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
Clustering ensembles: models of consensus and weak partitions
TL;DR: A unified representation for multiple clusterings is introduced and a probabilistic model of consensus is proposed using a finite mixture of multinomial distributions in a space of clusterings in order to define a new consensus function related to the classical intraclass variance criterion.
Journal ArticleDOI
Marginal maximum likelihood estimation of item parameters
R. D. Bock,Murray Aitkin +1 more
SPECIAL SERIES: ORIGINAL ARTICLES Using the outcome for imputation of missing predictor values was preferred
TL;DR: In this article, the authors used regression coefficients and standard errors (SEs) estimated from the original sample were considered as ‘‘true’' values and repeated this 1,000 times using simulations.
Book ChapterDOI
Recovery of Information and Adjustment for Dependent Censoring Using Surrogate Markers
James M. Robins,Andrea Rotnitzky +1 more
TL;DR: In this paper, a class of tests and estimators for the parameters of the Cox proportional hazards model, the accelerated failure time model, and a model for the effect of treatment on the mean of a response variable of interest are proposed that use surrogate marker data to recover information lost due to independent censoring and to adjust for bias due to dependent censoring in randomized clinical trials.
Journal ArticleDOI
Missing Data Five Practical Guidelines
TL;DR: The current user-friendly review provides five easy-to-understand practical guidelines, with the goal of reducing missing data bias and error in the reporting of research results.
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.