Bayesian semiparametric inference for the accelerated failure-time model

Bayesian semiparametric inference is considered for a loglinear model. This model consists of a parametric component for the regression coefficients and a nonparametric component for the unknown error distribution. Bayesian analysis is studied for the case of a parametric prior on the regression coefficients and a mixture-of-Dirichlet-processes prior on the unknown error distribution. A Markov-chain Monte Carlo (MCMC) method is developed to compute the features of the posterior distribution. A model selection method for obtaining a more parsimonious set of predictors is studied. The method adds indicator variables to the regression equation. The set of indicator variables represents all the possible subsets to be considered. A MCMC method is developed to search stochastically for the best subset. These procedures are applied to two examples, one with censored data.     

Variable selection in partial linear regression with functional covariate

The problem of variable selection is considered in high-dimensional partial linear regression under some model allowing for possibly functional variable. The procedure studied is that of nonconcave-penalized least squares. It is shown the existence of a √n/s_n-consistent estimator for the vector of p_n linear parameters in the model, even when p_n tends to ∞ as the sample size n increases (s_n denotes the number of influential variables). An oracle property is also obtained for the variable selection method, and the nonparametric rate of convergence is stated for the estimator of the nonlinear functional component of the model. Finally, a simulation study illustrates the finite sample size performance of our procedure.     

Likelihood approach for evaluating bioequivalence of highly variable drugs

Bioequivalence (BE) is required for approving a generic drug. The two one‐sided tests procedure (TOST, or the 90% confidence interval approach) has been used as the mainstream methodology to test average BE (ABE) on pharmacokinetic parameters such as the area under the blood concentration‐time curve and the peak concentration. However, for highly variable drugs (%CV > 30%), it is difficult to demonstrate ABE in a standard cross‐over study with the typical number of subjects using the TOST because of lack of power. Recently, the US Food and Drug Administration and the European Medicines Agency recommended similar but not identical reference‐scaled average BE (RSABE) approaches to address this issue. Although the power is improved, the new approaches may not guarantee a high level of confidence for the true difference between two drugs at the ABE boundaries. It is also difficult for these approaches to address the issues of population BE (PBE) and individual BE (IBE). We advocate the use of a likelihood approach for representing and interpreting BE data as evidence. Using example data from a full replicate 2 × 4 cross‐over study, we demonstrate how to present evidence using the profile likelihoods for the mean difference and standard deviation ratios of the two drugs for the pharmacokinetic parameters. With this approach, we present evidence for PBE and IBE as well as ABE within a unified framework. Our simulations show that the operating characteristics of the proposed likelihood approach are comparable with the RSABE approaches when the same criteria are applied. Copyright © 2014 John Wiley & Sons, Ltd.     

一致性分数阶优化灰色模型及其应用

传统的分数阶灰色预测模型在时间序列预测中具有较好的适应性和预测的有效性,但其累加和差分计算式比较复杂。一致性分数阶累加相对于一般的分数阶累加,形式更简单,更便于计算和理论推导。为了提高模型的适应性和预测能力,文章在CFGM(1,1)白化方程中引入一个新的可变系数,扩大了原有白化方程的适用范围,并在此基础上构建了一致性分数阶优化灰色模型,即CFOGM(1,1)模型。最优一致性分数阶阶数和可变系数通过PSO算法最小化平均相对误差获得。将构建的模型运用到两个实例中并与其他经典的灰色预测模型进行对比,结果表明所提出的模型具有较高的拟合和预测精度。     

政策变量与中国社会分层结构的调整

当代中国的改革是不断用政策来修正制度或体制。改革就是对政策、法规和体制的调整,这种调整最终体现为人们利益关系的变化,用分层理论表述则最终体现为调整社会分层结构。当代中国的政策调整自改革开放以来分为两个重要时期,一个是1992年邓小平南巡讲话以后;一个是2002年中共十六大以后。当前政策调整的趋势,明显地增加了公平方面的政策变量,试图使社会资源的配置向普惠的方向调整。但从2007年算起,尚需要三十三年才能将中国调整成一个中等收入层为主体的社会分层结构。所以,对社会结构的调整,需要做长时期的努力。     

基于变权综合的多指标席位分配模型

从指标的综合加权入手,对经典席位分配模型进行了推广,建立了多指标席位分配模型,以拓宽这种模型的应用范围.采用指标标准化,变权综合的方法对各种指标进行加权,克服了传统加权方法的不公平问题。最后以学校科研项目的分配说明多指标席位分配方法的更加合理,更加公平和广泛。     

The Estimation of Dynamic Bivariate Mixture Models

Watanabe estimated the dynamic bivariate mixture models introduced by Tauchen and Pitts and modified by Andersen using a Bayesian method via Markov chain Monte Carlo techniques. Based on a maximum likelihood method via efficient importance sampling, Liesenfeld and Richard obtained estimates that are significantly different from those of Watanabe. This note corrects the error in the multimove sampler used by Watanabe and reproduces all analyses in the work of Watanabe using a corrected multimove sampler. The estimates using the correct multimove sampler are found to be close to those obtained by Liesenfeld and Richard.     

The general class of chain estimators for the ratio of two means using double sampling

A wider class of chain based estimators for the ratio of two means of a finite population has been proposed by using two auxiliary variables. Singh et al.(1994) is a particular case of this class.