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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BookDOI
Applied Bayesian modeling and causal inference from incomplete-data perspectives : an essential journey with Donald Rubin's statistical family
Andrew Gelman,Xiao-Li Meng +1 more
TL;DR: Applied Bayesian modeling and causal inference from incomplete-data perspectives as discussed by the authors, applied Bayesian modelling and causality from incomplete data perspectives, Applied Bayesian model and inference in incomplete data perspective.
Journal ArticleDOI
Structure and Stress: Trajectories of Depressive Symptoms across Adolescence and Young Adulthood
TL;DR: This study develops a nuanced, dynamic model of the multiplicative effects of social disadvantage on early life depression disparities, and indicates females and minorities experience elevated depressive symptoms across early life compared to males and whites.
Journal ArticleDOI
Conceptualizing academic-entrepreneurial intentions: An empirical test
Igor Prodan,Mateja Drnovsek +1 more
TL;DR: In this article, a conceptual model of academics' entrepreneurial intentions is proposed, and empirically tested the model using structural equation modeling and a robust data set collected in two European academic settings.
Journal ArticleDOI
Nabiximols as an agonist replacement therapy during cannabis withdrawal: a randomized clinical trial.
David J. Allsop,Jan Copeland,Nicholas Lintzeris,Adrian Dunlop,Mark Montebello,Craig Sadler,Gonzalo Rivas,R Holland,Peter Muhleisen,Melissa M. Norberg,Jessica Booth,Iain S. McGregor +11 more
TL;DR: In a treatment-seeking cohort, nabiximols attenuated cannabis withdrawal symptoms and improved patient retention in treatment, however, placebo was as effective as nabximols in promoting long-term reductions in cannabis use following medication cessation.
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.