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
Survival After Application of Automatic External Defibrillators Before Arrival of the Emergency Medical System: Evaluation in the Resuscitation Outcomes Consortium Population of 21 Million
Myron L. Weisfeldt,Colleen M. Sitlani,Joseph P. Ornato,Thomas D. Rea,Tom P. Aufderheide,Daniel Davis,Jonathan Dreyer,Erik P. Hess,Jonathan Jui,Justin Maloney,George Sopko,Judy Powell,Graham Nichol,Laurie J. Morrison +13 more
TL;DR: Application of an AED in communities is associated with nearly a doubling of survival after out-of-hospital cardiac arrest, and these results reinforce the importance of strategically expanding community-based AED programs.
Journal ArticleDOI
Weighting for unequal selection probabilities in multilevel models
TL;DR: In this article, the authors proposed alternative ways of weighting the estimation of a two-level model by using the reciprocals of the selection probabilities at each stage of sampling, and demonstrated that the variance estimators perform extremely well.
Journal ArticleDOI
The global burden of listeriosis: A systematic review and meta-analysis
Charline Maertens de Noordhout,Brecht Devleesschauwer,Brecht Devleesschauwer,Frederick J. Angulo,Geert Verbeke,Juanita A. Haagsma,Martyn D. Kirk,Arie H. Havelaar,Niko Speybroeck +8 more
TL;DR: The first estimates of global numbers of illnesses, deaths, and disability-adjusted life-years (DALYs) due to listeriosis are provided, by synthesising information and knowledge through a systematic review of peer-reviewed and grey literature.
Journal ArticleDOI
Factor analytic models: viewing the structure of an assessment instrument from three perspectives.
TL;DR: A nonmathematical introduction to the application of confirmatory factor analysis (CFA) within the framework of structural equation modeling as it applies to psychological assessment instruments and identifies several common misconceptions and improper application practices.
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Restricted maximum likelihood estimation of covariances in sparse linear models
TL;DR: This paper surveys the theoretical and computational development of the restricted maximum likelihood approach for the estimation of covariance matrices in linear stochastic models, and gives a new derivation of this approach, valid under very weak conditions on the noise.
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.