Moments and Distributions of Estimates of Population Parameters from Fragmentary Samples
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This article is published in Annals of Mathematical Statistics.The article was published on 1932-08-01 and is currently open access. It has received 219 citations till now. The article focuses on the topics: Population.read more
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Inference and missing data
TL;DR: 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.
Journal ArticleDOI
Inference and missing data
TL;DR: In this paper, 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.
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The Relative Performance of Full Information Maximum Likelihood Estimation for Missing Data in Structural Equation Models
TL;DR: A Monte Carlo simulation examined the performance of 4 missing data methods in structural equation models and found that full information maximum likelihood (FIML) estimation was superior across all conditions of the design.
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Methodology of the Social Sciences
TL;DR: Kaufmann as discussed by the authors surveys the general problems of scientific method, because of his belief that its chief issues are broadly identical in the natural and the social sciences, the difference between them being merely one of degree.
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Regression with missing X’s: A review
TL;DR: The literature of regression analysis with missing values of the independent variables is reviewed in this article, where six classes of procedures are distinguished: complete case analysis, available case methods, least squares on imputed data, maximum likelihood, Bayesian methods, and multiple imputation.