数量特征随机化回答模型中的比估计和回归估计方法

本文研究了含辅助信息的数量特征随机化调查的理论与方法.建立和发展了数量特征随机化调查的比估计和回归估计理论与方法.对于给定的人群有限总体,构造了敏感特征指标总体均值的比估计量和回归估计量,证明了本文提出的方法优于Eichhorn BH,HayreLS（1983）和Gupta et al.（2002）提出的方法.Monte-Carlo数值模拟验证了提出方法的有效性.     

Comparing Fits of Latent Trait and Latent Class Models Applied to Sparse Binary Data: An Illustration with Human Resource Management Data

This paper addresses the problem of comparing the fit of latent class and latent trait models when the indicators are binary and the contingency table is sparse. This problem is common in the analysis of data from large surveys, where many items are associated with an unobservable variable. A study of human resource data illustrates: (1) how the usual goodness-of-fit tests, model selection and cross-validation criteria can be inconclusive; (2) how model selection and evaluation procedures from time series and economic forecasting can be applied to extend residual analysis in this context.     

季节性商品的订购策略

讨论了在一个时期内商品的订购价格有折扣，而且该商品的需求量是离散型随机变量的订购问题，得出了使利润最大化的最佳订购量的计算方法。     

Rhotacization and the ‘Beijing Smooth Operator’: The social meaning of a linguistic variable1

Recent sociolinguistic studies on style have focused much attention on the construction of social meaning in situated discursive practices. Despite a general recognition that the linguistic resources used are often already imbued with social meanings, little research has been done on what these meanings may be. Focusing on rhotacization, a sociolinguistic variable in Beijing Mandarin, this article explores its imbued social meanings and sociocultural associations. I demonstrate that rhotacization takes on semiotic saliency through co‐occurrence with key Beijing cultural terms and frequent use in written representations of authentic Beijing‐ness. Furthermore, this feature is associated with the ‘Beijing Smooth Operator,’ a salient male local character type, and is ideologically construed as reflecting its characterological attributes. The findings of this study shed light on the meaning potential of a linguistic variable, rhotacization in this case, which can enhance understanding of the possibilities and constraints for its use and meaning in new contexts.     

A Bayesian model for local smoothing in kernel density estimation

A new procedure is proposed for deriving variable bandwidths in univariate kernel density estimation, based upon likelihood cross-validation and an analysis of a Bayesian graphical model. The procedure admits bandwidth selection which is flexible in terms of the amount of smoothing required. In addition, the basic model can be extended to incorporate local smoothing of the density estimate. The method is shown to perform well in both theoretical and practical situations, and we compare our method with those of Abramson (The Annals of Statistics 10: 1217–1223) and Sain and Scott (Journal of the American Statistical Association 91: 1525–1534). In particular, we note that in certain cases, the Sain and Scott method performs poorly even with relatively large sample sizes.We compare various bandwidth selection methods using standard mean integrated square error criteria to assess the quality of the density estimates. We study situations where the underlying density is assumed both known and unknown, and note that in practice, our method performs well when sample sizes are small. In addition, we also apply the methods to real data, and again we believe our methods perform at least as well as existing methods.     

Manipulating and summarizing posterior simulations using random variable objects

Practical Bayesian data analysis involves manipulating and summarizing simulations from the posterior distribution of the unknown parameters. By manipulation we mean computing posterior distributions of functions of the unknowns, and generating posterior predictive distributions. The results need to be summarized both numerically and graphically. We introduce, and implement in R, an object-oriented programming paradigm based on a random variable object type that is implicitly represented by simulations. This makes it possible to define vector and array objects that may contain both random and deterministic quantities, and syntax rules that allow to treat these objects like any numeric vectors or arrays, providing a solution to various problems encountered in Bayesian computing involving posterior simulations. We illustrate the use of this new programming environment with examples of Bayesian computing, demonstrating missing-value imputation, nonlinear summary of regression predictions, and posterior predictive checking.     

Bias in meta-analysis due to outcome variable selection within studies

Although bias in meta-analysis arising from selective publication has been studied, within-study selection has received little attention. Chronic diseases often have several possible outcome variables. Methods based on the size of the effect allow results from studies with different outcomes to be combined. However, the possibility of selective reporting of outcomes must be considered. The effect of selective reporting on estimates of the size of the effect and significance levels is presented, and sensitivity analyses are suggested. Substantial publication bias could arise from multiple testing of outcomes in a study, followed by selective reporting. Two meta-analyses, on anthelminth therapy and a treatment for incontinence, are reassessed allowing for within-study selection, as it is clear that more outcomes had been measured than were reported. The sensitivity analyses show that the robustness of the anthelminth results is dependent on what assumption one makes about the reporting strategy for the largest trial. The possible influence of correlation between within-child measurements was such that the conclusions could easily be reversed. The effect of a mild assumption on within-trial selection alone could alter general recommendations about the treatment for incontinence.     

Inconsistency of the MLE for the Joint Distribution of Interval-Censored Survival Times and Continuous Marks

Abstract.  This paper considers the non-parametric maximum likelihood estimator (MLE) for the joint distribution function of an interval-censored survival time and a continuous mark variable. We provide a new explicit formula for the MLE in this problem. We use this formula and the mark-specific cumulative hazard function of Huang & Louis (1998) to obtain the almost sure limit of the MLE. This result leads to necessary and sufficient conditions for consistency of the MLE, which imply that the MLE is inconsistent in general. We show that the inconsistency can be repaired by discretizing the marks. Our theoretical results are supported by simulations.     

Ranked set sampling for ordered categorical variables

The authors show how ranked set sampling, both balanced and unbalanced, can be extended to ordered categorical variables with the goal of estimating the probabilities of all categories. They use ordinal logistic regression to aid in the ranking of the ordinal variable of interest. They also propose an optimal allocation scheme and methods for implementing it under either perfect or imperfect rankings. Results from a simulation study using data from the third National Health and Nutrition Examination Survey indicate that the use of ordinal logistic regression in ranking leads to substantial gains in precision for estimation of cell probabilities.     

复变函数与积分变换课堂教学方法改革研究

主要介绍了复变函数与积分变换课堂教学改革的四方面的内容:合适的教材,学生的兴趣与能力的培养.理论背景及思想方法的重要性,双向交流和多种教学手段的运用.