robust variance estimation approach Search Results


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
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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heteroskedasticity-robust nearest neighbor variance estimator with 3 neighbors  (RenderX Inc)
90
RenderX Inc heteroskedasticity-robust nearest neighbor variance estimator with 3 neighbors
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
https://www.bioz.com/product/robust+variance+estimation+approach/heteroskedasticity+robust+nearest+neighbor+variance+estimator+with+3+neighbors/pm39475840-161-12-13
Average 90 stars, based on 1 article reviews
heteroskedasticity-robust nearest neighbor variance estimator with 3 neighbors - by Bioz Stars, 2026-09
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robust variance estimator  (HealthCore Inc)
90
HealthCore Inc robust variance estimator
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
https://www.bioz.com/product/robust+variance+estimation+approach/robust+variance+estimator/pmc05069141-207-3-17
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robust variance estimator - by Bioz Stars, 2026-09
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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
https://www.bioz.com/product/robust+variance+estimation+approach/robust+variance+estimation/pmc06423006-127-45-65
Average 90 stars, based on 1 article reviews
robust variance estimation - by Bioz Stars, 2026-09
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robustness properties of variance component estimators  (Johns Hopkins HealthCare)
90
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
https://www.bioz.com/product/robust+variance+estimation+approach/robustness+properties+of+variance+component+estimators/10__1080_slash_00031305__1970__10477209-340-1-22
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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: