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 2006 Vol.22 Issue.1,Published 2006-02-20
 学术论文
 0 Test of the Error Variance Homogeneity about Models of Stochastic Effect Variance Analysis ($k=2$) ZHANG XINYU Considering the model of stochastic effect variauce analysis in two times repeated trials, we present in this paper a method to test the homogeneity of the error variances, i.e., to test $H_0:\sigma_1^2=\sigma_2^2$, by use of the piecewise likelihood ratio test, and then give some concrete conclusions according to three common models. 2006 Vol. 22 (1): 0-0 [Abstract] ( 1931 ) [HTML 0KB] [ PDF 0KB] ( 505 )
 1 Several Problems of Consistency of the Maximum Likelihood Estimate in Generalized Linear Model DING JIELI 2006 Vol. 22 (1): 1-9 [Abstract] ( 2548 ) [HTML 0KB] [ PDF 0KB] ( 1033 )
 56 On Some Limit Theorems for Sums of NA Random Matrix Sequences WU QUNYING;WANG YUANQING;WU YANCUN In this paper, the week law of large numbers, $L_p$ convergence and complete convergence of sums of NA random matrix sequences are studied. The results in series of previous papers are enriched and extended. 2006 Vol. 22 (1): 56-62 [Abstract] ( 1936 ) [HTML 0KB] [ PDF 0KB] ( 476 )
 69 Maximum Likelihood Estimation in Generalized Linear:Large Mixed Models Using Monte Carlo Methods: Application to Small-Area Estimation of Breast Cancer Mortality This paper provides a Monte Carlo approach for achieving maximum likelihood in generalized linear mixed models. Practical approaches for accessing convergence and precision of parameters are also discussed. Simulation study shows unbiased fixed effects parameter estimation with covariance components estimation comparable to previous study. Application for a small-area estimation of breast cancer mortality using Poisson distribution is illustrated. 2006 Vol. 22 (1): 69-80 [Abstract] ( 1922 ) [HTML 0KB] [ PDF 0KB] ( 347 )
 81 General Error-in-Variable Linear Models LI YONG;TAN SUOZHENG In linear error-in-variable models, it is supposed that the observed values of all variables include unknown measurable errors. So the models can not be used to describe the linear relationship of variables in which some variables can be observed exactly, and others can not be observed exactly. Therefore, we offer general error-in-variable models to solve the problem, and discuss the least squares method and likelihood method for the general models. 2006 Vol. 22 (1): 81-88 [Abstract] ( 1941 ) [HTML 0KB] [ PDF 0KB] ( 346 )
 337 Nonparametric Variance Function Estimate in Generalized Linear Models Chen Xia This article advances an improved estimate $\wh\sigma_{n}(u)$ of $\sigma(u)$, and under some conditions, proves $\sup\limits_{u}|\wh\sigma_{n}(u)-\sigma(u)|\rightarrow0$ in probability with faster speed. 2006 Vol. 22 (1): 337-346 [Abstract] ( 1688 ) [HTML 0KB] [ PDF 439KB] ( 212 )

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