20 February 2006, Volume 22 Issue 1

    

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学术论文
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  Test of the Error Variance Homogeneity about Models of Stochastic Effect Variance Analysis ($k=2$)

  ZHANG XINYU

  CHINESE JOURNAL OF APPLIED PROBABILITY AND STATIST. 2006, 22(1): 0-0.

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  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.
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  Several Problems of Consistency of the Maximum Likelihood Estimate in Generalized Linear Model

  DING JIELI

  CHINESE JOURNAL OF APPLIED PROBABILITY AND STATIST. 2006, 22(1): 1-9.

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  On Some Limit Theorems for Sums of NA Random Matrix Sequences

  WU QUNYING;WANG YUANQING;WU YANCUN

  CHINESE JOURNAL OF APPLIED PROBABILITY AND STATIST. 2006, 22(1): 56-62.

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  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.
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  Maximum Likelihood Estimation in Generalized Linear:Large Mixed Models Using Monte Carlo Methods: Application to Small-Area Estimation of Breast Cancer Mortality

  CHINESE JOURNAL OF APPLIED PROBABILITY AND STATIST. 2006, 22(1): 69-80.

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  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.
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  General Error-in-Variable Linear Models

  LI YONG;TAN SUOZHENG

  CHINESE JOURNAL OF APPLIED PROBABILITY AND STATIST. 2006, 22(1): 81-88.

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  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.
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  Nonparametric Variance Function Estimate in Generalized Linear Models

  Chen Xia

  CHINESE JOURNAL OF APPLIED PROBABILITY AND STATIST. 2006, 22(1): 337-346.

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  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.