非线性再生散度随机效应模型的贝叶斯分析

非线性再生散度随机效应模型是一类非常广泛的统计模型，包括了线性随机效应模型、非线性随机效应模型、广义线性随机效应模型和指数族非线性随机效应模型等．本文研究非线性再生散度随机效应模型的贝叶斯分析．通过视随机效应为缺失数据以及应用结合Gibbs抽样技术和Metropolis-Hastings算法（简称MH算法）的混合算法获得了模型参数与随机效应的同时贝叶斯估计．最后，用一个模拟研究和一个实际例子说明上述算法的可行眭．     

半参数再生散度非线性模型中参数的投影核和刀切估计的相合性与渐近正态性

半参数再生散度非线性模型(SRDNM)是再生散度非线性模型和半参数回归模型的自然推广和发展,它包括半参数非线性模型和半参数广义线性模型等特殊模型. 基于非参数部分的局部核估计, 给出了SRDNM模型中参数的投影核估计与刀切估计, 并对其进行了理论比较. 在一定的正则条件下,得到了这两类估计的强相合性与渐近正态性. 相比之下, 刀切估计比投影核估计具有更大的渐近方差. 最后, 模拟研究和实例分析被用来说明所给方法的有效性.     

带有不可忽略缺失数据的半参数再生散度模型的贝叶斯分析

半参数再生散度模型是再生散度模型和半参数回归模型的推广,包括了半参数广义线性模型和广义部分线性模型等特殊类型.讨论的是该模型在响应变量和协变量均存在非随机缺失数据情形下参数的Bayes估计和基于Bayes因子的模型选择问题.在分析中,采用了惩罚样条来估计模型中的非参数成分,并建立了Bayes层次模型;为了解决Gibbs抽样过程中因参数高度相关带来的混合性差以及因维数增加导致出现不稳定性的问题,引入了潜变量做为添加数据并应用了压缩Gibbs抽样方法,改进了收敛性;同时,为了避免计算多重积分,利用了M-H算法估计边缘密度函数后计算Bayes因子,为模型的选择比较提供了一种准则.最后,通过模拟和实例验证了所给方法的有效性.     

非线性再生散度随机效应模型似然函数的Laplace逼近

首先提出用Lap lace逼近方法对非线性再生散度随机效应模型的边缘对数似然函数进行近似,然后基于近似的边缘对数似然函数利用F isher'sscoring迭代算法得到了模型参数的极大似然估计.模拟研究和实例分析表明了该算法的可行性.     

非线性再生散度随机效应模型的渐近性质

对非线性再生散度随机效应模型, 该文给出了类似于Barndroff-Nielson, Cox (1989)和Severin, Wong (1992)的正则条件, 基于这些正则条件和Laplace近似, 证明了该模型参数极大似然估计的存在性、强相合性和渐近正态性.     

再生散度分布族下联合方位与散度混合专家回归模型的参数估计

再生散度分布族是一种比指数族分布更加广泛的分布,其适用性更强,为了了解散度的来源,基于一般的方位模型,提出了联合方位与散度模型,即再生散度分布族下联合方位与散度模型,而混合专家回归模型在统计机器学习方面被广泛的研究,并用于解决异质总体的分类问题.本文研究了再生散度分布族下的混合专家回归模型,并利用MM及EM算法对参数进行极大似然估计.最后,通过随机模拟和实例研究说明该模型和方法是有效和有用的.     

半参数非线性再生散度模型的渐近推断

对半参数非线性再生散度模型,先引入最佳偏差曲线.再求非参数部分的局部线性估计,然后得到参数的广义边侧极大似然估计.同时,基于正则条件,证明了所得估计的存在性,强相合性和渐近正态性,推广了已有文献的工作.     

非线性再生散度模型参数置信域的曲率表示

本文对非线性再生散度模型在Ｅｕｃｌｉｄ空间建立了几何结构。在此基础上,研究了该模型参数和子集参数的三种近似置信域,推广了Ｈａｍｉｌｔｏｎ和韦博成等人的工作。     

非线性再生散度随机效应模型参数置信域的曲率表示

该文基于Laplace逼近建立了非线性再生散度随机效应模型在Euclid空间中的几何结构, 并在此基础上研究了此模型参数和子集参数的置信域, 进一步推广和发展了 Hamilton, Watts 和 Bates^[1]关于正态非线性回归模型, Wei^[2,3]关于嵌入模型和指数族非线性模型, Zhu, Tang 和 Wei^[4]关于半参数非线性模型,唐年胜、韦博成和王学仁^[5]关于非线性再生散度模型, Tang 和 Wang^[6]关于拟似然非线性模型等的结果.     

