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Registros recuperados: 11
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A Comparison of Imputation Methods under Large Samples and Different Censoring Levels (PowerPoint) AgEcon
Lopez, Jose Antonio.
PowerPoint Presentation
Tipo: Conference Paper or Presentation Palavras-chave: Imputation methods; Multiple imputation; Censored prices; Protein demand; Elasticities; Demand and Price Analysis; Food Consumption/Nutrition/Food Safety; Research Methods/ Statistical Methods; C81; Q11; R21.
Ano: 2011 URL: http://purl.umn.edu/109894
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A Comparison of Price Imputation Methods under Large Samples and Different Levels of Censoring. AgEcon
Lopez, Jose Antonio.
Research paper
Tipo: Conference Paper or Presentation Palavras-chave: Consumer/Household Economics; Demand and Price Analysis; Research Methods/ Statistical Methods; Imputation methods; Multiple imputation; Censored prices; Protein demand; Elasticities.
Ano: 2011 URL: http://purl.umn.edu/104498
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A new framework for managing and analyzing multiply imputed data in Stata AgEcon
Carlin, John B.; Galati, John C.; Royston, Patrick.
A new set of tools is described for performing analyses of an ensemble of datasets that includes multiple copies of the original data with imputations of missing values, as required for the method of multiple imputation. The tools replace those originally developed by the authors. They are based on a simple data management paradigm in which the imputed datasets are all stored along with the original data in a single dataset with a vertically stacked format, as proposed by Royston in his ice and micombine commands. Stacking into a single dataset simplifies the management of the imputed datasets compared with storing them individually. Analysis and manipulation of the stacked datasets is performed with a new prefix command, mim, which can accommodate data...
Tipo: Article Palavras-chave: Mim; Mimstack; Ice; Micombine; Miset; Mifit; Multiple imputation; Missing data; Missing at random; Research Methods/ Statistical Methods.
Ano: 2008 URL: http://purl.umn.edu/120928
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Multiple imputation of missing values AgEcon
Royston, Patrick.
Following the seminal publications of Rubin about thirty years ago, statisticians have become increasingly aware of the inadequacy of “complete-case” analysis of datasets with missing observations. In medicine, for example, observations may be missing in a sporadic way for different covariates, and a complete-case analysis may omit as many as half of the available cases. Hotdeck imputation was implemented in Stata in 1999 by Mander and Clayton. However, this technique may perform poorly when many rows of data have at least one missing value. This article describes an implementation for Stata of the MICE method of multiple multivariate imputation described by van Buuren, Boshuizen, and Knook (1999). MICE stands for multivariate imputation by chained...
Tipo: Journal Article Palavras-chave: Mvis; Uvis; Micombine; Mijoin; Misplit; Missing data; Missing at random; Multiple imputation; Multivariate imputation; Regression modeling; Research Methods/ Statistical Methods.
Ano: 2004 URL: http://purl.umn.edu/116244
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Multiple imputation of missing values: further update of ice, with an emphasis on interval censoring AgEcon
Royston, Patrick.
Multiple imputation of missing data continues to be a topic of considerable interest and importance to applied researchers. In this article, the ice package for multiple imputation is further updated. Special attention in this article is paid to imputing interval-censored observations, and a suggestion to use imputation of right-censored survival data to elucidate covariate effects graphically.
Tipo: Article Palavras-chave: Ice; Uvis; Micombine; Ice_reformat; Multiple imputation; Interval censoring; Visualization; Censored survival data; Research Methods/ Statistical Methods.
Ano: 2007 URL: http://purl.umn.edu/119290
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Multiple imputation of missing values: update AgEcon
Royston, Patrick.
This article describes a substantial update to mvis, which brings it more closely in line with the feature set of S. van Buuren and C. G. M. Oudshoorn’s implementation of the MICE system in R and S-PLUS (for details, see http://www.multiple-imputation.com). To make a clear distinction from mvis, the principal program of the new Stata release is called ice. I will give details of how to use the new features and a practical illustrative example using real data. All the facilities of mvis are retained by ice. Some improvements to micombine for computing estimates from multiply imputed datasets are also described.
Tipo: Journal Article Palavras-chave: Ice; Mvis; Uvis; Micombine; Mijoin; Misplit; Missing data; Missing at random; Multiple imputation; Multivariate imputation; Regression modeling; Research Methods/ Statistical Methods.
Ano: 2005 URL: http://purl.umn.edu/117511
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Multiple imputation of missing values: Update of ice AgEcon
Royston, Patrick.
Royston (2004) introduced mvis, an implementation for Stata of MICE, a method of multiple multivariate imputation of missing values under missing-at-random (MAR) assumptions. In a second article, Royston (2005) described ice, an upgrade incorporating various improvements and changes to the software based on personal experience, discussion with colleagues, and user requests. This article describes an update to ice. The changes are less substantial but nevertheless important enough to warrant a brief explanation. The major modification is that the default method of imputing missing values in ice is now by sampling from the posterior predictive distribution rather than by predicted mean matching. The ice system comprises five ado-files: ice, micombine,...
