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调查中高影响群组的降权处理AI 解读

Certain clusters may be extremely influential on survey estimates and consequently contribute disproportionately to their variance. We propose a general approach to estimation that downweights highly influential clusters, with the amount of downweighting based on M-estimation applied to the empirical influence of the clusters. The method is motivated by a problem in census coverage estimation, and we illustrate it by using data from the 1990 Post Enumeration Survey (PES). In…

使用条件均值插补数据的统计推断AI 解读

In this article we present analytic techniques for inference from a dataset in which missing values have been replaced by predictive means derived from an imputation model. The derivations are based on asymptotic expansions of point estimators and their associated variance estimators, and the resulting formulas can be thought of as first-order approximations to standard multiple-imputation procedures with an infinite number of imputations for the missing values. Our method,…

基于条件均值填补数据的统计推断AI 解读

In this article we present analytic techniques for inference from a dataset in which missing values have been replaced by predictive means derived from an imputation model. The derivations are based on asymptotic expansions of point estimators and their associated variance estimators, and the resulting formulas can be thought of as first-order approximations to standard multiple-imputation procedures with an infinite number of imputations for the missing values. Our method,…

含间歇观测时变二元协变量的删失生存数据分析AI 解读

In survival analyses that include a time-dependent covariate, the standard partial likelihood approach is often complicated by the fact that the necessary observations of the time-dependent covariate are not always available for the subjects in each risk set. In this article, we consider the situation of a binary time-dependent covariate. We specify a longitudinal model for the binary covariate with missing observations and a proportional hazards model for survival time…

用贝叶斯逻辑回归多重插补人口普查行业与职业编码AI 解读

We describe methods used to create a new Census data base that can be used to study comparability of industry and occupation classification systems. This project represents the most extensive application of multiple imputation to date, and the modeling effort was considerable as well—hundreds of logistic regressions were estimated. One goal of this article is to summarize the strategies used in the project so that researchers can better understand how the new data bases were…

多重插补的渐近结果AI 解读

Imputation and multiple-imputation procedures have been used in practice to handle the problem of ignorable nonresponse in sample surveys. We examine the large-sample properties of these procedures where covariates are available for the case when the complete-data analysis is based on least squares. The results provide a formal justification for the inference procedures discussed by Rubin and Schenker for the location problem and suggest new procedures for the regression…

可忽略无响应下简单随机样本的多重插补区间估计AI 解读

Several multiple imputation techniques are described for simple random samples with ignorable nonresponse on a scalar outcome variable. The methods are compared using both analytic and Monte Carlo results concerning coverages of the resulting intervals for the population mean. Using m = 2 imputations per missing value gives accurate coverages in common cases and is clearly superior to single imputation (m = 1) in all cases. The performances of the methods for various m can…

对 Bootstrap 置信区间的疑虑AI 解读

The percentile method and bias-corrected percentile method of Efron (1981, 1982) are discussed. When these methods are used to construct nonparametric confidence intervals for the variance of a normal distribution, the coverage probabilities are substantially below the nominal level for small to moderate samples. This is due to the inapplicability of assumptions underlying the methods. These assumptions are difficult or impossible to check in the complicated situations for…