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
Multiple imputation of missing values: update
TL;DR: A substantial update to mvis is described, which brings it more closely in line with the feature set of S. M. van Buuren and C. G. Oudshoorn's implementation of the MICE system in R and S-PLUS.
Journal ArticleDOI
Application of Random-Effects Pattern-Mixture Models for Missing Data in Longitudinal Studies
Donald Hedeker,Robert D. Gibbons +1 more
TL;DR: Use of random-effects pattern-mixture models to further handle and describe the influence of missing data in longitudinal studies is described.
Journal ArticleDOI
Multiple-Imputation Inferences with Uncongenial Sources of Input
TL;DR: When it is desirable to conduct inferences under models for nonresponse other than the original imputation model, a possible alternative to recreating imputation models is to incorporate appropriate importance weights into the standard combining rules.
Journal ArticleDOI
Cognitive Behavioral Therapy for Posttraumatic Stress Disorder in Women: A Randomized Controlled Trial
Paula P. Schnurr,Matthew J. Friedman,Charles C. Engel,Edna B. Foa,M. Tracie Shea,Bruce K. Chow,Patricia A. Resick,Veronica Thurston,Susan M. Orsillo,Rodney Haug,Carole Turner,Nancy C. Bernardy +11 more
TL;DR: Prolonged exposure is an effective treatment for PTSD in female veterans and active-duty military personnel and it is feasible to implement prolonged exposure across a range of clinical settings.
Journal ArticleDOI
A population-based study of sexual orientation identity and gender differences in adult health.
TL;DR: Compared with heterosexuals, bisexuals reported more barriers to health care, current sadness, past-year suicidal ideation, and cardiovascular disease risk, and gay men were less likely to be overweight or obese and to obtain prostate-specific antigen tests, and lesbians were morelikely to be obese andto report multiple risks for cardiovascular disease.
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.