Asymptotic expansions relating to discrimination based on two-step monotone missing samples

This paper proposes two asymptotic expansions relating to discrimination based on two-step monotone missing samples. These asymptotic expansions have been obtained by Okamoto (1963) and McLachlan (1973) for complete data under multivariate normality. This paper extends the results up to the terms of the first order in the case of two-step monotone missing samples, respectively. Especially, these asymptotic expansions play important roles in obtaining the asymptotic approximations for the probabilities of misclassification in discriminant analysis. The simulation studies have been also conducted in order to evaluate the accuracy of the approximation derived in this paper.     

Weighted empirical likelihood for quantile regression with non ignorable missing covariates

In this paper, we propose an empirical likelihood-based weighted estimator of regression parameter in quantile regression model with non ignorable missing covariates. The proposed estimator is computationally simple and achieves semiparametric efficiency if the probability of missingness on the fully observed variables is correctly specified. The efficiency gain of the proposed estimator over the complete-case-analysis estimator is quantified theoretically and illustrated via simulation and a real data application.     

Validity and efficiency in analyzing ordinal responses with missing observations

This article addresses issues in creating public-use data files in the presence of missing ordinal responses and subsequent statistical analyses of the dataset by users. The authors propose a fully efficient fractional imputation (FI) procedure for ordinal responses with missing observations. The proposed imputation strategy retrieves the missing values through the full conditional distribution of the response given the covariates and results in a single imputed data file that can be analyzed by different data users with different scientific objectives. Two most critical aspects of statistical analyses based on the imputed data set,  validity  and  efficiency, are examined through regression analysis involving the ordinal response and a selected set of covariates. It is shown through both theoretical development and simulation studies that, when the ordinal responses are missing at random, the proposed FI procedure leads to valid and highly efficient inferences as compared to existing methods. Variance estimation using the fractionally imputed data set is also discussed. The Canadian Journal of Statistics 48: 138–151; 2020 © 2019 Statistical Society of Canada     

Estimation of multi-state models with missing covariate values based on observed data likelihood

Abstract

Continuous-time multi-state models are commonly used to study diseases with multiple stages. Potential risk factors associated with the disease are added to the transition intensities of the model as covariates, but missing covariate measurements arise frequently in practice. We propose a likelihood-based method that deals efficiently with a missing covariate in these models. Our simulation study showed that the method performs well for both “missing completely at random” and “missing at random” mechanisms. We also applied our method to a real dataset, the Einstein Aging Study.     

Defining estimands using a mix of strategies to handle intercurrent events in clinical trials

Randomized controlled trials (RCTs) are the gold standard for evaluation of the efficacy and safety of investigational interventions. If every patient in an RCT were to adhere to the randomized treatment, one could simply analyze the complete data to infer the treatment effect. However, intercurrent events (ICEs) including the use of concomitant medication for unsatisfactory efficacy, treatment discontinuation due to adverse events, or lack of efficacy may lead to interventions that deviate from the original treatment assignment. Therefore, defining the appropriate estimand (the appropriate parameter to be estimated) based on the primary objective of the study is critical prior to determining the statistical analysis method and analyzing the data. The International Council for Harmonisation (ICH) E9 (R1), adopted on November 20, 2019, provided five strategies to define the estimand: treatment policy, hypothetical, composite variable, while on treatment, and principal stratum. In this article, we propose an estimand using a mix of strategies in handling ICEs. This estimand is an average of the “null” treatment difference for those with ICEs potentially related to safety and the treatment difference for the other patients if they would complete the assigned treatments. Two examples from clinical trials evaluating antidiabetes treatments are provided to illustrate the estimation of this proposed estimand and to compare it with the estimates for estimands using hypothetical and treatment policy strategies in handling ICEs.     

李绂佚文《汀州府志序》考释

乾隆《汀州府志》中收有一篇署名为李绂的序文,经寻绎辨析确定为新发现的李绂的佚文。该序文的发现,有助于更清晰了解李绂晚年的方志思想,对于深入了解李绂的生平经历,以及思想变化情况都有所裨益,值得进一步研究。     

论孝文化缺失对农村家庭养老的影响

孝文化是我国农村家庭养老的文化基础。受历史的和现实的各种因素的影响,目前农村孝文化呈缺失状态。孝文化的缺失,对农村家庭养老造成了重大的冲击。弘扬孝文化是解决农村家庭养老困境的关键。     

基于EM的朴素贝叶斯分类算法

将EM算法引入到朴素贝叶斯分类研究中,提出一种基于EM的朴素贝叶斯分类算法。首先用未缺失的数据属性的算术均数作为初始值,求得极大似然估计;其次迭代执行算法的E步和M步直至收敛,然后完成缺失数据的填补;最后根据朴素贝叶斯分类算法对数据进行分类。实验结果表明,与朴素贝叶斯分类算法相比,基于EM的朴素贝叶斯分类算法具有较高的分类准确率。     

缺失数据下广义线性模型的经验似然推断

利用经验似然方法,讨论缺失数据下广义线性模型中参数的置信域问题,得到了对数经验似然比统计量的渐近分布为标准卡方分布;给出参数的一些估计量及其渐近分布,利用数据模拟解释了所提出的方法。     

Random vs. systematic non-response in group-level research

This study investigates whether, in the context of group-level research, the effect of random non-response pattern, in biasing group-level scores, would be more salient than the effect of systematic non-response pattern with all other factors (i.e. group size, within-group variability, between-group variability and response rate) held constant. The findings of the Monte Carlo simulation study are counter-intuitive and reveal that the amount of error in the group-level variable is indeed the largest under the conditions of random non-response pattern.