Likelihood-Based Boosting in Binary and Ordinal Random Effects Models

This article presents a likelihood-based boosting approach for fitting binary and ordinal mixed models. In contrast to common procedures, this approach can be used in high-dimensional settings where a large number of potentially influential explanatory variables are available. Constructed as a componentwise boosting method, it is able to perform variable selection with the complexity of the resulting estimator being determined by information criteria. The method is investigated in simulation studies both for cumulative and sequential models and is illustrated by using real datasets. The supplementary materials for the article are available online.     

误差分布未知下时空模型的自适应非参数估计

极大似然估计作为参数估计中较为有效的一种估计方法,在误差分布未知下无法进行,另一方面,时空数据经常含有奇异点或来自重尾分布,此时基于最小二乘的估计方法效果欠佳.考虑时空异质性和相关性,针对误差分布未知的时空模型,本文提出基于核密度估计的自适应非参数估计方法.在较弱的条件下证明了该估计量和已知误差分布下的局部极大似然估计...     

Modeling and Smoothing Unequally Spaced Sequence Data

The application of the continuous state space model to unequally spaced sequence data is discussed and illustrated. The continuous model implies a discrete model for the observed data. Practical expressions for relevant discrete model quantities are given. These quantities are required for the digital processing of the data and in particular for the application of the Kalman and smoothing filter and related calculations. Applications illustrate the procedures.     

动态变系数回归模型的识别与非参数GMM估计

主要研究关于面板数据的有限阶固定效应的动态变系数回归模型(简称FDVCM)的统计推断问题.基于B-样条函数和广义矩估计(简称GMM)方法,首先建立了未知系数函数的非参数GMM估计,并证明大样本情形下该估计达到最优非参数收敛速度且具有渐近正态性质.然而实际问题中模型的动态阶数完全未知,也可能存在其它冗余的回归变量,文中借助文[Fan J,Li R.Variable selection via penalized likelihood and its oracle properties.Journal of the American Statistical Association,2001,96(456):1348-1360]中的smoothly clipped absolute deviation(简称SCAD)惩罚函数同时识别真实的动态阶数和显著的外生回归变量.同时建立了压缩估计的Oracle性质,即所识别的模型与真实模型中的参数估计具有相同的渐近分布.最后,无论是数值试验还是实例数据分析都验证了本文方法的合理性和可行性.     

比例优势模型在多结局区间截断数据上的应用

探索比例优势模型在临床医学中常见的多结局区间截断数据中的应用.用条件的逻辑回归方法避免讨厌参数的估计,用牛顿-拉普森算法估计回归系数,用＂夹心方差＂估计量作为参数方差的估计.通过随机模型检验模型应用的有效性.     

部分线性模型中的非参数部分的线性性检验

In this paper,we propose the test statistic to check whether the nonpara- metric function in partially linear models is linear or not.We estimate the nonpara- metric function in alternative by using the local linear method,and then estimate the parameters by the two stage method.The test statistic under the null hypothesis is calculated,and it is shown to be asymptotically normal.     

检验两个部分线性模型中非参函数相等

We propose the test statistic to check whether the nonparametric func-tions in two partially linear models are equality or not in this paper. We estimate the nonparametric function both in null hypothesis and the alternative by the local linear method, where we ignore the parametric components, and then estimate the parameters by the two stage method. The test statistic is derived, and it is shown to be asymptotically normal under the null hypothesis.     

Semiparametric model for prediction of individual claim loss reserving

The estimation of loss reserves for incurred but not reported (IBNR) claims presents an important task for insurance companies to predict their liabilities. Conventional methods, such as ladder or separation methods based on aggregated or grouped claims of the so-called “run-off triangle”, have been illustrated to have some drawbacks. Recently, individual claim loss models have attracted a great deal of interest in actuarial literature, which can overcome the shortcomings of aggregated claim loss models. In this paper, we propose an alternative individual claim loss model, which has a semiparametric structure and can be used to fit flexibly the claim loss reserving. Local likelihood is employed to estimate the parametric and nonparametric components of the model, and their asymptotic properties are discussed. Then the prediction of the IBNR claim loss reserving is investigated. A simulation study is carried out to evaluate the performance of the proposed methods.     

Stochastic comparisons of lifetimes of series and parallel systems with dependent and heterogeneous components

This work considers stochastic comparisons of lifetimes of series and parallel systems with dependent and heterogeneous components having lifetimes following the proportional odds (PO) model. The joint distribution of component lifetimes is modeled by Archimedean survival copula. We discuss some potential applications of our findings in system reliability and actuarial science.     

