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
Framework for the treatment and reporting of missing data in observational studies: The Treatment And Reporting of Missing data in Observational Studies framework.
Katherine J Lee,Kate Tilling,Rosie Cornish,Roderick J. A. Little,Melanie L. Bell,Els Goetghebeur,Joseph W. Hogan,James R. Carpenter,James R. Carpenter +8 more
TL;DR: In this article, the authors present a framework for handling and reporting the analysis of incomplete data in observational studies, using a case study from the Avon Longitudinal Study of Parents and Children.
Journal ArticleDOI
Likelihood Estimation for Censored Random Vectors
TL;DR: In this paper, the authors show how to construct a likelihood for a general class of censoring problems, i.e., its maximizer is consistent and the respective root-n estimator is asymptotically efficient and normally distributed under regularity conditions.
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Exploring incomplete data using visualization techniques
TL;DR: The main goal of this contribution is to stress the importance of exploring missing values using visualization methods and to present a collection of such visualization techniques for incomplete data, all of which are implemented in the VIM package VIM.
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Identifying and mitigating biases in EHR laboratory tests
TL;DR: A methodology for leveraging measurement frequency to identify and reduce laboratory test biases is presented, showing that the context of a laboratory test measurement can often be captured by the way the test is measured through time.
Journal ArticleDOI
Postadoption parenting and socioemotional development in postinstitutionalized children.
TL;DR: Higher EA scores reduced the negative association between initiation of joint attention and indiscriminate friendliness, thus suggesting that parenting quality may moderate the effects of early institutional deprivation.
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.
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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.