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
Multiple Imputation Strategies for Multiple Group Structural Equation Models
TL;DR: In this paper, the authors use multiple imputation with multiple group models to examine moderation effects in psychology and the behavioral sciences, and show that failing to preserve the interactive effects during the imputation phase can produce biased parameter estimates in the subsequent analysis phase, even when the data are missing completely at random or missing at random.
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Prevalence of depressive symptoms and predictors of treatment among U.S. adults from 2005 to 2010
TL;DR: The prevalence of depressive symptoms is high, and only a small portion of patients with moderate to severe depression received treatments, and treatment disparities exist and need improvement.
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Bias in pharmacoepidemiologic studies using secondary health care databases: a scoping review.
TL;DR: Suboptimal use of secondary databases in pharmacoepidemiologic studies has introduced biases in the studies, which may have led to erroneous conclusions.
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A new imputation method for small software project data sets
Qinbao Song,Martin Shepperd +1 more
TL;DR: A class mean imputation (CMI) method based on the k-NN hot deck imputation method (MINI) to impute both continuous and nominal missing data in small data sets, using an incremental approach to increase the variance of population.
Journal ArticleDOI
A Latent Cluster-Mean Approach to the Contextual Effects Model With Missing Data
TL;DR: The authors proposed an alternative approach that conditions on the latent true cluster means of covariates having contextual effects while taking into account ignorable missing data with a general missing pattern at each level.
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
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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.