A Class of Permutation Tests for the Equality of Two Marginal Survival Functions Using Paired Censored Data

We studied several test statistics for testing the equality of marginal survival functions of paired censored data. The null distribution of the test statistics was approximated by permutation. These tests do not require explicit modeling or estimation of the within-pair correlation, accommodate both paired data and singletons, and the computation is straightforward with most statistical software. Numerical studies showed that these tests have competitive size and power performance. One test statistic has higher power than previously published test statistics when the two survival functions under comparison cross. We illustrate use of these tests in a propensity score matched dataset.     

An Overview of Asymptotic Properties of Estimators in Truncated Distributions

In this article, a method is proposed to get the limiting distributions and asymptotic properties of estimators based on the minimum and/or maximum of a given srs of a truncated distribution. Following a common outline, a review is carried out by considering different kinds of truncated distributions, some new results are also developed.     

Maximum entropy in the mean methods in propensity score matching for interval and noisy data

Abstract

In this paper, we propose maximum entropy in the mean methods for propensity score matching classification problems. We provide a new methodological approach and estimation algorithms to handle explicitly cases when data is available: (i) in interval form; (ii) with bounded measurement or observational errors; or (iii) both as intervals and with bounded errors. We show that entropy in the mean methods for these three cases generally outperform benchmark error-free approaches.     

Modified mantel-haenszel procedures for matched pairs

In the prospective study of a finely stratified population, one individual from each stratum is chosen at random for the “treatment” group and one for the “non-treatment” group. For each individual the probability of failure is a logistic function of parameters designating the stratum, the treatment and a covariate. Uniformly most powerful unbiased tests for the treatment effect are given. These tests are generally cumbersome but, if the covariate is dichotomous, the tests and confidence intervals are simple. Readily usable (but non-optimal) tests are also proposed for poly-tomous covariates and factorial designs. These are then adapted to retrospective studies (in which one “success” and one “failure” per stratum are sampled). Tests for retrospective studies with a continuous “treatment” score are also proposed.     

On the Performance of Sequential Regression Multiple Imputation Methods with Non Normal Error Distributions

Sequential regression multiple imputation has emerged as a popular approach for handling incomplete data with complex features. In this approach, imputations for each missing variable are produced based on a regression model using other variables as predictors in a cyclic manner. Normality assumption is frequently imposed for the error distributions in the conditional regression models for continuous variables, despite that it rarely holds in real scenarios. We use a simulation study to investigate the performance of several sequential regression imputation methods when the error distribution is flat or heavy tailed. The methods evaluated include the sequential normal imputation and its several extensions which adjust for non normal error terms. The results show that all methods perform well for estimating the marginal mean and proportion, as well as the regression coefficient when the error distribution is flat or moderately heavy tailed. When the error distribution is strongly heavy tailed, all methods retain their good performances for the mean and the adjusted methods have robust performances for the proportion; but all methods can have poor performances for the regression coefficient because they cannot accommodate the extreme values well. We caution against the mechanical use of sequential regression imputation without model checking and diagnostics.     

A matching prior based on the modified profile likelihood in a generalized Weibull stress‐strength model

Bayesian inference of a generalized Weibull stress‐strength model (SSM) with more than one strength component is considered. For this problem, properly assigning priors for the reliabilities is challenging due to the presence of nuisance parameters. Matching priors, which are priors matching the posterior probabilities of certain regions with their frequentist coverage probabilities, are commonly used but difficult to derive in this problem. Instead, we apply an alternative method and derive a matching prior based on a modification of the profile likelihood. Simulation studies show that this proposed prior performs well in terms of frequentist coverage and estimation even when the sample sizes are minimal. The prior is applied to two real datasets. The Canadian Journal of Statistics 41: 83–97; 2013 © 2012 Statistical Society of Canada     

Some Results on Comparing the Quantiles of Dependent Groups

Recently, Lombard derived an extension of the Doksum–Sievers shift function to dependent groups. This article suggests using a particular numerical method for determining the critical value, reports on the ability of the method to control the probability of a Type I error when sample sizes are small, and it provides comparisons with methods aimed at comparing deciles. It is found that for continuous distributions, Lombard's method performs well and in particular has high power relative to the other two methods considered. But when tied values can occur, now it can have relatively poor power; a method based on the Harrell-Davis estimator is found to give more satisfactory results.     

A class of fixed-width confidence intervals for a ranked normal mean

Suppose that we are given k(≥ 2) independent and normally distributed populations π₁, …, π_k where π_i has unknown mean μ_i and unknown variance σ² _i (i = 1, …, k). Let μ_[i] (i = 1, …, k) denote the i^th smallest one of μ₁, …, μ_k. A two-stage procedure is used to construct lower and upper confidence intervals for μ_[i] and then use these to obtain a class of two-sided confidence intervals on μ_[i] with fixed width. For i = k, the interval given by Chen and Dudewicz (1976) is a special case. Comparison is made between the class of two-sided intervals and a symmetric interval proposed by Chen and Dudewicz (1976) for the largest mean, and it is found that for large values of k at least one of the former intervals requires a smaller total sample size. The tables needed to actually apply the procedure are provided.     

基于消息"指纹"的等同测试方法研究

研究通信复杂性中的等同测试,提出基于消息“指纹”的概率测试方法,推导“错误匹配”概率计算公式;论述引入“随机性”不仅保证极低的通信复杂性,并使出现“错误匹配”的概率可忽略。     

基于域映射模式的信息化平台匹配理论研究

从企业信息化应用角度出发,应用域映射理论和方法对管理系统与信息系统关系进行分析,在深入研究企业建模和信息系统开发建模的基础上,提出管理域和信息域的互映射机制、过程和方式,进而提出信息化平台匹配理论,并对映射一致性的测度方法进行研究,以此作为信息化产品选型过程的理论和方法论指导。