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Title:

FORTRAN 90 and SAS-IML Programs for Computation of Critical Values for Multiple Testing and Simultaneous Confidence Intervals

Publisher:

University of California at Los Angeles, Department of Statistics

Year of Publication:

2001-01-01T00:00:00Z

Document Type:

article

Language:

English

Subjects:

LCC:Statistics ; LCC:HA1-4737 ; LCC:Social Sciences ; LCC:H ; DOAJ:Statistics ; DOAJ:Mathematics and Statistics ; LCC:Statistics ; LCC:HA1-4737 ; LCC:Social Sciences ; LCC:H ; DOAJ:Statistics ; DOAJ:Mathematics and Statistics ; LCC:Statistics ; LCC:HA1-4737 ; LCC:Social Sciences ; LCC:H ; LCC:Statistics ; LCC:HA1-4737 ; LCC:Social Sciences ; LCC:H

LCC:Statistics ; LCC:HA1-4737 ; LCC:Social Sciences ; LCC:H ; DOAJ:Statistics ; DOAJ:Mathematics and Statistics ; LCC:Statistics ; LCC:HA1-4737 ; LCC:Social Sciences ; LCC:H ; DOAJ:Statistics ; DOAJ:Mathematics and Statistics ; LCC:Statistics ; LCC:HA1-4737 ; LCC:Social Sciences ; LCC:H ; LCC:Statistics ; LCC:HA1-4737 ; LCC:Social Sciences ; LCC:H Minimize

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http://www.jstatsoft.org/v06/i05/paper

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Title:

FORTRAN 90 and SAS-IML Programs for Computation of Critical Values for Multiple Testing and Simultaneous Confidence Intervals

Description:

See paper for mathematical introduction.

See paper for mathematical introduction. Minimize

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article

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Title:

MCPMod: An R Package for the Design and Analysis of Dose-Finding Studies

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In this article the MCPMod package for the R programming environment will be introduced. It implements a recently developed methodology for the design and analysis of dose-response studies that combines aspects of multiple comparison procedures and modeling approaches (Bretz et al. 2005). The MCPMod package provides tools for the analysis of dos...

In this article the MCPMod package for the R programming environment will be introduced. It implements a recently developed methodology for the design and analysis of dose-response studies that combines aspects of multiple comparison procedures and modeling approaches (Bretz et al. 2005). The MCPMod package provides tools for the analysis of dose finding trials, as well as a variety of tools necessary to plan an experiment to be analyzed using the MCP-Mod methodology. Minimize

Publisher:

University of California, Los Angeles

Year of Publication:

2009-01-01T00:00:00Z

Document Type:

article

Language:

English

Subjects:

LCC:Statistics ; LCC:HA1-4737 ; LCC:Social Sciences ; LCC:H ; DOAJ:Statistics ; DOAJ:Mathematics and Statistics ; LCC:Statistics ; LCC:HA1-4737 ; LCC:Social Sciences ; LCC:H ; DOAJ:Statistics ; DOAJ:Mathematics and Statistics ; LCC:Statistics ; LCC:HA1-4737 ; LCC:Social Sciences ; LCC:H ; LCC:Statistics ; LCC:HA1-4737 ; LCC:Social Sciences ; LCC:H

LCC:Statistics ; LCC:HA1-4737 ; LCC:Social Sciences ; LCC:H ; DOAJ:Statistics ; DOAJ:Mathematics and Statistics ; LCC:Statistics ; LCC:HA1-4737 ; LCC:Social Sciences ; LCC:H ; DOAJ:Statistics ; DOAJ:Mathematics and Statistics ; LCC:Statistics ; LCC:HA1-4737 ; LCC:Social Sciences ; LCC:H ; LCC:Statistics ; LCC:HA1-4737 ; LCC:Social Sciences ; LCC:H Minimize

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http://www.jstatsoft.org/v29/i07/paper

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Title:

MCPMod: An R Package for the Design and Analysis of Dose-Finding Studies

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Description:

In this article the MCPMod package for the R programming environment will be introduced. It implements a recently developed methodology for the design and analysis of dose-response studies that combines aspects of multiple comparison procedures and modeling approaches (Bretz et al. 2005). The MCPMod package provides tools for the analysis of dos...

In this article the MCPMod package for the R programming environment will be introduced. It implements a recently developed methodology for the design and analysis of dose-response studies that combines aspects of multiple comparison procedures and modeling approaches (Bretz et al. 2005). The MCPMod package provides tools for the analysis of dose finding trials, as well as a variety of tools necessary to plan an experiment to be analyzed using the MCP-Mod methodology. Minimize

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Title:

Efficient and easy-to-use sample size formulas in ratio-based non-inferiority tests

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In many biomedical applications, tests for the classical hypotheses based on the difference of treatment means in a one-way layout can be replaced by tests for ratios (or tests for relative changes). This approach is well noted for its simplicity in defining the margins, as for example in tests for non-inferiority. Here, we derive approximate an...