非线性再生散度模型的几何结构及其渐近推断

本文基于唐年胜等（1999）对非线性再生散度模型在欧氏空间建立的微分几何结构导出了与估计有关的随机展开以及与统计曲率有关的若干渐近推断，推广了韦博成等人关于非线性回归模型和指数族非线性模型的相应结果。     

Local influence analysis for semiparametric reproductive dispersion nonlinear models

The present paper proposes a semiparametric reproductive dispersion nonlinear model (SRDNM) which is an extension of the nonlinear reproductive dispersion models and the semiparameter regression models. Maximum penalized likelihood estimates (MPLEs) of unknown parameters and nonparametric functions in SRDNM are presented. Assessment of local influence for various perturbation schemes are investigated. Some local influence diagnostics are given. A simulation study and a real example are used to illustrate the proposed methodologies.     

Consistency and asymptotic normality of profilekernel and backfitting estimators in semiparametric reproductive dispersion nonlinear models

Semiparametric reproductive dispersion nonlinear model (SRDNM) is an extension of nonlinear reproductive dispersion models and semiparametric nonlinear regression models, and includes semiparametric nonlinear model and semiparametric generalized linear model as its special cases. Based on the local kernel estimate of nonparametric component, profile-kernel and backfitting estimators of parameters of interest are proposed in SRDNM, and theoretical comparison of both estimators is also investigated in this paper. Under some regularity conditions, strong consistency and asymptotic normality of two estimators are proved. It is shown that the backfitting method produces a larger asymptotic variance than that for the profile-kernel method. A simulation study and a real example are used to illustrate the proposed methodologies. This work was supported by National Natural Science Foundation of China (Grant Nos. 10561008, 10761011), Natural Science Foundation of Department of Education of Zhejiang Province (Grant No. Y200805073), PhD Special Scientific Research Foundation of Chinese University (Grant No. 20060673002) and Program for New Century Excellent Talents in University (Grant No. NCET-07-0737)     

Bayesian analysis of non-linear structural equation models with non-ignorable missing outcomes from reproductive dispersion models

Non-linear structural equation models are widely used to analyze the relationships among outcomes and latent variables in modern educational, medical, social and psychological studies. However, the existing theories and methods for analyzing non-linear structural equation models focus on the assumptions of outcomes from an exponential family, and hence can’t be used to analyze non-exponential family outcomes. In this paper, a Bayesian method is developed to analyze non-linear structural equation models in which the manifest variables are from a reproductive dispersion model (RDM) and/or may be missing with non-ignorable missingness mechanism. The non-ignorable missingness mechanism is specified by a logistic regression model. A hybrid algorithm combining the Gibbs sampler and the Metropolis–Hastings algorithm is used to obtain the joint Bayesian estimates of structural parameters, latent variables and parameters in the logistic regression model, and a procedure calculating the Bayes factor for model comparison is given via path sampling. A goodness-of-fit statistic is proposed to assess the plausibility of the posited model. A simulation study and a real example are presented to illustrate the newly developed Bayesian methodologies.     

非线性再生散度模型基于统计曲率的诊断统计量

对非线性散度模型在Euclid空间建立几何结构。在此基础上，研究了均值漂移模型的曲率度量。从而导出相应Cook距离，似然距离等诊断统计量的二阶近似公式。     

离散型指数族非线性模型的变离差检验

It is necessary to test for varying dispersion in generalized nonlinear models. Wei,et al (1998) developed a likelihood ratio test,a score test and their adjustments to test for varying dispersion in continuous exponential family nonlinear models. This type of problem in the framework of general discrete exponential family nonlinear models is discussed. Two types of varying dispersion, which are random coefficients model and random effects model, are proposed ,and corresponding score test statistics are constructed and expressed in simple ,easy to use ,matrix formulas.     

TESTING FOR VARYING DISPERSION IN DISCRETE EXPONENTIAL FAMILY NONLINEAR MODELS

§ 1　 Introduction and modelsThe general form of exponential family nonlinear models isg(μi) =f(xi,﹀) , (1 )where,g(· ) is a monotonic link function,f is a known differentiable nonlinear functionand﹀ is a p-vectoroffixed population parameters;μi=E(yi) and the density of response yiisp(yi) =exp{[yiθi -b(θi) -c(yi) ] -12 a(yi,) } ,(2 )whereθi is the natural parameter, is the dispersion parameter.From [1 1 ] ,μi=b(θi) ,Vi=Var(yi) =- 1 b(θi) .If f(xi,β) =x Ti ﹀,then mod…