling to the elegant result of Drygas (1976) for classical linear regression models, we point out that the necessary condition guaranteeing the weak consistency of QMLE is S n ?1 → 0, as the sample size n → ∞.
基金Partially supported by the National Natural Science Foundation of China (No.10271010)the Natural Science Foundation of Beijing and a Project of Science and Technology of Beijing Education Committee.
文摘Abstract Comparison is made between the MINQUE and simple estimate of the error variance in the normal linear model under the mean square errors criterion, where the model matrix need not have full rank and the dispersion matrix can be singular. Our results show that any one of both estimates cannot be always superior to the other. Some sufficient criteria for any one of them to be better than the other are established. Some interesting relations between these two estimates are also given.
