A class of semiparametric transormation frailty models with application to estimating treatment effects and heterogenity in aids clinical traials

Muitivariate failure time data are common in medical research; com¬monly used statistical models for such correlated failure-time data include frailty and marginal models. Both types of models most often assume pro¬portional hazards (Cox, 1972); but the Cox model may not fit the data well This article presents a class of linear transformation frailty models that in¬cludes, as a special case, the proportional hazards model with frailty. We then propose approximate procedures to derive the best linear unbiased es¬timates and predictors of the regression parameters and frailties. We apply the proposed methods to analyze results of a clinical trial of different dose levels of didansine (ddl) among HIV-infected patients who were intolerant of zidovudine (ZDV). These methods yield estimates of treatment effects and of frailties corresponding to patient groups defined by clinical history prior to entry into the trial.     

Recursive location and scale estimators

It is shown that a recursive estimator with the same asymptotic properties as the median has convergence properties in finite samples which depend heavily on the scale of the data. A simple modification which adjusts for the scale is suggested and its application illustrated on simulated data. The modified estimator has much improved properties which are similar to those of the sample (non-recursive) median.     

Tukey-Type Distributions in the Context of Financial Data

Abstract

In one-parameter (θ) families, we were not aware of explicit hypothesis testing scenarios where maximal invariant statistics failed to distinguish the models. We start with a concrete example (Sec. 2.2) to highlight such a hypothesis testing problem involving markedly different models. In this problem, because of the absence of a nontrivial uniformly most powerful invariant (UMPI) test, we briefly suggest two approaches to test the hypothesis. The first resolution (Sec. 3.1) is frequentist in nature. It utilizes a weight function on the parameter space and compares “average” distributions obtained under the null and alternative models in the sense of Wald (1947 Wald , A. ( 1947 ). Sequential Analysis . New York : Wiley . [Google Scholar] 1950 Wald , A. ( 1950 ). Statistical Decision Functions . New York : Wiley . [Google Scholar]). In contrast, a fully Bayesian resolution (Sec. 3.2) is also included. The note ends with a series of other interesting examples involving one-parameter families where maximal invariant statistics fail to distinguish the hypothesized models. The examples include easy-to-construct families of probability models involving only a single location or scale parameter θ.     

Relative Curvature Measure for Heteroscedastic or Non Normal Nonlinear Regression

The Bates–Watts relative curvature measure can assess the validity of the linearized approximation in nonlinear regression models. However, it is developed based on an ordinary nonlinear regression in which the observation is assumed to be homoscedastically and normally distributed. In this article, we extend the original Bates–Watts relative curvature measure to one that can be applicable to nonlinear regression with heteroscedastic or non normal data, based on the transformation-both-sides (TBS) approach. In pharmacokinetic models, a diagnostic use of their measures is illustrated. By means of a simulation experiment, the performance of the relative curvature measure for the TBS approach is evaluated.     

Consistency of the bootstrap for the ransformed two-sample t-test

A method of bootstrapping the two-sample t-test after a Box-Cox transformation is proposed. The procedure is shown to be consistent and asymptotically as efficient as the non-bootstrapped Box-Cox t-test. Because the bootstrap samples are drawn without the assumption of the same distributional shapes,the procedure may be more robust against violation of this assumption. Simulation results support this conjecture.     

Exclusion restrictions in instrumental variables equations

In this paper we consider two-stage estimators of parameters of a structural equation in a model with recursive exclusion restrictions on the instrumental variables equations. The estimations considered are simple OLS and GLS estimators after substitution of estimates of the systematic part of the IV equations for the endogenous variables. It is known in the literature that neither imposing the restrictions in the first stage nor ignoring them will in general be more efficient than the alternative. We introduce a class of mixed instrumental variables estimators (MIV) with these possibilities as special cases which yields an estimator which is not only more efficient than the two stage estimators considered in the literature but as efficient as an efficient system estimator like 3SLS.     

Bias Reduction through First-order Mean Correction, Bootstrapping and Recursive Mean Adjustment

Standard methods of estimation for autoregressive models are known to be biased in finite samples, which has implications for estimation, hypothesis testing, confidence interval construction and forecasting. Three methods of bias reduction are considered here: first-order bias correction, FOBC, where the total bias is approximated by the O(T^-1) bias; bootstrapping; and recursive mean adjustment, RMA. In addition, we show how first-order bias correction is related to linear bias correction. The practically important case where the AR model includes an unknown linear trend is considered in detail. The fidelity of nominal to actual coverage of confidence intervals is also assessed. A simulation study covers the AR(1) model and a number of extensions based on the empirical AR(p) models fitted by Nelson & Plosser (1982). Overall, which method dominates depends on the criterion adopted: bootstrapping tends to be the best at reducing bias, recursive mean adjustment is best at reducing mean squared error, whilst FOBC does particularly well in maintaining the fidelity of confidence intervals.     

几种传输线的场分布

求开放平板线、外正三角形內圆同轴线、外方內圆同轴线、外正六边形內圆同轴线的特性阻抗而建立的精确保角变换的基础上,本文进一步讨论了显式逆变换函数,从而求得了这几种传输线的场分布的闭合表达式。由于所用保角变换的精确性,所得的显式场分布在内导体和外导体的内接圆尺寸之比小于0.5in。基本上是准确的,在这个尺寸比稍大些时也可作为参考。这些场分布对于了解传输线的功率容量,计算衰减常数,考虑功率耦合,设计有关的有源器件等都是必不可少的。文中获得的几种传输线的场分布的显著特点是计算方便,在一般实用范围內有足够的精度。     

论我国高等教育运行机制的过渡与转换

本文认为,我国现阶段的高教运行机制正处于过渡与转换的初始阶段,机制转换的完成需要经历一个较长的过渡时期.为了顺利平稳地完成机制转换,必须构建相应的过渡性目标模式,以此作为从适应产品经济到适应有计划的商品经济的高教运行机制模式转换的中介。因此,今后—个时期高教改革的基本思路就不应只朝一个方向努力,而应以国家职能转变为契机,在国家、社会和学校三方面同时展开,并辅之于教育市场的不断培育和竞争机制的逐步加强。