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Improving predictive inference under covariate shift by weighting the loglikelihood function
 JOURNAL OF STATISTICAL PLANNING AND INFERENCE
, 2000
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Variable Selection with Incomplete Covariate Data
, 2007
"... Application of classical model selection methods such as Akaike’s information criterion AIC becomes problematic when observations are missing. In this paper we propose some variations on the AIC, which are applicable to missing covariate problems. The method is directly based on the EM algorithm a ..."
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Application of classical model selection methods such as Akaike’s information criterion AIC becomes problematic when observations are missing. In this paper we propose some variations on the AIC, which are applicable to missing covariate problems. The method is directly based on the EM algorithm and is readily available for EMbased estimation methods, without much additional computational efforts. The missing dataAIC criteria are formally derived and shown to work in a simulation study and by application to data on diabetic retinopathy.
Schwarz, Wallace, and Rissanen: Intertwining Themes in Theories of Model Selection
, 2000
"... Investigators interested in model order estimation have tended to divide themselves into widely separated camps; this survey of the contributions of Schwarz, Wallace, Rissanen, and their coworkers attempts to build bridges between the various viewpoints, illuminating connections which may have pr ..."
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Investigators interested in model order estimation have tended to divide themselves into widely separated camps; this survey of the contributions of Schwarz, Wallace, Rissanen, and their coworkers attempts to build bridges between the various viewpoints, illuminating connections which may have previously gone unnoticed and clarifying misconceptions which seem to have propagated in the applied literature. Our tour begins with Schwarz's approximation of Bayesian integrals via Laplace's method. We then introduce the concepts underlying Rissanen 's minimum description length principle via a Bayesian scenario with a known prior; this provides the groundwork for understanding his more complex nonBayesian MDL which employs a "universal" encoding of the integers. Rissanen's method of parameter truncation is contrasted with that employed in various versions of Wallace's minimum message length criteria.
A diagnostic for assessing the influence of cases on the prediction of random effects in a mixed model
 Journal of Data Science
, 2005
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February 1999UK NMS Software Support for Metrology Programme Model Validation Survey v1.0 MODEL VALIDATION IN THE CONTEXT OF
, 1999
"... Models are used in metrology to characterise measurement data and, where appropriate, the process which produced that data. Indeed, modelling is a procedure designed to extract information from data. Therefore, for any proposed model, the question then arises: is the information provided by the mode ..."
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Models are used in metrology to characterise measurement data and, where appropriate, the process which produced that data. Indeed, modelling is a procedure designed to extract information from data. Therefore, for any proposed model, the question then arises: is the information provided by the model comprehensive and reliable? This question is resolved by model validation. This report describes some theoretical concepts which prove useful in analysing models, and presents some practical examples from various metrological areas where they have been applied.
Missing Covariates in Logistic Regression, Estimation and Distribution Selection
, 2009
"... We derive explicit formulae for estimation in logistic regression models where some of the covariates are missing. Our approach allows for modeling the distribution of the missing covariates either as a multivariate normal or multivariate tdistribution. A main advantage of this method is that it is ..."
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We derive explicit formulae for estimation in logistic regression models where some of the covariates are missing. Our approach allows for modeling the distribution of the missing covariates either as a multivariate normal or multivariate tdistribution. A main advantage of this method is that it is fast and does not require the use of iterative procedures. A model selection method is derived which allows to choose amongst these distributions. In addition we consider versions of AIC that are based on the EM algorithm and on multiple imputation methods that have a wide applicability to model selection in likelihood models in general.
Working Paper M09/09 Methodology Estimation Of International Migration Flow Tables In Europe
, 2009
"... A methodology is developed to estimate comparable international migration flows between a set of countries. International migration flow data may be missing, reported by the sending country, reported by the receiving country or reported by both the sending and receiving countries. For the last situa ..."
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A methodology is developed to estimate comparable international migration flows between a set of countries. International migration flow data may be missing, reported by the sending country, reported by the receiving country or reported by both the sending and receiving countries. For the last situation, reported counts rarely match due to differences in definitions and data collection systems. In this paper, data known to be of a reliable standard is used to create an incomplete migration flow table of harmonized values. Cells for which no reliable reported flows exist are then estimated from a negative binomial regression model fitted using the ExpectationMaximization (EM) algorithm. Finally, measures of precision for all missing cell estimates are derived using the Supplemented EM algorithm. Recent data on international migration between countries in Europe are used to illustrate the methodology. The results represent a complete table of comparable flows that can be used by regional policy makers and social scientists alike to better understand population behaviour and change.
An Akaike Criterion based on Kullback Symmetric Divergence in the Presence of IncompleteData
"... This paper investigates and evaluates an extension of the Akaike information criterion, KIC, which is an approximately unbiased estimator for a risk function based on the Kullback symmetric divergence. KIC is based on the observeddata empirical loglikelihood which may be problematic to compute in ..."
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This paper investigates and evaluates an extension of the Akaike information criterion, KIC, which is an approximately unbiased estimator for a risk function based on the Kullback symmetric divergence. KIC is based on the observeddata empirical loglikelihood which may be problematic to compute in the presence of incompletedata. We derive and investigate a variant of KIC criterion for model selection in settings where the observeddata is incomplete. We examine the performance of our criterion relative to other well known criteria in a large simulation study based on bivariate normal model and bivariate regression modeling.
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"... Key words intrinsic quality factor – stress drop – rise time – corrected Akaike information criterion 1. ..."
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Key words intrinsic quality factor – stress drop – rise time – corrected Akaike information criterion 1.