响应变量缺失下变系数部分线性测量误差模型的约束估计

当响应变量缺失、协变量具有测量误差,且模型参数部分有附加的线性约束时,主要研究一类变系数部分线性模型的统计推断问题.利用借补技术来补全缺失数据,并借助修正的profile最小二乘估计得到了模型参数分量和非参数分量的借补约束估计,并证明了参数分量的估计满足渐近正态性,同时非参数分量的估计与通常的非参数回归函数的估计具有相同的收敛速度.其次利用profile拉格朗日乘子检验对模型参数的约束条件进行检验,并证明了给出的检验统计量在原假设成立时渐近地服从标准卡方分布.数值模拟进一步表明对缺失数据进行借补可以有效地提高参数估计和假设检验的效率.     

缺失数据下部分非线性变系数EV模型的统计推断

该文研究了响应变量缺失下半参数部分非线性变系数EV模型的统计推断问题,利用逆概率加权局部纠偏profile最小二乘法构造了模型中非参数分量和参数分量的估计,证明了估计量的渐近正态性.通过数值模拟和实际数据分析,验证了所提出的估计方法是有效的.     

缺失数据下半参数变系数部分线性模型的统计推断

该文研究协变量随机缺失下半参数变系数部分线性模型的统计推断.利用逆概率加权最小二乘方法给出了模型中参数分量和非参数分量的估计,并证明了它们的渐近正态性.另外该文又提出了一个逆概率加权经验对数似然比统计量,并证明该统计量服从标准χ~2分布,从而构造了模型中参数分量的经验似然置信域.最后通过模拟研究和实例分析说明该文提出的方法具有较好的有限样本性质.     

基于生存数据的半参数变换的删失回归估计

生存数据经过未知的单调变换后等于协变量的线性函数加上随机误差, 随机误差的分布函数已知或是带未知参数的已知函数\bd 本文先给出未知单调变换的一个相合估计, 再对删失数据做变换, 在此基础上给出了协变量系数的最小二乘估计, 并讨论它的大样本性质.     

因变量缺失下变系数部分线性测量误差模型的变量选择

主要研究因变量存在缺失且协变量部分包含测量误差情形下,如何对变系数部分线性模型同时进行参数估计和变量选择.我们利用插补方法来处理缺失数据,并结合修正的profile最小二乘估计和SCAD惩罚对参数进行估计和变量选择.并且证明所得的估计具有渐近正态性和Oracle性质.通过数值模拟进一步研究所得估计的有限样本性质.     

对部分线性模型用惩罚最小二乘估计时最优光滑化的注记

部分线性模型也就是响应变量关于一个或者多个协变量是线性的, 但对于其他的协变量是非线性的关系\bd 对于部分线性模型中的参数和非参数部分的估计方法, 惩罚最小二乘估计是重要的估计方法之一\bd 对于这种估计方法, 广义交叉验证法提供了一种确定光滑参数的方法\bd 但是, 在部分线性模型中, 用广义交叉验证法确定光滑参数的最优性还没有被证明\bd 本文证明了利用惩罚最小二乘估计对于部分线性模型估计时, 用广义交叉验证法选择光滑参数的最优性\bd 通过模拟验证了本文中所提出的用广义交叉验证法选择光滑参数具有很好的效果, 同时, 本文在模拟部分比较了广义交叉验证和最小二乘交叉验证的优劣.     

部分线性变系数模型的随机约束岭估计

作为变系数模型和部分线性模型的推广,部分线性变系数模型近年来得到越来越多的关注.本文考虑该模型在线性部分自变量存在多重共线性并且参数分量附加有随机约束条件时的估计问题.基于profile最小二乘技术以及岭估计和混合估计方法,构造参数分量的profile混合岭估计,并且研究所提估计量的渐近性质.最后利用数值模拟验证所提估计方法的有效性.     

缺失数据下EV模型的调整最小二乘估计

该文考虑协变量缺失时的多元线性EV模型参数的估计, 其中协变量的缺失机制是Rubin(1976)提出的随机缺失(MAR). 利用加权调整最小二乘方法给出参数估计, 证明了估计的相合性和渐近正态性. 数值模拟结果表明所给的估计性态良好.     

基于纵向数据的半参数变系数部分线性回归模型

纵向数据是数理统计研究中的复杂数据类型之一0,在生物、医学和经济学中具有广泛的应用．在实际中经常需要对纵向数据进行统计分析和建模．文章讨论了纵向数据下的半参数变系数部分线性回归模型,这里的纵向数据的在纵向观察在时间上可以是不均等的,也可看成是按某一随机过程来发生．所研究的半参数变系数模型包括了许多半参数模型,比如部分线性模型和变系数模型等．利用计数过程理论和局部线性回归方法,对于纵向数据下半参数变系数进行了统计推断,给出了参数分量和非参数分量的profile最小二乘估计,研究了这些估计的渐近性质,获得这些估计的相合性和渐近正态性．     

Ⅰ型区间删失情形下部分线性模型Sieve极大似然估计渐近性质

本文在响应变量的观测值为Ⅰ型区间删失数据的情形下,讨论部分线性模型Ｓｉｅｖｅ极大似然估计的渐近性质．在一定条件下证明了该估计具有强相合性;参数分量的估计具有渐近正态性,并且是渐近有效的;非参数分量估计达到了最优弱收敛速度．     

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本文研究了线性模型响应变量被污染且被区间截断下的参数估计问题, 借助于区间数据的无偏转换, 得到了回归系数和污染系数的估计, 并在一定的条件下得到了这些估计的强相合性. 通过若干模拟例子说明, 尽管数据经过污染和区间截断的双重信息损失, 但用本文提出的方法得到的估计, 仍能取得良好的估计效果     

