Fisher’s observed information matrix for location and scale parameters under type i censoring

An expression for Fisher's observed information matrix is given under type I censoring for any location-scale distribution under mild requirements. It is illustrated on a data set which has been analyzed by several authors.     

功率器件有 JTE 结构的电场的数值分析与模拟

本文用 damped-Newton(DN)法求解由一般非线性方程获得的Poisson 方程。研究了阻尼因子λ对其收敛性和求解效率的影响。利用此方法计算了横向表面掺杂为 Gauss 分布的器件的归一化电场与掺杂浓度和掺杂层宽度的关系。     

Probabilistic Expert Systems for Forensic Inference from Genetic Markers

We present a number of real and fictitious examples in illustration of a new approach to analysing complex cases of forensic identification inference. This is effected by careful restructuring of the relevant pedigrees as a Probabilistic Expert System. Existing software can then be used to perform the required inferential calculations. Specific complications which are readily handled by this approach include missing data on one or more relevant individuals, and genetic mutation. The method is particularly valuable for disputed paternity cases, but applies also to certain criminal cases.     

Extending conditional likelihood in models for stratified binary data

The conditional likelihood is widely used in logistic regression models with stratified binary data. In particular, it leads to accurate inference for the parameters of interest, which are common to all strata, eliminating stratum-specific nuisance parameters. The modified profile likelihood is an accurate approximation to the conditional likelihood, but has the advantage of being available for general parametric models. Here, we propose the modified profile likelihood as an ideal extension of the conditional likelihood in generalized linear models for binary data, with generic link function. An important feature is that for the implementation we only need standard outputs of routines for generalized linear models. The accuracy of the method is supported by theoretical properties and is confirmed by simulation results.This research was supported by MIUR COFIN 2001-2003.     

Estimation and testing in generalized partial linear models—A comparative study

A particular semiparametric model of interest is the generalized partial linear model (GPLM) which extends the generalized linear model (GLM) by a nonparametric component.The paper reviews different estimation procedures based on kernel methods as well as test procedures on the correct specification of this model (vs. a parametric generalized linear model). Simulations and an application to a data set on East–West German migration illustrate similarities and dissimilarities of the estimators and test statistics.     

The Cox Proportional Hazards Model with a Partly Linear Relative Risk Function

The conventional Cox proportional hazards regression model contains a loglinear relative risk function, linking the covariate information to the hazard ratio with a finite number of parameters. A generalization, termed the partly linear Cox model, allows for both finite dimensional parameters and an infinite dimensional parameter in the relative risk function, providing a more robust specification of the relative risk function. In this work, a likelihood based inference procedure is developed for the finite dimensional parameters of the partly linear Cox model. To alleviate the problems associated with a likelihood approach in the presence of an infinite dimensional parameter, the relative risk is reparameterized such that the finite dimensional parameters of interest are orthogonal to the infinite dimensional parameter. Inference on the finite dimensional parameters is accomplished through maximization of the profile partial likelihood, profiling out the infinite dimensional nuisance parameter using a kernel function. The asymptotic distribution theory for the maximum profile partial likelihood estimate is established. It is determined that this estimate is asymptotically efficient; the orthogonal reparameterization enables employment of profile likelihood inference procedures without adjustment for estimation of the nuisance parameter. An example from a retrospective analysis in cancer demonstrates the methodology.     

A simultaneous variable selection methodology for linear mixed models

Selecting an appropriate structure for a linear mixed model serves as an appealing problem in a number of applications such as in the modelling of longitudinal or clustered data. In this paper, we propose a variable selection procedure for simultaneously selecting and estimating the fixed and random effects. More specifically, a profile log-likelihood function, along with an adaptive penalty, is utilized for sparse selection. The Newton-Raphson optimization algorithm is performed to complete the parameter estimation. By jointly selecting the fixed and random effects, the proposed approach increases selection accuracy compared with two-stage procedures, and the usage of the profile log-likelihood can improve computational efficiency in one-stage procedures. We prove that the proposed procedure enjoys the model selection consistency. A simulation study and a real data application are conducted for demonstrating the effectiveness of the proposed method.     

认知语法配价关系探究

从对应关系、侧重决定体、概念自主和依存关系及成分组合关系四个方面,尝试对认知语法配价关系理论的核心思想和实际运作进行初步剖析,从中发现对应关系在认知语法配价关系中具有恒定性;而侧重决定体、概念自主和依存关系及成分组合关系则复杂和多变。应根据构式在句中的地位、语义指向、语用信息及社会知识和经验,辩证地理解构式之间的内在联系及联结动因。     

全面薪酬满意度的分选效应研究

薪酬不仅具有激励效应,而且具有分选效应.基于316份有效样本的数据分析,发现全面薪酬满意度存在三个不同的员工子群体,即较不满意组、基本满意组和较满意组,且不同子群体的全面薪酬满意度对离职倾向的影响不同,从而基于个体差异视角揭示了全面薪酬满意度的分选效应.具体而言:在较不满意组,福利满意度、工作生活平衡满意度和绩效与认可满意度对离职倾向呈显著负向影响;在基本满意组,薪酬满意度、绩效与认可满意度和职业发展机会满意度对离职倾向呈显著负向影响;在较满意组,职业发展机会满意度对离职倾向呈显著负向影响.此外,工作价值观的个体差异可以部分解释全面薪酬满意度对离职倾向的不同影响.因此,在管理实践中,企业应针对不同的员工子群体定制差别化的全面薪酬体系,从而有助于企业形成稳定高效的员工队伍.