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1.
Simultaneous Inference in General Parametric Models
Open Access
Title:
Simultaneous Inference in General Parametric Models
Author:
Torsten Hothorn
;
Frank Bretz
;
Peter Westfall
;
Torsten Hothorn
;
Frank Bretz
;
Novartis Pharma Ag
;
Peter Westfall
Torsten Hothorn
;
Frank Bretz
;
Peter Westfall
;
Torsten Hothorn
;
Frank Bretz
;
Novartis Pharma Ag
;
Peter Westfall
Minimize authors
Description:
Simultaneous inference is a common problem in many areas of application. If multiple null hypotheses are tested simultaneously, the probability of rejecting erroneously at least one of them increases beyond the prespecified significance level. Simultaneous inference procedures have to be used which adjust for multiplicity and thus control the o...
Simultaneous inference is a common problem in many areas of application. If multiple null hypotheses are tested simultaneously, the probability of rejecting erroneously at least one of them increases beyond the prespecified significance level. Simultaneous inference procedures have to be used which adjust for multiplicity and thus control the overall type I error rate. In this paper we describe simultaneous inference procedures in general parametric models, where the experimental questions are specified through a linear combination of elemental model parameters. The framework described here is quite general and extends the canonical theory of multiple comparison procedures in ANOVA models to linear regression problems, generalized linear models, linear mixed effects models, the Cox model, robust linear models, etc. Several examples using a variety of different statistical models illustrate the breadth of the results. For the analyses we use the R addon package multcomp, which provides a convenient interface to the general approach adopted here. Key words: multiple tests, multiple comparisons, simultaneous confidence intervals, adjusted pvalues, multivariate normal distribution, robust statistics. 1
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20081204
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http://epub.ub.unimuenchen.de/2120/1/tr019.pdf
http://epub.ub.unimuenchen.de/2120/1/tr019.pdf
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http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.122.4411
http://epub.ub.unimuenchen.de/2120/1/tr019.pdf
http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.122.4411
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2.
multcomp: Simultaneous Inference in General Parametric Models, 2008. URL http://CRAN. Rproject.org. R package version
Open Access
Title:
multcomp: Simultaneous Inference in General Parametric Models, 2008. URL http://CRAN. Rproject.org. R package version
Author:
Torsten Hothorn
;
Frank Bretz
;
Novartis Pharma Ag
;
Peter Westfall
Torsten Hothorn
;
Frank Bretz
;
Novartis Pharma Ag
;
Peter Westfall
Minimize authors
Description:
Simultaneous inference is a common problem in many areas of application. If multiple null hypotheses are tested simultaneously, the probability of rejecting erroneously at least one of them increases beyond the prespecified significance level. Simultaneous inference procedures have to be used which adjust for multiplicity and thus control the o...
Simultaneous inference is a common problem in many areas of application. If multiple null hypotheses are tested simultaneously, the probability of rejecting erroneously at least one of them increases beyond the prespecified significance level. Simultaneous inference procedures have to be used which adjust for multiplicity and thus control the overall type I error rate. In this paper we describe simultaneous inference procedures in general parametric models, where the experimental questions are specified through a linear combination of elemental model parameters. The framework described here is quite general and extends the canonical theory of multiple comparison procedures in ANOVA models to linear regression problems, generalized linear models, linear mixed effects models, the Cox model, robust linear models, etc. Several examples using a variety of different statistical models illustrate the breadth This is a preprint of an article published in Biometrical Journal, Volume 50, Number 3, 346–363.
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Year of Publication:
20100117
Source:
http://cran.rproject.org/web/packages/multcomp/vignettes/generalsiminf.pdf
http://cran.rproject.org/web/packages/multcomp/vignettes/generalsiminf.pdf
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http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.151.9267
http://cran.rproject.org/web/packages/multcomp/vignettes/generalsiminf.pdf
http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.151.9267
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3.
multcomp: Simultaneous Inference in General Parametric Models, 2008. URL http://CRAN. Rproject.org. R package version
Open Access
Title:
multcomp: Simultaneous Inference in General Parametric Models, 2008. URL http://CRAN. Rproject.org. R package version
Author:
Torsten Hothorn
;
Frank Bretz
;
Novartis Pharma Ag
;
Peter Westfall
Torsten Hothorn
;
Frank Bretz
;
Novartis Pharma Ag
;
Peter Westfall
Minimize authors
Description:
Simultaneous inference is a common problem in many areas of application. If multiple null hypotheses are tested simultaneously, the probability of rejecting erroneously at least one of them increases beyond the prespecified significance level. Simultaneous inference procedures have to be used which adjust for multiplicity and thus control the o...