完全与截尾样本时半参数回归模型估计的强相合性

对于固定设计下的半参数回归模型：ｙ_ｉ=ｘｉβ＋ｇ（ｔｉ）＋ｅｉ,ｉ＝１,…,ｎ．其中｛ｅｉ｝为独立随机误差序列（不必同分布）,且Ｅｅｉ＝０,Ｅｅｉ２＝σ_ｉ~２＞０．对完全和截尾样本,仿文献［２］给出了β、ｇ（·）的估计量,并证明了他们的强相合性．     

两个未知均值方差混合模型有限制下对数极大似然比的极限分布

本文讨论了检验样本是来自一个正态总体还是两个未知均值和方差的正态的混合分布,采用对数极大似然比的检验,如果不加限制,Ｈａｒｔｉｎｇａｎｍ曾指出不是寻找的、Ｘ＾２分布,我们在混合的中了一点后得到了其极限分布产工给出了分位点数值表。     

Efficient Non-Iterative and Nonparametric Estimation of Heterogeneity Variance for the Standardized Mortality Ratio

In this paper the situation of extra population heterogeneity in the standardized mortality ratio is discussed from the point-of-view of an analysis of variance. First, some simple non-iterative ways are provided to estimate the variance of the heterogeneity distribution without estimating the heterogeneity distribution itself. Next, a wider class of linear unbiased estimators is introduced and their properties investigated. Consistency is shown for a wide sub-class of estimators charactererized by the fact that the associated linear weights are within some positive, finite bounds. Furthermore, it is shown that an efficient estimator is often provided when the weights are proportional to the expected counts.     

On a family of risk measures based on proportional hazards models and tail probabilities

In this paper, we explore a class of tail variability measures based on distances among proportional hazards models. Tail versions of some well-known variability measures, such as the Gini mean difference, the Wang right tail deviation and the cumulative residual entropy are, up to a scale factor, in this class. These tail variability measures are combined with tail conditional expectation to generate premium principles that are especially useful to price heavy-tailed risks. We study their properties, including stochastic consistency and bounds, as well as the coherence of the associated premium principles.     

GLRT和LS_SVM应用于基因表达数据分类

为快速、准确地对基因芯片表达数据进行分类,提出了一种新型的基因芯片表达数据分类模型.该模型首先使用广义似然比检验(GLRT)有效鉴别出表达有显著性差异的基因.然后,将这些表达有显著性差异的基因用于最小二乘支持向量机(LS_SVM)的训练,从而建立了基于GLRT+LS_SVM的基因芯片表达数据分类模型.该模型在处理数据量大、维数高、样本量小、非线性等特点的基因芯片数据时有很大优势,可以广泛用于处理基因芯片数据.     

左截断双删失数据下可加可乘风险率模型的估计

在生存分析中,可加可乘风险率模型常用来研究协变量对初始事件和终止事件之间持续时间的影响效应。在本文中,我们考虑了在初始事件存在部分区间删失,同时终止事件存在左截断右删失的情形下,持续时间的可加可乘风险率模型的估计问题。我们提出了一个两阶段估计过程来估计模型的回归参数。并通过模拟分析验证了估计的大样本性质。最后利用该方法分析了恶性黑色素瘤手术治疗数据。     

Estimation of semi-varying coefficient model with surrogate data and validation sampling

In this paper, we investigate the estimation of semi-varying coefficient models when the nonlinear covariates are prone to measurement error. With the help of validation sampling, we propose two estimators of the parameter and the coefficient functions by combining dimension reduction and the profile likelihood methods without any error structure equation specification or error distribution assumption. We establish the asymptotic normality of proposed estimators for both the parametric and nonparametric parts and show that the proposed estimators achieves the best convergence rate. Data-driven bandwidth selection methods are also discussed. Simulations are conducted to evaluate the finite sample property of the estimation methods proposed.     

平衡设计多向分类多元重复测量模型的Wilks检验

对平衡设计多向分类多元重复测量模型,利用极大似然比方法,推导了对各单个固定效应分别进行检验的Wilks型检验规则.并推导了对多个固定效应进行同时检验的检验规则.推导了非中心分布的参数与原始参数和样本容量的关系.     

税企博弈模型的再研究

本文以文[1]基础,在更一般条件下提出了税企博弈模型,得到新的骗税罚款系数公式.从理论上证明了税务机关对企业抽样检查与全面检查具有相同的经济效果.最后,给出建模应注意的几个问题.     

具有一致相关的纵向数据模型中方差和相关系数的齐性检验

在纵向数据分析中, 模型方差的齐性是一个基本假定, 但是该假定未必正确. 林金官、韦博成^[1]讨论了具有AR(1)误差的非线性纵向数据模型中方差和相关系数的齐性检验. 本文对具有一致相关协方差结构的纵向数据模型, 研究了方差齐性和相关系数齐性的检验, 得到了检验的score统计量, 并应用于葡萄糖数据. 最后, 本文还给出了模拟结果.     

The Kullback-Leibler risk of the Stein estimator and the conditional MLE

The decomposition of the Kullback-Leibler risk of the maximum likelihood estimator (MLE) is discussed in relation to the Stein estimator and the conditional MLE. A notable correspondence between the decomposition in terms of the Stein estimator and that in terms of the conditional MLE is observed. This decomposition reflects that of the expected log-likelihood ratio. Accordingly, it is concluded that these modified estimators reduce the risk by reducing the expected log-likelihood ratio. The empirical Bayes method is discussed from this point of view.