Journal ArticleDOI
Inference and missing data
TLDR
In this article, it was shown that ignoring the process that causes missing data when making sampling distribution inferences about the parameter of the data, θ, is generally appropriate if and only if the missing data are missing at random and the observed data are observed at random, and then such inferences are generally conditional on the observed pattern of missing data.Abstract:
Two results are presented concerning inference when data may be missing. First, ignoring the process that causes missing data when making sampling distribution inferences about the parameter of the data, θ, is generally appropriate if and only if the missing data are “missing at random” and the observed data are “observed at random,” and then such inferences are generally conditional on the observed pattern of missing data. Second, ignoring the process that causes missing data when making Bayesian inferences about θ is generally appropriate if and only if the missing data are missing at random and the parameter of the missing data is “independent” of θ. Examples and discussion indicating the implications of these results are included.read more
Citations
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Journal ArticleDOI
Assessing the Non-Random Sampling Effects of Subject Attrition in Longitudinal Research
Jodi S. Goodman,Terry C. Blum +1 more
TL;DR: In this paper, the potential effects of attrition in longitudinal research are addressed and a procedure for assessing its effects is recommended, using data collected from a random sample of employed adults in the US regarding job satisfaction, job characteristics, demographics and mood.
Journal ArticleDOI
The ECME algorithm: A simple extension of EM and ECM with faster monotone convergence
Chuanhai Liu,Donald B. Rubin +1 more
TL;DR: ECME as discussed by the authors is a generalization of the ECM algorithm, which is itself an extension of the EM algorithm (Dempster, Laird & Rubin, 1977), which can be obtained by replacing some CM-steps of ECM, which maximise the constrained expected complete-data loglikelihood function, with steps that maximize the correspondingly constrained actual likelihood function.
Book ChapterDOI
Chapter 70 Econometric Evaluation of Social Programs, Part I: Causal Models, Structural Models and Econometric Policy Evaluation ⁎
TL;DR: In this article, the authors developed a general evaluation framework that addresses well-posed economic questions and analyzes agent choice rules and subjective evaluations of outcomes as well as the standard objective evaluation of outcomes.
Multiple Imputation for Missing Data: Concepts and New Development
TL;DR: This paper reviews methods for analyzing missing data, including basic concepts and applications of multiple imputation techniques, and presents new SAS R procedures for creating multiple imputations for incomplete multivariate data and for analyzing results from multiply imputed data sets.
Journal ArticleDOI
Ignorability and Coarse Data
TL;DR: In this article, the authors present a general statistical model for data coarsening, which includes as special cases rounded, heaped, censored, partially categorized and missing data, and establish simple conditions under which the possible stochastic nature of the coarsing mechanism can be ignored when drawing Bayesian and likelihood inferences and thus the data can be validly treated as grouped data.
References
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Book
Bayesian inference in statistical analysis
George E. P. Box,George C. Tiao +1 more
TL;DR: In this article, the effect of non-normality on inference about a population mean with generalizations was investigated. But the authors focused on the effect on the mean with information from more than one source.
Journal ArticleDOI
Bayesian Inference in Statistical Analysis
Journal ArticleDOI
Maximum Likelihood Estimates for a Multivariate Normal Distribution when Some Observations are Missing
TL;DR: In this paper, the authors give an approach to derive maximum likelihood estimates of parameters of multivariate normal distributions in cases where some observations are missing (Edgett [2] and Lord [3], [4]).
Journal ArticleDOI
Missing Observations in Multivariate Statistics I. Review of the Literature
TL;DR: In this paper, a review of the literature on the problem of handling multivariate data with observations missing on some or all of the variables under study is presented, where the authors examine the ways that statisticians have devised to estimate means, variances, correlations and linear regression functions.