robust sandwich variance estimator Search Results


log-poisson model with robust error estimation sas proc genmod  (SAS institute)
90
SAS institute log-poisson model with robust error estimation sas proc genmod
Log Poisson Model With Robust Error Estimation Sas Proc Genmod, supplied by SAS institute, used in various techniques. Bioz Stars score: 90/100, based on 1 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
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multivariable poisson regression with a robust variance estimator  (SAS institute)
90
SAS institute multivariable poisson regression with a robust variance estimator
Multivariable Poisson Regression With A Robust Variance Estimator, supplied by SAS institute, used in various techniques. Bioz Stars score: 90/100, based on 1 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
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generalized linear models with robust sandwich estimators  (SAS institute)
90
SAS institute generalized linear models with robust sandwich estimators
Generalized Linear Models With Robust Sandwich Estimators, supplied by SAS institute, used in various techniques. Bioz Stars score: 90/100, based on 1 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
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robust variance estimation  (BOHER ARCHITECTURE LIMITED)
90
BOHER ARCHITECTURE LIMITED robust variance estimation
Asymptotic naive and <t>robust</t> standard errors of estimates of treatment effect under the <t>marginal</t> <t>model</t> (left panel) and partially conditional model (right panel) omitting a binary covariate Z as a function of \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$P(Z=1)$$\end{document} P ( Z = 1 ) ; \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\phi $$\end{document} ϕ is the odds ratio of ( X , Z ); \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\phi = 2.0$$\end{document} ϕ = 2.0
Robust Variance Estimation, supplied by BOHER ARCHITECTURE LIMITED, used in various techniques. Bioz Stars score: 90/100, based on 1 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
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robust sandwich estimation  (Morakot Industries Public Company Limited)
90
Morakot Industries Public Company Limited robust sandwich estimation
Asymptotic naive and <t>robust</t> standard errors of estimates of treatment effect under the <t>marginal</t> <t>model</t> (left panel) and partially conditional model (right panel) omitting a binary covariate Z as a function of \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$P(Z=1)$$\end{document} P ( Z = 1 ) ; \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\phi $$\end{document} ϕ is the odds ratio of ( X , Z ); \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\phi = 2.0$$\end{document} ϕ = 2.0
Robust Sandwich Estimation, supplied by Morakot Industries Public Company Limited, used in various techniques. Bioz Stars score: 90/100, based on 1 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
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robust sandwich estimator of standard errors proc genmod  (SAS institute)
90
SAS institute robust sandwich estimator of standard errors proc genmod
Asymptotic naive and <t>robust</t> standard errors of estimates of treatment effect under the <t>marginal</t> <t>model</t> (left panel) and partially conditional model (right panel) omitting a binary covariate Z as a function of \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$P(Z=1)$$\end{document} P ( Z = 1 ) ; \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\phi $$\end{document} ϕ is the odds ratio of ( X , Z ); \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\phi = 2.0$$\end{document} ϕ = 2.0
Robust Sandwich Estimator Of Standard Errors Proc Genmod, supplied by SAS institute, used in various techniques. Bioz Stars score: 90/100, based on 1 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
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robust sandwich estimate of lin and wei [13]  (Johns Hopkins HealthCare)
90
Johns Hopkins HealthCare robust sandwich estimate of lin and wei [13]
Asymptotic naive and <t>robust</t> standard errors of estimates of treatment effect under the <t>marginal</t> <t>model</t> (left panel) and partially conditional model (right panel) omitting a binary covariate Z as a function of \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$P(Z=1)$$\end{document} P ( Z = 1 ) ; \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\phi $$\end{document} ϕ is the odds ratio of ( X , Z ); \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\phi = 2.0$$\end{document} ϕ = 2.0
Robust Sandwich Estimate Of Lin And Wei [13], supplied by Johns Hopkins HealthCare, used in various techniques. Bioz Stars score: 90/100, based on 1 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
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poisson regression with robust variance estimates  (Wolters Kluwer Health)
90
Wolters Kluwer Health poisson regression with robust variance estimates
Asymptotic naive and <t>robust</t> standard errors of estimates of treatment effect under the <t>marginal</t> <t>model</t> (left panel) and partially conditional model (right panel) omitting a binary covariate Z as a function of \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$P(Z=1)$$\end{document} P ( Z = 1 ) ; \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\phi $$\end{document} ϕ is the odds ratio of ( X , Z ); \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\phi = 2.0$$\end{document} ϕ = 2.0
Poisson Regression With Robust Variance Estimates, supplied by Wolters Kluwer Health, used in various techniques. Bioz Stars score: 90/100, based on 1 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
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clustercorrelated robust estimate of variance  (SAS institute)
90
SAS institute clustercorrelated robust estimate of variance
Asymptotic naive and <t>robust</t> standard errors of estimates of treatment effect under the <t>marginal</t> <t>model</t> (left panel) and partially conditional model (right panel) omitting a binary covariate Z as a function of \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$P(Z=1)$$\end{document} P ( Z = 1 ) ; \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\phi $$\end{document} ϕ is the odds ratio of ( X , Z ); \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\phi = 2.0$$\end{document} ϕ = 2.0
Clustercorrelated Robust Estimate Of Variance, supplied by SAS institute, used in various techniques. Bioz Stars score: 90/100, based on 1 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
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robust variance estimates from generalised estimating equations  (SAS institute)
90
SAS institute robust variance estimates from generalised estimating equations
Asymptotic naive and <t>robust</t> standard errors of estimates of treatment effect under the <t>marginal</t> <t>model</t> (left panel) and partially conditional model (right panel) omitting a binary covariate Z as a function of \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$P(Z=1)$$\end{document} P ( Z = 1 ) ; \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\phi $$\end{document} ϕ is the odds ratio of ( X , Z ); \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\phi = 2.0$$\end{document} ϕ = 2.0
Robust Variance Estimates From Generalised Estimating Equations, supplied by SAS institute, used in various techniques. Bioz Stars score: 90/100, based on 1 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
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heteroskedasticity-robust nearest neighbor variance estimator with 3 neighbors  (RenderX Inc)
90
RenderX Inc heteroskedasticity-robust nearest neighbor variance estimator with 3 neighbors
Asymptotic naive and <t>robust</t> standard errors of estimates of treatment effect under the <t>marginal</t> <t>model</t> (left panel) and partially conditional model (right panel) omitting a binary covariate Z as a function of \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$P(Z=1)$$\end{document} P ( Z = 1 ) ; \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\phi $$\end{document} ϕ is the odds ratio of ( X , Z ); \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\phi = 2.0$$\end{document} ϕ = 2.0
Heteroskedasticity Robust Nearest Neighbor Variance Estimator With 3 Neighbors, supplied by RenderX Inc, used in various techniques. Bioz Stars score: 90/100, based on 1 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
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robust variance estimation (rve) method function for linear mixed effect models  (RStudio)
90
RStudio robust variance estimation (rve) method function for linear mixed effect models
Asymptotic naive and <t>robust</t> standard errors of estimates of treatment effect under the <t>marginal</t> <t>model</t> (left panel) and partially conditional model (right panel) omitting a binary covariate Z as a function of \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$P(Z=1)$$\end{document} P ( Z = 1 ) ; \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\phi $$\end{document} ϕ is the odds ratio of ( X , Z ); \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\phi = 2.0$$\end{document} ϕ = 2.0
Robust Variance Estimation (Rve) Method Function For Linear Mixed Effect Models, supplied by RStudio, used in various techniques. Bioz Stars score: 90/100, based on 1 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
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Image Search Results


