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
Multivariate Logistic Models for Incomplete Binary Responses
TL;DR: In this article, a likelihood-based regression model was proposed for analyzing incomplete multivariate binary responses. But the identification of the parameters of non-ignorable models is still open.
Journal ArticleDOI
Homogeneity analysis of Turkish meteorological data set
Sinan Şahin,H. Kerem Cigizoglu +1 more
TL;DR: In this article, the missing values on the monthly data set were estimated using two methods: the linear regression (LR) and the expectation maximization (EM) algorithm, because of higher correlations between test and reference series, EM algorithm results were preferred.
Journal ArticleDOI
Robust linear mixed models using the skew t distribution with application to schizophrenia data
Hsiu J. Ho,Tsung-I Lin +1 more
TL;DR: An efficient alternating expectation-conditional maximization (AECM) algorithm for the computation of maximum likelihood estimates of parameters on the basis of two convenient hierarchical formulations is presented.
Journal ArticleDOI
Pseudo-Maximum Likelihood Estimation of Mean and Covariance Structures with Missing Data
TL;DR: In this article, a nonlinear mean-and covariance-structure model for one or more groups is constructed, and the parameters of the model and the asymptotic covariance matrix of the parameter estimates using pseudo-maximum likelihood (PML) estimation.
Journal ArticleDOI
The prognostic value of bleeding academic research consortium (BARC)-defined bleeding complications in ST-segment elevation myocardial infarction: a comparison with the TIMI (Thrombolysis In Myocardial Infarction), GUSTO (Global Utilization of Streptokinase and Tissue Plasminogen Activator for Occluded Coronary Arteries), and ISTH (International Society on Thrombosis and Haemostasis) bleeding classifications
Wouter J. Kikkert,Nan van Geloven,Mariet H. van der Laan,Marije M. Vis,Jan Baan,Karel T. Koch,Ron J.G. Peters,Robbert J. de Winter,Jan J. Piek,Jan G.P. Tijssen,José P.S. Henriques +10 more
TL;DR: In this article, the authors compared 1-year mortality prediction of Bleeding Academic Research Consortium (BARC)-defined bleeding complications with existing bleeding definitions in patients with ST-segment elevation myocardial infarction (STEMI) and investigated the prognostic value of the individual data elements of theÂbleeding classifications for 1 year mortality.
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.