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
Tests of homoscedasticity, normality, and missing completely at random for incomplete multivariate data
Mortaza Jamshidian,Siavash Jalal +1 more
TL;DR: A modification of the proposed normal-theory Hawkins test for complete data is proposed to improve its performance, and its application to test of homoscedasticity and MCAR when data are multivariate normal and incomplete.
Journal ArticleDOI
Exploiting missing clinical data in Bayesian network modeling for predicting medical problems
Jau-Huei Lin,Peter J. Haug +1 more
TL;DR: This study experimented with four methods of treating missing values in a clinical data set and showed that in most cases the classifiers trained using the explicit missing value treatments performed better, suggesting that information may exist in "missingness" itself.
Journal ArticleDOI
Estimation of quantitative genetic parameters.
TL;DR: This paper gives a short review of the development of genetic parameter estimation over the last 40 years and their application to the analysis of artificial selection experiments and breeding programmes in animals.
Journal ArticleDOI
Daily touchscreen use in infants and toddlers is associated with reduced sleep and delayed sleep onset
Celeste H. M. Cheung,Rachael Bedford,Irati R. Saez de Urabain,Annette Karmiloff-Smith,Tim J. Smith +4 more
TL;DR: This is the first report linking the use of touchscreen with sleep problems in infants and toddlers andStructural equation models controlling for age, sex, TV exposure and maternal education indicated a significant association between touchscreen use and night-time sleep, daytime sleep and sleep onset.
Journal ArticleDOI
Adherence to Antiretroviral Therapy During and After Pregnancy: Cohort Study on Women Receiving Care in Malawi's Option B+ Program
Andreas D Haas,Malango T. Msukwa,Matthias Egger,Lyson Tenthani,Hannock Tweya,Andreas Jahn,Oliver J. Gadabu,Kali Tal,Luisa Salazar-Vizcaya,Janne Estill,Adrian Spoerri,Nozgechi Phiri,Frank Chimbwandira,Joep J. van Oosterhout,Olivia Keiser +14 more
TL;DR: One-third of women enrolled in Malawi's program to prevent human immunodeficiency virus mother-to-child-transmission adhered inadequately to antiretroviral therapy during pregnancy and breastfeeding.
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.