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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Springer Texts in Statistics
TL;DR: The Springer Texts in Statistics (STS) series as mentioned in this paper includes advanced textbooks from 3rd-to 4th-year undergraduate courses to 1st-to 2nd-year graduate courses.
Journal ArticleDOI
The Norwegian Institute of Public Health twin study of mental health: examining recruitment and attrition bias.
Kristian Tambs,Torbjørn Rønning,Carol A. Prescott,Kenneth S. Kendler,Ted Reichborn-Kjennerud,Svenn Torgersen,Jennifer R. Harris +6 more
TL;DR: Standard genetic twin analyses of indicators of various mental disorders from Q2, validated by diagnostic data from the MHS, did not indicate differences in genetic/environmental covariance structures between participants and nonparticipants in MHS.
Journal ArticleDOI
Shell-neighbor method and its application in missing data imputation
TL;DR: This paper introduces a new imputation approach called SN (Shell Neighbors) imputation, or simply SNI, and demonstrates that the generalized SNI method outperforms the kNN imputation method at imputation accuracy and classification accuracy.
BNT STRUCTURE LEARNING PACKAGE : Documentation and Experiments
TL;DR: The Bayes Net Toolbox for Matlab, introduced by Murphy (2004), offers functions for both using and learning Bayesian Networks, but this toolbox is not ’state of the art’ as regards structural learning methods, so the SLP package is proposed.
Journal ArticleDOI
Imputation using Markov chains
TL;DR: In this article, an iterative imputation procedure based on the idea of Markov chain is proposed, where the incomplete values are filled in through sampling from their predictive distribution, which is a theoretically sound method to fill in incomplete values.
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.