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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Mortality in takotsubo syndrome is similar to mortality in myocardial infarction - A report from the SWEDEHEART registry.
Björn Redfors,Ramtin Vedad,Oskar Angerås,Truls Råmunddal,Petur Petursson,Inger Haraldsson,Anwar Ali,Christian Dworeck,Jacob Odenstedt,Dan Ioaness,Berglin Libungan,Yangzhen Shao,Per Albertsson,Gregg W. Stone,Elmir Omerovic +14 more
TL;DR: The proportion of acute coronary syndromes attributed to takotsubo syndrome in Western Sweden has increased over the last decade, with similar early and late mortality as STEMI and NSTEMI.
Journal ArticleDOI
Imputations of Missing Values in Practice: Results from Imputations of Serum Cholesterol in 28 Cohort Studies
Federica Barzi,Mark Woodward +1 more
TL;DR: A comparison of eight imputation procedures and the naive, complete participant analysis for each of 28 studies in the Asia Pacific Cohort Studies Collaboration found clear differences existed between the methods, in which case past research suggests that multiple imputation is the method of choice.
Journal ArticleDOI
Distinguishing “Missing at Random” and “Missing Completely at Random”
Daniel F. Heitjan,Srabashi Basu +1 more
TL;DR: It is argued that practitioners who face potentially non-ignorable incomplete data must consider both the mode of inference and the nature of the conditioning when deciding which ignorability condition to invoke.
Journal ArticleDOI
Dealing With Missing Data in Developmental Research
TL;DR: A brief introduction to modern methods for handling missing data and their application to developmental research is provided.
RESEARCH REPORTS Leader Vision and the Development of Adaptive and Proactive Performance: A Longitudinal Study
TL;DR: The authors proposed that leader vision would lead to an increase in adaptivity for employees who were high in openness to work role change and would be associated with a increase in proactivity when employees wereHigh in role breadth self-efficacy.
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.