Tipo: Journal Article Palavras-chave: Ice; Uvis; Multiple imputation; Missing values; Update; Research Methods/ Statistical Methods.
Ano: 2005 URL: http://purl.umn.edu/117543
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Multiple imputation procedures allow the rescue of missing data: An application to determine serum tumor necrosis factor (TNF) concentration values during the treatment of rheumatoid arthritis patients with anti-TNF therapy Biol. Res.
SCHIATTINO,IRENE; VILLEGAS,RODRIGO; CRUZAT,ANDREA; CUENCA,JIMENA; SALAZAR,LORENA; ARAVENA,OCTAVIO; PESCE,BÁRBARA; CATALÁN,DIEGO; LLANOS,CAROLINA; CUCHACOVICH,MIGUEL; AGUILLÓN,JUAN C.
Longitudinal studies aimed at evaluating patients clinical response to specific therapeutic treatments are frequently summarized in incomplete datasets due to missing data. Multivariate statistical procedures use only complete cases, deleting any case with missing data. MI and MIANALYZE procedures of the SAS software perform multiple imputations based on the Markov Chain Monte Carlo method to replace each missing value with a plausible value and to evaluate the efficiency of such missing data treatment. The objective of this work was to compare the evaluation of differences in the increase of serum TNF concentrations depending on the ­308 TNF promoter genotype of rheumatoid arthritis (RA) patients receiving anti-TNF therapy with and without multiple...
Tipo: Journal article Palavras-chave: Multiple imputation; Mixed model; TNF polymorphism.
Ano: 2005 URL: http://www.scielo.cl/scielo.php?script=sci_arttext&pid=S0716-97602005000100002
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Series de tiempo con múltiples puntos de cambio y observaciones censuradas Colegio de Postgraduados
Castro Montoya, René.
Debido a factores externos a las variables de inter´es, una serie de tiempo puede presentar cambios en la estructura del modelo o en algunos par´ametros, y debido a l´ımitaciones en los instrumentos de medici´on puede presentar tambi´en censura en las observaciones. Por ejem- plo, cuando se monitorean contaminantes del aire, como pueden ser hidrocarburos arom´aticos (PAHs), mon´oxido de carbono (CO), di´oxido de sulfuro (S O2 ), etc., las series de tiempo ob- servadas pueden tener mediciones censuradas y cambios en la estructura del modelo. En esta tesis se propone un modelo bayesiano para series de tiempo con un n´ umero desconocido de puntos de cambio y con algunas observaciones censuradas, donde cada segmento es un proceso autoregresivo de...
Tipo: Tesis Palavras-chave: Imputación múltiple; Inferencia bayesiana; Algoritmo de Metrópolis; Algoritmo de cadenas de Markov Monte Carlo con saltos reversibles; Doctorado; Estadística; Multiple imputation; Bayesian inference; Metropolis algorithm; Reversible jump Markov chain Monte Carlo algorithm.
Ano: 2009 URL: http://hdl.handle.net/10521/1396
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Spatio-temporal modelling of coffee berry borer infestation patterns accounting for inflation of zeroes and missing values Scientia Agricola
Ruiz-Cárdenas,Ramiro; Assunção,Renato Martins; Demétrio,Clarice Garcia Borges.
The study of pest distributions in space and time in agricultural systems provides important information for the optimization of integrated pest management programs and for the planning of experiments. Two statistical problems commonly associated to the space-time modelling of data that hinder its implementation are the excess of zero counts and the presence of missing values due to the adopted sampling scheme. These problems are considered in the present article. Data of coffee berry borer infestation collected under Colombian field conditions are used to study the spatio-temporal evolution of the pest infestation. The dispersion of the pest starting from initial focuses of infestation was modelled considering linear and quadratic infestation growth...
Tipo: Info:eu-repo/semantics/article Palavras-chave: Markov chain Monte Carlo methods; Risk maps; Mixture model; Zero inflated model; Multiple imputation.
Ano: 2009 URL: http://www.scielo.br/scielo.php?script=sci_arttext&pid=S0103-90162009000100014
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Tools for analyzing multiple imputed datasets AgEcon
Carlin, John B.; Li, Ning; Greenwood, Philip; Coffey, Carolyn.
The method of multiple imputation (MI) is used increasingly for analyzing datasets with missing observations. Two sets of tasks are required in order to implement the method: (a) generating multiple complete datasets in which missing values have been imputed by simulating from an appropriate probability distribution and (b) analyzing the multiple imputed datasets and combining complete data inferences from them to form an overall inference for parameters of interest. An increasing number of software tools are available for task (a), although this is difficult to automate, because the method of imputation should depend on the context and available covariate data. When the quantity of missing data is not great, the sensitivity of results to the imputation...
Tipo: Journal Article Palavras-chave: Missing data; Multiple imputation; Rubin's rule of combination; Overall estimates; Research Methods/ Statistical Methods.
Ano: 2003 URL: http://purl.umn.edu/116087
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