Simultaneous inference is a common problem in many areas of application. If multiple null hypotheses are tested simultaneously, the probability of rejecting erroneously at least one of them increases beyond the prespecified significance level. Simultaneous inference procedures have to be used which adjust for multiplicity and thus control the overall type I error rate. In this paper we describe simultaneous inference procedures in general parametric models, where the experimental questions are specified through a linear combination of elemental model parameters. The framework described here is quite general and extends the canonical theory of multiple comparison procedures in ANOVA models to linear regression problems, generalized linear models, linear mixed effects models, the Cox model, robust linear models, etc. Several examples using a variety of different statistical models illustrate the breadth This is a preprint of an article published in Biometrical Journal, Volume 50, Number 3, 346–363.
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20120325
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http://cran.rproject.org/web/packages/multcomp/vignettes/generalsiminf.pdf
http://cran.rproject.org/web/packages/multcomp/vignettes/generalsiminf.pdf
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http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.216.1973
http://cran.rproject.org/web/packages/multcomp/vignettes/generalsiminf.pdf
http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.216.1973
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4.
multcomp: Simultaneous Inference in General Parametric Models, 2008. URL http://CRAN. Rproject.org. R package version
Open Access
Title:
multcomp: Simultaneous Inference in General Parametric Models, 2008. URL http://CRAN. Rproject.org. R package version
Author:
Torsten Hothorn
;
Frank Bretz
;
Novartis Pharma Ag
;
Peter Westfall
Torsten Hothorn
;
Frank Bretz
;
Novartis Pharma Ag
;
Peter Westfall
Minimize authors
Description:
Simultaneous inference is a common problem in many areas of application. If multiple null hypotheses are tested simultaneously, the probability of rejecting erroneously at least one of them increases beyond the prespecified significance level. Simultaneous inference procedures have to be used which adjust for multiplicity and thus control the o...
Simultaneous inference is a common problem in many areas of application. If multiple null hypotheses are tested simultaneously, the probability of rejecting erroneously at least one of them increases beyond the prespecified significance level. Simultaneous inference procedures have to be used which adjust for multiplicity and thus control the overall type I error rate. In this paper we describe simultaneous inference procedures in general parametric models, where the experimental questions are specified through a linear combination of elemental model parameters. The framework described here is quite general and extends the canonical theory of multiple comparison procedures in ANOVA models to linear regression problems, generalized linear models, linear mixed effects models, the Cox model, robust linear models, etc. Several examples using a variety of different statistical models illustrate the breadth This is a preprint of an article published in Biometrical Journal, Volume 50, Number 3, 346–363.
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The Pennsylvania State University CiteSeerX Archives
Year of Publication:
20100725
Source:
http://cran.at.rproject.org/web/packages/multcomp/vignettes/generalsiminf.pdf
http://cran.at.rproject.org/web/packages/multcomp/vignettes/generalsiminf.pdf
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310 Collections of general statistics
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http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.168.2924
http://cran.at.rproject.org/web/packages/multcomp/vignettes/generalsiminf.pdf
http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.168.2924
http://cran.at.rproject.org/web/packages/multcomp/vignettes/generalsiminf.pdf
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5.
On multiple comparisons
Open Access
Title:
On multiple comparisons
Author:
Frank Bretz
;
Torsten Hothorn
;
Peter Westfall
Frank Bretz
;
Torsten Hothorn
;
Peter Westfall
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The multiplicity problem arises when several inferences are considered simultaneously as a group. If each inference has a 5 % error rate, then the error rate over the entire group can be much higher than 5%. This article shows practical examples of multiple comparisons procedures that control the error of making any incorrect inference. The mul...
The multiplicity problem arises when several inferences are considered simultaneously as a group. If each inference has a 5 % error rate, then the error rate over the entire group can be much higher than 5%. This article shows practical examples of multiple comparisons procedures that control the error of making any incorrect inference. The multcomp package for the R statistical environment allows for multiple comparisons of parameters whose estimates are generally correlated, including comparisons of k groups in general linear models. The package has many common multiple comparison procedures “hardcoded”, including Dunnett, Tukey, sequential pairwise contrasts, comparisons with the average, changepoint analysis, Williams’, Marcus’, McDermott’s, and tetrad contrasts. In addition, a free input interface for the contrast matrix allows for more general comparisons. The comparisons themselves are not restricted to balanced or simple designs. Instead, the package is designed to provide general multiple comparisons, thus allowing for covariates, nested effects, correlated means, likelihoodbased esti
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Year of Publication:
20150116
Source:
http://math.furman.edu/~dcs/courses/math47/R/library/multcomp/doc/Rmc.pdf
http://math.furman.edu/~dcs/courses/math47/R/library/multcomp/doc/Rmc.pdf
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http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.532.8640
http://math.furman.edu/~dcs/courses/math47/R/library/multcomp/doc/Rmc.pdf
http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.532.8640
http://math.furman.edu/~dcs/courses/math47/R/library/multcomp/doc/Rmc.pdf
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6.
