robust variance estimates Search Results


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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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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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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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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clustercorrelated robust estimate of variance - by Bioz Stars, 2026-06
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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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robust variance estimates from generalised estimating equations - by Bioz Stars, 2026-06
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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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robust variance estimation (rve) method function for linear mixed effect models - by Bioz Stars, 2026-06
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logistic regression maximum likelihood estimation under naive independence without a robust variance  (SAS institute)
90
SAS institute logistic regression maximum likelihood estimation under naive independence without a robust 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
Logistic Regression Maximum Likelihood Estimation Under Naive Independence Without A Robust 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 estimator  (HealthCore Inc)
90
HealthCore Inc robust variance estimator
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 Estimator, supplied by HealthCore 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 estimators sas statement covsandwich  (SAS institute)
90
SAS institute robust variance estimators sas statement covsandwich
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 Estimators Sas Statement Covsandwich, 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 approach  (SAS institute)
90
SAS institute robust variance estimation approach
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 Approach, 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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robustness properties of variance component estimators  (Johns Hopkins HealthCare)
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Johns Hopkins HealthCare robustness properties of variance component estimators
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
Robustness Properties Of Variance Component Estimators, 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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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: