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
Obesity and Underweight Are Associated with an Increased Risk of Death after Lung Transplantation
David J. Lederer,Jessie S. Wilt,Frank D'Ovidio,Matthew Bacchetta,Lori Shah,Shankari Ravichandran,Jenny Lenoir,Brenda Klein,Joshua R. Sonett,Selim M. Arcasoy +9 more
TL;DR: Both obesity and underweight are independent risk factors for death after lung transplantation, contributing to up to 12% of deaths in the first year after transplantation.
Journal ArticleDOI
Latent Space Models for Dynamic Networks
Daniel K. Sewell,Yuguo Chen +1 more
TL;DR: A model which embeds longitudinal network data as trajectories in a latent Euclidean space is presented and a novel approach is given to detect and visualize an attracting influence between actors using only the edge information.
Journal ArticleDOI
Improved double-robust estimation in missing data and causal inference models
TL;DR: A new class of double-robust estimators for the parameters of regression models with incompleteCross-sectional or longitudinal data, and of marginal structural mean models for cross-sectional data with similar efficiency properties are derived.
Journal ArticleDOI
Levels of Excess Infant Deaths Attributable to Maternal Smoking During Pregnancy in the United States
TL;DR: Smoking during pregnancy accounts for a sizeable number of infant deaths in the United States and highlights the need for infusion of more resources into existing smoking cessation campaigns in order to achieve higher quit rates, and substantially diminish current levels of smoking-associated infant deaths.
Book
Longitudinal Data Analysis Using Structural Equation Models
TL;DR: McArdle and Nesselroade as discussed by the authors identify five basic purposes of longitudinal structural equation modeling and present the most useful strategies and models for each purpose, and two important but underused approaches are emphasized: multiple factorial invariance over time and latent change scores.
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.