multcomp: Simultaneous Inference in General Parametric Models, 2008. URL http://CRAN. Rproject.org. R package version
Open Access
Title:
multcomp: Simultaneous Inference in General Parametric Models, 2008. URL http://CRAN. Rproject.org. R package version
Author:
Torsten Hothorn
;
Frank Bretz
;
Novartis Pharma Ag
;
Peter Westfall
Torsten Hothorn
;
Frank Bretz
;
Novartis Pharma Ag
;
Peter Westfall
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Description:
Simultaneous inference is a common problem in many areas of application. If multiple null hypotheses are tested simultaneously, the probability of rejecting erroneously at least one of them increases beyond the prespecified significance level. Simultaneous inference procedures have to be used which adjust for multiplicity and thuscontroltheover...
Simultaneous inference is a common problem in many areas of application. If multiple null hypotheses are tested simultaneously, the probability of rejecting erroneously at least one of them increases beyond the prespecified significance level. Simultaneous inference procedures have to be used which adjust for multiplicity and thuscontroltheoveralltypeIerrorrate. Inthispaperwedescribesimultaneousinference procedures in general parametric models, where the experimental questions are specified through a linear combination of elemental model parameters. The framework described here is quite general and extends the canonical theory of multiple comparison procedures in ANOVA models to linear regression problems, generalized linear models, linear mixed effects models, the Cox model, robust linear models, etc. Several examples using a variety of different statistical models illustrate the breadth This is a preprint of an article published in Biometrical Journal, Volume 50, Number 3, 346–363.
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Year of Publication:
20130724
Source:
http://cran.rproject.org/web/packages/multcomp/vignettes/generalsiminf.pdf
http://cran.rproject.org/web/packages/multcomp/vignettes/generalsiminf.pdf
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http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.304.1954
http://cran.rproject.org/web/packages/multcomp/vignettes/generalsiminf.pdf
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7.
multcomp: Simultaneous Inference in General Parametric Models, 2008. URL http://CRAN. Rproject.org. R package version
Open Access
Title:
multcomp: Simultaneous Inference in General Parametric Models, 2008. URL http://CRAN. Rproject.org. R package version
Author:
Torsten Hothorn
;
Frank Bretz
;
Novartis Pharma Ag
;
Peter Westfall
Torsten Hothorn
;
Frank Bretz
;
Novartis Pharma Ag
;
Peter Westfall
Minimize authors
Description:
Simultaneous inference is a common problem in many areas of application. If multiple null hypotheses are tested simultaneously, the probability of rejecting erroneously at least one of them increases beyond the prespecified significance level. Simultaneous inference procedures have to be used which adjust for multiplicity and thus control the ...
Simultaneous inference is a common problem in many areas of application. If multiple null hypotheses are tested simultaneously, the probability of rejecting erroneously at least one of them increases beyond the prespecified significance level. Simultaneous inference procedures have to be used which adjust for multiplicity and thus control the overall type I error rate. In this paper we describe simultaneous inference procedures in general parametric models, where the experimental questions are specified through a linear combination of elemental model parameters. The framework described here is quite general and extends the canonical theory of multiple comparison procedures in ANOVA models to linear regression problems, generalized linear models, linear mixed effects models, the Cox model, robust linear models, etc. Several examples using a variety of different statistical models illustrate the breadth ∗This is a preprint of an article published in Biometrical Journal, Volume 50, Number 3, 346–363. Copyright ➞ 2008 WILEYVCH Verlag GmbH & Co. KGaA, Weinheim; available online http://www. biometricaljournal.com. 1 of the results. For the analyses we use the R addon package multcomp, which provides a convenient interface to the general approach adopted here. Key words: multiple tests, multiple comparisons, simultaneous confidence intervals, adjusted pvalues, multivariate normal distribution, robust statistics. 1
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20141210
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http://cran.fhcrc.org/web/packages/multcomp/vignettes/generalsiminf.pdf
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http://cran.fhcrc.org/web/packages/multcomp/vignettes/generalsiminf.pdf
http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.470.9372
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8.
multcomp: Simultaneous Inference in General Parametric Models, 2008. URL http://CRAN. Rproject.org. R package version
Open Access
Title:
multcomp: Simultaneous Inference in General Parametric Models, 2008. URL http://CRAN. Rproject.org. R package version
Author:
Torsten Hothorn
;
Frank Bretz
;
Novartis Pharma Ag
;
Peter Westfall
Torsten Hothorn
;
Frank Bretz
;
Novartis Pharma Ag
;
Peter Westfall
Minimize authors
Description:
Simultaneous inference is a common problem in many areas of application. If multiple null hypotheses are tested simultaneously, the probability of rejecting erroneously at least one of them increases beyond the prespecified significance level. Simultaneous inference procedures have to be used which adjust for multiplicity and thus control the o...
