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
Statistical data preparation: management of missing values and outliers.
Sang Kyu Kwak,Jong Hae Kim +1 more
TL;DR: The types of missing values, ways of identifying outliers, and dealing with the two are discussed, which affect the process of estimating statistics, resulting in overestimated or underestimated values.
Journal ArticleDOI
Umgang mit fehlenden Werten in der psychologischen Forschung : Probleme und Lösungen
TL;DR: In this paper, a Ubersicht der in der Literatur diskutierten Ansatze zum Umgang mit fehlenden Werten vorgenommen, wobei drei Typen von Verfahren unterschieden werden.
Journal ArticleDOI
Identification and prospective validation of clinically relevant chronic obstructive pulmonary disease (COPD) subtypes
Judith Garcia-Aymerich,Federico P. Gómez,Marta Benet,Eva Farrero,Xavier Basagaña,Angel Gayete,Carles Paré,Xavier Freixa,Jaume Ferrer,Antoni Ferrer,Josep Roca,Juan B. Galdiz,Jaume Sauleda,Eduard Monsó,Joaquim Gea,Joan Albert Barberà,Alvar Agusti,Josep M. Antó +17 more
TL;DR: In patients with COPD recruited at their first hospitalisation, three different COPD subtypes were identified and prospectively validated: ‘severe respiratory COPD’, ‘moderate respiratory COPd’ and ‘systemic COPD'.
Journal ArticleDOI
Computational Strategies for Multivariate Linear Mixed-Effects Models With Missing Values
Joseph L. Schafer,Recai M. Yucel +1 more
TL;DR: In this paper, the authors present new computational techniques for multivariate longitudinal or clustered data with missing values by applying a multivariate extension of a popular linear mixed-effects model, creating multiple imputations of missing values for subsequent analyses by a straightforward and effective Markov chain Monte Carlo procedure.
Journal ArticleDOI
Investment in Energy Efficiency: Do the Characteristics of Firms Matter?
TL;DR: In this article, a discrete choice regression is estimated over a large sample of participating and non-participating firms to examine whether firms' characteristics influence their decision to join the Environmental Protection Agency's voluntary Green Lights program.
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.