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
An introduction to modern missing data analyses
TL;DR: The theoretical underpinnings of missing data analyses are explained, an overview of traditional missing data techniques are given, and accessible descriptions of maximum likelihood and multiple imputation are provided.
Journal ArticleDOI
Recurrent Neural Networks for Multivariate Time Series with Missing Values.
TL;DR: In this article, a deep learning model based on Gated Recurrent Unit (GRU) is proposed to exploit the missing values and their missing patterns for effective imputation and improving prediction performance.
Journal ArticleDOI
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.
Journal ArticleDOI
Longitudinal Research: The Theory, Design, and Analysis of Change:
TL;DR: The trade-offs among analytic strategies (repeated measures general linear model, random coefficient modeling, and latent growth modeling), circumstances in which such methods are most appropriate, and ways to analyze data when one is using each approach are discussed.
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How Money Matters for Young Children's Development: Parental Investment and Family Processes
TL;DR: Much of the association between income and children's W-J scores was mediated by the family's ability to invest in providing a stimulating learning environment, and family income was associated with children's BPI scores primarily through maternal emotional distress and parenting practices.
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.