Testing equality of two normal covariance matrices with monotone missing data

Abstract

The problem of testing equality of two multivariate normal covariance matrices is considered. Assuming that the incomplete data are of monotone pattern, a quantity similar to the Likelihood Ratio Test Statistic is proposed. A satisfactory approximation to the distribution of the quantity is derived. Hypothesis testing based on the approximate distribution is outlined. The merits of the test are investigated using Monte Carlo simulation. Monte Carlo studies indicate that the test is very satisfactory even for moderately small samples. The proposed methods are illustrated using an example.     

指数级数的增长性和值分布

本文研究了随机狄里克莱级数 在随机变量序列{Xn}独立（可不同分布）以及满足等条件时的增长性以及值分布,得到了一些新的结果.     

建国初期有组织犯罪惩治策略研究--一种犯罪学的分析路径

有组织犯罪日益猖獗已成为国际关注的社会问题。我国 97刑法增设了黑社会性质组织罪条款 ,作出了立法反应。对这种特殊形态的犯罪如何有效地惩治与防范 ,已是急迫而艰巨的任务 ,也是犯罪学极富价值的课题。从建国初期新生政权惩治有组织犯罪的策略其效用来看 ,对当代中国惩治有组织犯罪实践有三个方面的启示 :国家与社会双本位控制 ;打击与促进社会整合相结合 ;宽严相济的灵活的刑事政策有利于分化瓦解有组织犯罪。     

张家山汉简所见“妻悍”“妻殴夫”等事论说

江陵张家山汉简中有关于“妻悍”、“妻殴夫”简文,可以体现当时社会的性别关系。有 关法律,在后代有所变化。讨论有关现象,对于认识汉代文化的特征,有积极的意义。     

浅谈我国古代文艺作品中的影射技法

影射 ,作为一种文学技巧 ,最先出现在《诗经》 ,日后由于意识形态变化的原因 ,并在儒家文学观的指引下 ,逐渐推广到辞赋、散文、小说、词曲和戏剧所有文学领域 ,表现形式也由单一的比喻象征发展到多种类型 ,形成姿态横生、形式大备、羽翼丰满的艺术手段     

论救助款项的含义

对海上救助中有关救助款项的含义进行了分析和研究 ,同时分析了我国《海商法》中有关条文对救助款项的规定。     

公路桥头跳车成因分析及技术措施

桥头跳车一直是道路桥梁工程界广为关注而又一直尚未解决的重大技术难题，本文针对桥头跳车问题进行了成因分析，介绍了其危害性及可行的几种防治措施，为进一步解决这一问题提供一些可实施的方法。     

Type I error rates from likelihood‐based repeated measures analyses of incomplete longitudinal data

The last observation carried forward (LOCF) approach is commonly utilized to handle missing values in the primary analysis of clinical trials. However, recent evidence suggests that likelihood‐based analyses developed under the missing at random (MAR) framework are sensible alternatives. The objective of this study was to assess the Type I error rates from a likelihood‐based MAR approach – mixed‐model repeated measures (MMRM) – compared with LOCF when estimating treatment contrasts for mean change from baseline to endpoint (Δ). Data emulating neuropsychiatric clinical trials were simulated in a 4 × 4 factorial arrangement of scenarios, using four patterns of mean changes over time and four strategies for deleting data to generate subject dropout via an MAR mechanism. In data with no dropout, estimates of Δ and SE_Δ from MMRM and LOCF were identical. In data with dropout, the Type I error rates (averaged across all scenarios) for MMRM and LOCF were 5.49% and 16.76%, respectively. In 11 of the 16 scenarios, the Type I error rate from MMRM was at least 1.00% closer to the expected rate of 5.00% than the corresponding rate from LOCF. In no scenario did LOCF yield a Type I error rate that was at least 1.00% closer to the expected rate than the corresponding rate from MMRM. The average estimate of SE_Δ from MMRM was greater in data with dropout than in complete data, whereas the average estimate of SE_Δ from LOCF was smaller in data with dropout than in complete data, suggesting that standard errors from MMRM better reflected the uncertainty in the data. The results from this investigation support those from previous studies, which found that MMRM provided reasonable control of Type I error even in the presence of MNAR missingness. No universally best approach to analysis of longitudinal data exists. However, likelihood‐based MAR approaches have been shown to perform well in a variety of situations and are a sensible alternative to the LOCF approach. MNAR methods can be used within a sensitivity analysis framework to test the potential presence and impact of MNAR data, thereby assessing robustness of results from an MAR method. Copyright © 2004 John Wiley & Sons, Ltd.     

A Bayesian Adjustment for Covariate Misclassification with Correlated Binary Outcome Data

Estimated associations between an outcome variable and misclassified covariates tend to be biased when the methods of estimation that ignore the classification error are applied. Available methods to account for misclassification often require the use of a validation sample (i.e. a gold standard). In practice, however, such a gold standard may be unavailable or impractical. We propose a Bayesian approach to adjust for misclassification in a binary covariate in the random effect logistic model when a gold standard is not available. This Markov Chain Monte Carlo (MCMC) approach uses two imperfect measures of a dichotomous exposure under the assumptions of conditional independence and non-differential misclassification. A simulated numerical example and a real clinical example are given to illustrate the proposed approach. Our results suggest that the estimated log odds of inpatient care and the corresponding standard deviation are much larger in our proposed method compared with the models ignoring misclassification. Ignoring misclassification produces downwardly biased estimates and underestimate uncertainty.