Simultaneous inference is a common problem in many areas of application. If multiple null hypotheses are tested simultaneously, the probability of rejecting erroneously at least one of them increases beyond the prespecified significance level. Simultaneous inference procedures have to be used which adjust for multiplicity and thus control the overall type I error rate. In this paper we describe simultaneous inference procedures in general parametric models, where the experimental questions are specified through a linear combination of elemental model parameters. The framework described here is quite general and extends the canonical theory of multiple comparison procedures in ANOVA models to linear regression problems, generalized linear models, linear mixed effects models, the Cox model, robust linear models, etc. Several examples using a variety of different statistical models illustrate the breadth This is a preprint of an article published in Biometrical Journal, Volume 50, Number 3, 346–363.
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Year of Publication:
20100415
Source:
http://cran.rproject.org/web/packages/multcomp/vignettes/generalsiminf.pdf
http://cran.rproject.org/web/packages/multcomp/vignettes/generalsiminf.pdf
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310 Collections of general statistics
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http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.161.3255
http://cran.rproject.org/web/packages/multcomp/vignettes/generalsiminf.pdf
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9.
Chairperson of the Committee
Open Access
Title:
Chairperson of the Committee
Author:
Andrew John Patterson
;
Tanja Karp
;
Sunanda Mitra
;
Brian Nutter
;
Peter Westfall
;
John Borrelli
Andrew John Patterson
;
Tanja Karp
;
Sunanda Mitra
;
Brian Nutter
;
Peter Westfall
;
John Borrelli
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I would first like to thank my committee members for their patience and comments throughout writing this dissertation. I would especially like to thank Dr. Tanja Karp for her guidance and willingness to always discuss any questions I had. I would also like to thank my wonderful parents whose love and support has been constant and unwavering. My ...
I would first like to thank my committee members for their patience and comments throughout writing this dissertation. I would especially like to thank Dr. Tanja Karp for her guidance and willingness to always discuss any questions I had. I would also like to thank my wonderful parents whose love and support has been constant and unwavering. My inlaws have also been great to me throughout this dissertation. Thank you for all the encouragement, but most of all thank you for not coming close to my computer while I finished!! I thank God for giving me the wonderful opportunity and experience I had at Texas Tech, and for providing wisdom and encouragement when I needed it most. Lastly and most importantly I would like to thank my loving wife. Her support and encouragement was crucial in me finishing this dissertation. Her patience with me while I spent the late nights was unbelievable. Thank you. I owe you one!!
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Year of Publication:
20080701
Source:
http://etd.lib.ttu.edu/theses/available/etd04252006004528/unrestricted/Patterson_Andrew_Diss.pdf
http://etd.lib.ttu.edu/theses/available/etd04252006004528/unrestricted/Patterson_Andrew_Diss.pdf
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CONTENTS ACKNOWLEDGEMENTS. ii ABSTRACT. vi
CONTENTS ACKNOWLEDGEMENTS. ii ABSTRACT. vi
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http://etd.lib.ttu.edu/theses/available/etd04252006004528/unrestricted/Patterson_Andrew_Diss.pdf
http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.87.1766
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10.
Chairperson of the Committee
Open Access
Title:
Chairperson of the Committee
Author:
Scott E Hein
;
Jeffrey M Mercer
;
Mike Stegemoller
;
Bradley T Ewing
;
Peter H Westfall
;
John Borrelli
;
Mike Stegemoller
;
Bradley Ewing
;
Peter Westfall
Scott E Hein
;
Jeffrey M Mercer
;
Mike Stegemoller
;
Bradley T Ewing
;
Peter H Westfall
;
John Borrelli
;
Mike Stegemoller
;
Bradley Ewing
;
Peter Westfall
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Copyright 2006, EngkuNgah S. EngkuChik ACKNOWLEDGEMENTS I have been greatly aided in this dissertation by the insightful suggestions and guidance provided
Copyright 2006, EngkuNgah S. EngkuChik ACKNOWLEDGEMENTS I have been greatly aided in this dissertation by the insightful suggestions and guidance provided
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http://etd.lib.ttu.edu/theses/available/etd07162006141253/unrestricted/EngkuChik_EngkuNgah.pdf
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