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
EM and beyond
TL;DR: The basic theme of the EM algorithm, to repeatedly use complete-data methods to solve incomplete data problems, is also a theme of several more recent statistical techniques that combine simulation techniques with complete- data methods to attack problems that are difficult or impossible for EM.
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Dealing With Omitted and Not-Reached Items in Competence Tests: Evaluating Approaches Accounting for Missing Responses in Item Response Theory Models
TL;DR: This paper investigated the performance of classical and model-based approaches in empirical data, accounting for different kinds of missing responses simultaneously, and confirmed the existence of a unidimensional tendency to omit items.
Journal ArticleDOI
Ignorability in general incomplete-data models
TL;DR: In this article, the authors extend the Heitjan-Rubin model by explicitly defining the observed degree of coarseness as a data element, which permits the development of a frequentist theory, including a generalisation of'missing completely at random', the frequentist ignorability condition for missing data.
Journal ArticleDOI
Bayesian analysis for partially observed network data, missing ties, attributes and actors
TL;DR: An elaboration of the Bayesian data augmentation scheme of Koskinen et al. (2010) that uses the exchange algorithm for inference for the exponential random graph model (ERGM) where tie variables are partly observed.
Journal ArticleDOI
The impact of social protection and poverty elimination on global tuberculosis incidence: a statistical modelling analysis of Sustainable Development Goal 1
Daniel J Carter,Philippe Glaziou,Knut Lönnroth,Andrew M. Siroka,Katherine Floyd,Diana Weil,Mario C. Raviglione,Mario C. Raviglione,Rein M G J Houben,Delia Boccia +9 more
TL;DR: The reduction in global tuberculosis incidence that could be obtained by reaching SDG 1 is estimated and an exponential decay model based on linear associations betweenSDG 1 indicators and tuberculosis incidence is applied to estimate tuberculosis incidence in 2035.
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.
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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]).
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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.