In many biomedical applications, tests for the classical hypotheses based on the difference of treatment means in a one-way layout can be replaced by tests for ratios (or tests for relative changes). This approach is well noted for its simplicity in defining the margins, as for example in tests for non-inferiority. Here, we derive approximate and efficient sample size formulas in a multiple testing situation and then thoroughly investigate the relative performance of hypothesis testing based on the ratios of treatment means when compared with differences of means. The results will be illustrated with an example on simultaneous tests for non-inferiority. ; relative margin, sample size, multivariate t, normal approximation Minimize

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Title:

Methods for the Computation of Multivariate t-Probabilities

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This paper compares methods for the numerical computation of multivariate t-probabilities for hyperrectangular integration regions. Methods based on acceptance-rejection, spherical-radial transformations and separation-of-variables transformations are considered. Tests using randomly chosen problems show that the most efficient numerical methods...

This paper compares methods for the numerical computation of multivariate t-probabilities for hyperrectangular integration regions. Methods based on acceptance-rejection, spherical-radial transformations and separation-of-variables transformations are considered. Tests using randomly chosen problems show that the most efficient numerical methods use a transformation developed by Genz (1992) for multivariate normal probabilities. These methods allow moderately accurate multivariate t-probabilities to be quickly computed for problems with as many as twenty variables. Methods for the non-central multivariate t-distribution are also described. Key Words: multivariate t-distribution, non-central distribution, numerical integration, statistical computation. 1 Introduction A common problem in many statistics applications is the numerical computation of the multivariate t (MVT) distribution function (see Tong, 1990) defined by T(a; b; \Sigma; ) = \Gamma( +m 2 ) \Gamma( 2 ) p j\Sigma. Minimize

Contributors:

The Pennsylvania State University CiteSeerX Archives

Year of Publication:

2009-04-15

Source:

http://www.sci.wsu.edu/math/faculty/genz/papers/mvtcmpn.ps

http://www.sci.wsu.edu/math/faculty/genz/papers/mvtcmpn.ps Minimize

Document Type:

text

Language:

en

DDC:

518 Numerical analysis *(computed)*

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Metadata may be used without restrictions as long as the oai identifier remains attached to it.

Metadata may be used without restrictions as long as the oai identifier remains attached to it. Minimize

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Title:

Comparison of Methods for the Computation Of Multivariate t-Probabilities

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This paper compares methods for the numerical computation of multivariate t-probabilities for hyperrectangular integration regions. Methods based on acceptance-rejection, spherical-radial transformations and separation-of-variables transformations are considered. Tests using randomly chosen problems show that the most efficient numerical methods...

This paper compares methods for the numerical computation of multivariate t-probabilities for hyperrectangular integration regions. Methods based on acceptance-rejection, spherical-radial transformations and separation-of-variables transformations are considered. Tests using randomly chosen problems show that the most efficient numerical methods use a transformation developed by Genz (1992) for multivariate normal probabilities. These methods allow moderately accurate multivariate t-probabilities to be quickly computed for problems with as many as twenty variables. Methods for the non-central multivariate t-distribution are also described. Minimize

Contributors:

The Pennsylvania State University CiteSeerX Archives

Year of Publication:

2011-01-26

Source:

http://www.sci.wsu.edu/math/faculty/genz/papers/mvtcmp.ps

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text

Language:

en

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Imports MASS, stats License GPL (> = 2)

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Title Efficient design and analysis of factorial two-colour microarray data

Title Efficient design and analysis of factorial two-colour microarray data Minimize

Contributors:

The Pennsylvania State University CiteSeerX Archives

Year of Publication:

2014-12-03

Source:

http://www.bioconductor.org/packages/release/bioc/manuals/daMA/man/daMA.pdf

http://www.bioconductor.org/packages/release/bioc/manuals/daMA/man/daMA.pdf Minimize

Document Type:

text

Language:

en

Subjects:

Usage analyseMA ( data ; design ; id ; cmat ; cinfo ; padj=c("none ; bonferroni ; fdr ; tol=1e-06) Arguments

Usage analyseMA ( data ; design ; id ; cmat ; cinfo ; padj=c("none ; bonferroni ; fdr ; tol=1e-06) Arguments Minimize

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Title:

Numerical Computation Of Multivariate t-Probabilities With Application To Power Calculation Of Multiple Contrasts

Author:

Contributors:

The Pennsylvania State University CiteSeerX Archives

Year of Publication:

2011-01-26

Source:

http://www.sci.wsu.edu/math/faculty/genz/papers/mvt.ps

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Document Type:

text

Language:

en

Subjects:

Multivariate t-distribution ; Monte Carlo methods ; lattice rule algorithm ; calculation of power ; multiple contrast test

Multivariate t-distribution ; Monte Carlo methods ; lattice rule algorithm ; calculation of power ; multiple contrast test Minimize

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Imports MASS, stats License GPL (> = 2)

Description:

design and analysis of factorial two-colour microarray data

design and analysis of factorial two-colour microarray data Minimize

Contributors:

The Pennsylvania State University CiteSeerX Archives

Year of Publication:

2013-10-16

Source:

http://www.bioconductor.org/packages/2.13/bioc/manuals/daMA/man/daMA.pdf

http://www.bioconductor.org/packages/2.13/bioc/manuals/daMA/man/daMA.pdf Minimize

Document Type:

text

Language:

en

Subjects:

Usage analyseMA ( data ; design ; id ; cmat ; cinfo ; padj=c("none ; bonferroni ; fdr ; tol=1e-06) Arguments

Usage analyseMA ( data ; design ; id ; cmat ; cinfo ; padj=c("none ; bonferroni ; fdr ; tol=1e-06) Arguments Minimize

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