Asymptotic naive and robust standard errors of estimates of treatment effect under the marginal model (left panel) and partially conditional model (right panel) omitting a binary covariate Z as a function of \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$P(Z=1)$$\end{document} P ( Z = 1 ) ; \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\phi $$\end{document} ϕ is the odds ratio of ( X , Z ); \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\phi = 2.0$$\end{document} ϕ = 2.0

Journal: Lifetime Data Analysis

Article Title: The effect of omitted covariates in marginal and partially conditional recurrent event analyses

doi: 10.1007/s10985-018-9430-y

Figure Lengend Snippet: Asymptotic naive and robust standard errors of estimates of treatment effect under the marginal model (left panel) and partially conditional model (right panel) omitting a binary covariate Z as a function of \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$P(Z=1)$$\end{document} P ( Z = 1 ) ; \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\phi $$\end{document} ϕ is the odds ratio of ( X , Z ); \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\phi = 2.0$$\end{document} ϕ = 2.0

Article Snippet: The model-based naive variance \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${{\mathcal {A}}}^{-1}(\beta ^\dagger )$$\end{document} A - 1 ( β † ) will underestimate the variability of \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${\hat{\beta }}$$\end{document} β ^ under a misspecified marginal model so robust variance estimation is recommended to ensure valid inference (Lin and Wei ; Bernardo and Harrington ; Boher and Cook ).

Techniques:

Estimates of treatment effect for cystic fibrosis trial using marginal and partially conditional models with four strata based on no events, 1 event, 2 events and \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\ge 3$$\end{document} ≥ 3 events when ignoring or controlling for the centered forced expiratory volume (FEVC)

Journal: Lifetime Data Analysis

Article Title: The effect of omitted covariates in marginal and partially conditional recurrent event analyses

doi: 10.1007/s10985-018-9430-y

Figure Lengend Snippet: Estimates of treatment effect for cystic fibrosis trial using marginal and partially conditional models with four strata based on no events, 1 event, 2 events and \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\ge 3$$\end{document} ≥ 3 events when ignoring or controlling for the centered forced expiratory volume (FEVC)

Article Snippet: The model-based naive variance \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${{\mathcal {A}}}^{-1}(\beta ^\dagger )$$\end{document} A - 1 ( β † ) will underestimate the variability of \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${\hat{\beta }}$$\end{document} β ^ under a misspecified marginal model so robust variance estimation is recommended to ensure valid inference (Lin and Wei ; Bernardo and Harrington ; Boher and Cook ).

Techniques: