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．     

部分线性模型参数极大似然估计的收敛速度

针对部分线性模型, 在其随机误差的分布函数属于刻度族, 刻度参数未知, 并且响应变量的观测值为区间删失数据的情形下, 讨论了其Sieve极大似然估计的强相合性和弱收敛速度.     

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。     

广义线性回归极大似然估计的强相合性

设有该文第1节所描述的广义线性回归模型,以$\underline{\lambda}_n$和$\overline{\lambda}_n$分别记$\sum\limits_{i=1}^{n}Z_iZ_i^{\prime}$的最小和最大特征根,$\hat{\beta}_n$记$\beta_0$的极大似然估计.在文献[1]中,当\{$Z_i,i\ge1$\}有界时得到$\hat{\beta}_n$强相合的充分条件,在自然联系和非自然联系下分别为$\underline{\lambda}_n\rightarrow\infty$, $(\overline{\lambda}_n)^{1/2+\delta}=O(\underline{\lambda}_n)$（对某$\delta>0$)以及$\underline{\lambda}_n\rightarrow\infty$, $\overline{\lambda}_n=O(\underline{\lambda}_n)$.作者将后一结果改进为只要求$(\overline{\lambda}_n)^{1/2+\delta}=O(\underline{\lambda}_n)$,从而与自然联系情况下的条件达到一致.     

Ⅱ型区间删失数据下广义部分线性模型的筛极大似然估计(英文)

This article concerded with a semiparametric generalized partial linear model （GPLM） with the type Ⅱ censored data. A sieve maximum likelihood estimator （MLE） is proposed to estimate the parameter component, allowing exploration of the nonlinear relationship between a certain covariate and the response function. Asymptotic properties of the proposed sieve MLEs are discussed. Under some mild conditions, the estimators are shown to be strongly consistent. Moreover, the estimators of the unknown parameters are asymptotically normal and efficient, and the estimator of the nonparametric function has an optimal convergence rate.     

广义线性模型中极大拟似然估计的强相合性

假定在一个具有一般联系函数的广义线性模型中, q×1响应变量y_i是可观测的, p×q协变量Z_i是固定设计的, 以记的最小特征根. 在(对某个α>0), sup_i≥1E||y_i||r <∞(对某个r>1/α)和其他正则条件下, 证明了以概率为1, 当n充分大时, 未知回归参数向量的拟似然方程有一个解, 它收敛于参数真值. 这一结果是对文献中的相应结果的实质性改进.     

广义线性模型中极大拟似然估计的强收敛速度

在sup_{i ≥1}E||y_i||^2+α < ∞(对某个α > 0)和其它正则条件下, 证明了一般联系函数的多维广义线性模型拟似然估计的强相合性, 并得到了强收敛速度, 其中 y_i 是响应变量. 此结果是对文献中相应结果的改进.     

广义线性模型中极大拟似然估计的渐近正态性与强相合性

在广义线性模型中,当λn→∞,supi≥1 E||ei||2+α<∞(对某个α>0), 且其他一些正则条件满足时,证明了极大拟似然估计(MQLE)是渐近正态的;进 一步假定 ,证明了MQLE是强相合的,其中λn(λn) 是∑i=1 nZiZiT的最小(最大)特征根,Zi是有界的p×q回归系数,ei=yi-Eyi,yi 是q×1响应变量．也讨论了设计阵Zi是自适应的情形．     

广义线性模型中拟极大似然估计的强相合性及收敛速度

在广义线性模型(GLM)因变量有正确表述的假定及其他一些光滑性条件之下, 证明以概率1当样本量n充分大时, 广义线性模型的拟似然方程有一解, 且可求出该解收敛于真值的速度, 在一个重要的特例中, 该速度同于独立同分布随机变量序列部分和的重对数律所确定的速度, 故而不能改进.     

一种改进的半参数极大似然法估计量的渐近性质

本文研究线性回归模型, Y=β'X+∈,并假设Y可被右删失,∈的分布函 数F0未知.本文证明,在某些条件下, β的一种改进的半参数极大似然法估计量β 有相合性. 同时证明,如果F0不连续,则P{β≠βi.o.}=0.这意味着以概率为一, 当样本很大时, β=β.文献中的现有估计量未见有关于这一性质的报道.相反,包括 Buckley-James估计量及M-估计量在内的大多数的估计量,都不满足这一性质.     

Asymptotic Properties of a Modified Semi-Parametric MLE in Linear Regression with Right-Censored Data

Consider a linear regression model, Y=β′X+ε where Y may be right censored and the cdf F _o of ε is unknown. We show that a modified semi-parametric MLE, denoted by is strongly consistent under certain regularity conditions. Moreover, if F _o is discontinuous, then P(≠β i.o.)=0, which means that P(=β if the sample size is large)=1. The latter property has not been reported for the existing estimators. By contrast, most estimators, such as the Buckley-James estimator and M-estimators , satisfy that P(≠β i.o.)=1. Received April 23, 2001, Accepted November 13, 2001     

SOME PROPERTIES OF ROSEN’S MLE FOR GENERAL DISTRIBUTIONS

1 IntroductionConsider the lnultivariate linear model (MLM) as follows:mX = Z AiBiC E (1)i= 1where X, Ai, Bi and C are p x nfp x qi(qi 5 p), qi x ki and ki x n matrices respectively, Z is ap x p definite positive matrix with p(C1) p 5 n and R(CL) G R(Cfu--,) g' g R(CI), p(.)and R(.) stand for the rank and the colunu spanned linear space Of a matriX respbctively.e = (e1,'2,... f e.), e1le21',f n are iid. p--variate random vectors with D(e1) = Z > 0,E(El) = 0, A: aild C: are …     

Convergence rates of MLE in a partly linear model

This paper considers the estimation for a partly linear model with case 1 interval censored data. We assume that the error distribution belongs to a known family of scale distributions with an unknown scale parameter. The sieve maximum likelihood estimator (MLE) for the model’s parameter is shown to be strongly consistent, and the convergence rate of the estimator is obtained and discussed.     

Error bounds for asymptotic expansions of the distribution of the MLE in a GMANOVA model

Summary In this paper we obtain asymptotic expansions for the distribution function and the density function of a linear combination of the MLE in a GMANOVA model, and for the density function of the MLE itself. We also obtain certain error bounds for the asymptotic expansions.     

带有不完全信息随机截尾试验下最大似然估计的重对数律

本文在条件($\Phi$)下,证明了带有不完全信息随机截尾试验下最大似然估计的收敛速度符合重对数律,并验证了Weibull分布、对数正态分布满足条件($\Phi$).     

Generalized MLE of a Joint Distribution Function with Multivariate Interval-Censored Data

We consider the problem of estimation of a joint distribution function of a multivariate random vector with interval-censored data. The generalized maximum likelihood estimator of the distribution function is studied and its consistency and asymptotic normality are established under the case 2 multivariate interval censorship model and discrete assumptions on the censoring random vectors.