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	Provedor de dados AgEcon (6) Autor Carroll, Raymond J. (6) Hardin, James W. (5) Schmiediche, Henrik (3) Amos, Christopher I. (1) Gutierrez, Roberto G. (1) Lin, Danyu Y. (1) Marchenko, Yulia V. (1) Mais... Palavra-chave Research Methods/ Statistical Methods (6) Generalized linear models (5) Measurement error (5) Instrumental variables (4) Replicate measures (2) Bootstrap (1) Case–control data (1) Haplologit (1) Haplotype-based analysis (1) Haplotype–environment independence (1) Hardy–Weinberg equilibrium (1) Murphy–Topel (1) Profile likelihood (1) Regression calibration (1) Retrospective study (1) Robust variance (1) Sandwich estimate of variance (1) Simulation extrapolation (1) Single nucleotide polymorphisms (SNPs) (1) Transportability (1) Mais... Tipo do documento Journal Article (5) Article (1) Ano 2008 (1) 2003 (5) País United States (6) Idioma Inglês (6)		Ordenar por:  Relevância Autor Título Ano Registros recuperados: 6 Primeira ... 1 ... Última Instrumental variables, bootstrapping, and generalized linear models AgEcon Hardin, James W.; Schmiediche, Henrik; Carroll, Raymond J.. This paper discusses and illustrates the qvf command for fitting generalized linear models. The differences between this new command and Stata’s glm command are highlighted. One of the most notable features of the qvf command is its ability to include instrumental variables. This functionality was added specifically to address measurement error but may be utilized by the user for other purposes. The qvf command was developed in the C-language using Stata’s new plugin features and executes much faster than the glm ado-file. Tipo: Journal Article Palavras-chave: Measurement error; Instrumental variables; Murphy–Topel; Bootstrap; Generalized linear models; Research Methods/ Statistical Methods. Ano: 2003 URL: http://purl.umn.edu/116178 Variance estimation for the instrumental variables approach to measurement error in generalized linear models AgEcon Hardin, James W.; Carroll, Raymond J.. This paper derives and gives explicit formulas for a derived sandwich variance estimate. This variance estimate is appropriate for generalized linear additive measurement error models fitted using instrumental variables. We also generalize the known results for linear regression. As such, this article explains the theoretical justification for the sandwich estimate of variance utilized in the software for measurement error developed under the Small Business Innovation Research Grant (SBIR) by StataCorp. The results admit estimation of variance matrices for measurement error models where there is an instrument for the unknown covariate. Tipo: Journal Article Palavras-chave: Sandwich estimate of variance; Measurement error; White's estimator; Robust variance; Generalized linear models; Instrumental variables; Research Methods/ Statistical Methods. Ano: 2003 URL: http://purl.umn.edu/116177 The simulation extrapolation method for fitting generalized linear models with additive measurement error AgEcon Hardin, James W.; Schmiediche, Henrik; Carroll, Raymond J.. We discuss and illustrate the method of simulation extrapolation for fitting models with additive measurement error. We present this discussion in terms of generalized linear models (GLMs) following the notation defined in Hardin and Carroll (2003). As in Hardin, Schmiediche, and Carroll (2003), our discussion includes specified measurement error and measurement error estimated by replicate error-prone proxies. In addition, we discuss and illustrate three extrapolant functions. Tipo: Journal Article Palavras-chave: Simulation extrapolation; Measurement error; Instrumental variables; Replicate measures; Generalized linear models; Research Methods/ Statistical Methods. Ano: 2003 URL: http://purl.umn.edu/116184 The regression-calibration method for fitting generalized linear models with additive measurement error AgEcon Hardin, James W.; Schmiediche, Henrik; Carroll, Raymond J.. This paper discusses and illustrates the method of regression calibration. This is a straightforward technique for fitting models with additive measurement error. We present this discussion in terms of generalized linear models (GLMs) following the notation defined in Hardin and Carroll (2003). Discussion will include specified measurement error, measurement error estimated by replicate error-prone proxies, and measurement error estimated by instrumental variables. The discussion focuses on software developed as part of a small business innovation research (SBIR) grant from the National Institutes of Health (NIH). Tipo: Journal Article Palavras-chave: Regression calibration; Measurement error; Instrumental variables; Replicate measures; Generalized linear models; Research Methods/ Statistical Methods. Ano: 2003 URL: http://purl.umn.edu/116180 Semiparametric analysis of case–control genetic data in the presence of environmental factors AgEcon Marchenko, Yulia V.; Carroll, Raymond J.; Lin, Danyu Y.; Amos, Christopher I.; Gutierrez, Roberto G.. In the past decade, many statistical methods have been proposed for the analysis of case–control genetic data with an emphasis on haplotype-based disease association studies. Most of the methodology has concentrated on the estimation of genetic (haplotype) main effects. Most methods accounted for environmental and gene–environment interaction effects by using prospective-type analyses that may lead to biased estimates when used with case–control data. Several recent publications addressed the issue of retrospective sampling in the analysis of case–control genetic data in the presence of environmental factors by developing efficient semiparametric statistical methods. This article describes the new Stata command haplologit, which implements efficient... Tipo: Article Palavras-chave: Haplologit; Haplotype-based analysis; Haplotype–environment independence; Case–control data; Hardy–Weinberg equilibrium; Profile likelihood; Retrospective study; Single nucleotide polymorphisms (SNPs); Research Methods/ Statistical Methods. Ano: 2008 URL: http://purl.umn.edu/122596 Measurement error, GLMs, and notational conventions AgEcon Hardin, James W.; Carroll, Raymond J.. This paper introduces additive measurement error in a generalized linear-model context. We discuss the types of measurement error along with their effects on fitted models. In addition, we present the notational conventions to be used in this and the accompanying papers. Tipo: Journal Article Palavras-chave: Generalized linear models; Transportability; Measurement error; Research Methods/ Statistical Methods. Ano: 2003 URL: http://purl.umn.edu/116175 Registros recuperados: 6 Primeira ... 1 ... Última

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