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
Bounded, efficient and doubly robust estimation with inverse weighting
TL;DR: This paper proposed doubly robust estimators, which have desirable properties in efficiency if the propensity score model is correctly specified, and in boundedness even if the inverse probability weights are highly variable.
Journal ArticleDOI
The Treatment of Missing Data in Multivariate Analysis
Jae-On Kim,James P. Curry +1 more
TL;DR: How to assess the nature of missing data especially with regard to randomness, a comparison of listwise and pairwise deletion, and methods for using maximum information to estimate parameters or missing values are covered.
Journal Article
Comparison of discrimination techniques applied to a complex data set of head injured patients
D. M. Titterington,G. Murray,L. S. Murray,David Spiegelhalter,A. M. Skene,J. D. F. Habbema,G. J. Gelpke +6 more
TL;DR: In this paper, several techniques for discriminant analysis are applied to a set of data from patients with severe head injuries, for the purpose of prognosis, such that multidimensionality, continuous, binary and ordered categorical variables and missing data must be coped with.
Journal ArticleDOI
Models for Nonresponse in Sample Surveys
TL;DR: Key concepts from the literature on incomplete data, such as factorizations of the likelihood for specialData patterns, the EM algorithm for general data patterns, and ignorability of the response mechanism, are discussed within the survey context.
Journal ArticleDOI
Higher risk of death and stroke in patients with persistent vs. paroxysmal atrial fibrillation: results from the ROCKET-AF Trial
Benjamin A. Steinberg,Anne S. Hellkamp,Yuliya Lokhnygina,Manesh R. Patel,Günter Breithardt,Graeme J. Hankey,Richard C. Becker,Daniel E. Singer,Jonathan L. Halperin,Werner Hacke,Christopher C. Nessel,Scott D. Berkowitz,Kenneth W. Mahaffey,Keith A.A. Fox,Robert M. Califf,Jonathan P. Piccini +15 more
TL;DR: In patients with AF at moderate-to-high risk of stroke receiving anticoagulation, those with persistent AF have a higher risk of thrombo-embolic events and worse survival compared with paroxysmal AF.
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.