Markovian modeling for dependent interrecurrence times in bladder cancer

A methodology to model a process in which repeated events occur is presented. The context is the evolution of non-muscle-invasive bladder carcinoma (NMIBC), characterized by recurrent relapses. It is based on the statistical flowgraph approach, a technique specifically suited for semi-Markov processes. A very useful feature of the flowgraph framework is that it naturally incorporates the management of censored data. However, this approach presents two difficulties with the process to be modeled. On one hand, the management of covariates is not straightforward. However, it is of great interest to know how the characteristics of a certain patient influence the evolution of the disease. On the other hand, repeated events on the same subject are generally not independent, in which case the semi-Markov framework is not sufficient because the semi-Markov assumption implies independence among waiting time distributions. We solve this issue by extending the flowgraph methodology using the Markovian arrival process (MAP), which does successfully model the dependence between events. Along the way, we provide a procedure to consider covariates and censored times in MAPs, a pending task needed in this field. In short, we have managed to extend the flowgraph methodology beyond the semi-Markovian framework, simplifying the incorporation of covariates and keeping the management of censored times. All of which has allowed us to build a multistate model of the evolution of NMIBC. The developed model allows us to compute the Survival function for any evolution of a patient with specific clinic-pathological characteristics in this primary tumor.     

具有时变协变量的纵向资料的线性混合效应模型及算法

许多研究领域中都会涉及到纵向资料的分析处理.在纵向资料的分析中,常常遇到带有时变协变量的情况.传统的方差分析难以处理具有时变协变量的纵向资料.对线性混合效应模型应用于分析这类资料进行了方法学的探讨,并编制了模型拟合的SAS程序.通过实例应用,对线性混合效应模型分析纵向资料的方法和过程给出详细的介绍.     

纵向删失数据下线性模型的相合估计

医药临床试验,生存分析,可靠性统计等研究领域,由于考虑到时间和费用问题,研究往往有一定期限.因为研究到期的被迫结束或者某些病人中途退出试验,最后得到的试验结果往往是删失数据.对于删失数据,采用无偏转换的方法处理,方法的最大优点是得到的估计量为显式解.首先讨论了在纵向右删失数据下线性回归模型回归系数估计的均方相合性,并且把结论推广到了污染线性模型,得到了污染系数、回归系数的强相合估计.     

区间数据情形下线性模型的经验似然推断

§1Introduction Instatisticalapplications,weoftenencounterintervalcensoreddatawhenafailure timeYcannotbeobserved,butcanonlybedeterminedtolieinanintervalobtainedfroma sequenceofexaminationtimes.Forinstance,themaximumdosagewhichpatientscan endureisconcerned.LetYibethemaximumdosagewhichtheithpatientcanendure,Ui,j(j=1,2,...,k)bethedosagewhichthepatienthasbeentested.ItisobviousthatYiis unobservable.SupposetheithpatientisnormalwhenthedosageisUi,j,andhe(orshe)is abnormalwhenthedosageisUi,j+1.Then…     

A Bayesian Estimate of the Risk of Tick-Borne Diseases

The paper considers the problem of estimating the risk of a tick-borne disease in a given region. A large set of epidemiological data is evaluated, including the point pattern of collected cases, the population map and covariates, i.e. explanatory variables of geographical nature, obtained from GIS.The methodology covers the choice of those covariates which influence the risk of infection most. Generalized linear models are used and AIC criterion yields the decision. Further, an empirical Bayesian approach is used to estimate the parameters of the risk model. Statistical properties of the estimators are investigated. Finally, a comparison with earlier results is discussed from the point of view of statistical disease mapping.     

ESTIMATORS AND SOME BEHAVIORS FOR A PARTIALLY LINEAR MODEL WITH CENSORED DATA

1991MRSubjectClassification62G05,62E20,62J991IntroductionInrecelltyearstherehasbeedagreatdealofinterestilltheallalysisofpal.tiallylilleal*model.Speckman[']proposedaluethodofkernelsmoothingandthepartialregressionestinlator.ChenandShiau[2]showedthatatwo-stagesplinesmoothingmethodanddiscussedthelarge-samplebehavioroftwoefficientestimatorsforthepararl--letriccorxlponentofapartiallylineal.model.Inthispaper,weconsidertheestimationofpartiallylinearlliodelwith,f..sorilig.Anappealingnonparallletric…     

随机截尾下污染数据部分线性模型的强相合估计

考虑一类新的污染数据部分线性模型,当受污染后的因变量被随机右截断时,就截断分布已知的情形,利用所获得截断观测数据构造了模型中的参数分量,非参数分量及污染系数的估计量,并在适当的条件下,证明了这些估计量的强相合性.     

Asymptotics for a censored generalized linear model with unknown link function

For censored response variable against projected co-variable, a generalized linear model with an unknown link function can cover almost all existing models under censorship. Its special cases include the accelerated failure time model with censored data. Such a model in the uncensored case is called the single-index model in econometrics. In this paper, we systematically study the asymptotic properties. We derive the central limit theorem and the law of the iterated logarithm for an estimator of the direction parameter. We also obtain the optimal convergence rate of an estimator of the unknown link function in the model.      

Diagnostic Measures for Censored Linear Models based on Empirical Likelihood Method

In this paper, the diagnostic measures for censored linear models are studied based on the empirical likelihood method. First, the diagnostic measures for linear models are studied; Then, the censored linear models are converted to linear models, and the diagnostic measures for converted models are studied; Last, simulation studies and real data analysis are given to illustrate the validity of statistical diagnostic measures.