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
Estimating Gross Labor-Force Flows
John M. Abowd,Arnold Zellner +1 more
TL;DR: In this paper, an adjustment procedure for the Bureau of the Census and Bureau of Labor Statistics gross labor-force flows data is presented and applied to compute adjusted monthly gross change data for the period January 1977-December 1982.
Journal ArticleDOI
Efficient Approximations for the MarginalLikelihood of Bayesian Networks with Hidden Variables
TL;DR: Bayesian methods for model averaging and model selection among Bayesian-network models with hidden variables, and large-sample approximations for the marginal likelihood of naive-Bayes models in which the root node is hidden are examined.
Journal ArticleDOI
The comparative efficacy of imputation methods for missing data in structural equation modeling
TL;DR: This paper compares the efficacy of five current, and promising, methods that can be used to deal with missing data and concludes that MI, because of its theoretical and distributional underpinnings, is probably most promising for future applications in this field.
Journal ArticleDOI
Tuning multiple imputation by predictive mean matching and local residual draws.
TL;DR: PMM and LRD may have a role for imputing covariates which are not strongly associated with outcome, and when the imputation model is thought to be slightly but not grossly misspecified, which is better than fully parametric imputation in simulation studies.
Journal ArticleDOI
The misuse of BLUP in ecology and evolution.
TL;DR: Analytically and through simulation and example why BLUP often gives anticonservative and biased estimates of evolutionary and ecological parameters is shown and how unbiased and powerful tests can be derived that adequately quantify uncertainty are shown.
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.