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Munich, December 2009. The General Linear Model and the Generalized Singular Value Decomposition

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minor improvement have been made.

minor improvement have been made. Minimize

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The Pennsylvania State University CiteSeerX Archives

Year of Publication:

2013-08-13

Source:

http://epub.ub.uni-muenchen.de/9185/1/tr048.pdf

http://epub.ub.uni-muenchen.de/9185/1/tr048.pdf Minimize

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text

Language:

en

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of an orthogonal matrix

of an orthogonal matrix Minimize

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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:

l l l l 1 On the Accuracy of Statistical Distributions in Microsoft Excel 97

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The paper deals with the numerical accuracy of some statistical functions in Microsoft Excel

The paper deals with the numerical accuracy of some statistical functions in Microsoft Excel Minimize

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The Pennsylvania State University CiteSeerX Archives

Year of Publication:

2013-10-22

Source:

http://www.stat.uni-muenchen.de/~knuesel/elv/excelacc.pdf

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text

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en

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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:

Factor Analysis: Chisquare as Rotation Criterion

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Abstract: The rotation problem in factor analysis consists in finding an orthogonal transformation of the initial factor loadings so that the rotated loadings have a simple structure that can be easily interpreted. The most popular orthogonal transformations are the quartimax and varimax procedure with Kaiser normalization. In this paper we prop...

Abstract: The rotation problem in factor analysis consists in finding an orthogonal transformation of the initial factor loadings so that the rotated loadings have a simple structure that can be easily interpreted. The most popular orthogonal transformations are the quartimax and varimax procedure with Kaiser normalization. In this paper we propose the classical chisquare contingency measure as a rotation criterion. We think that this is a very natural and attractive criterion, not only for rotations but also for oblique transformations, that is not to be found in our popular statistical packages up to now. Minimize

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The Pennsylvania State University CiteSeerX Archives

Year of Publication:

2013-08-21

Source:

http://epub.ub.uni-muenchen.de/6350/1/tr040.pdf

http://epub.ub.uni-muenchen.de/6350/1/tr040.pdf Minimize

Document Type:

text

Language:

en

Subjects:

rotation criterion ; chisquare ; varimax ; quartimax ; oblique transformations

rotation criterion ; chisquare ; varimax ; quartimax ; oblique transformations Minimize

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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:

Alternatives to the MCMC method

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The Makov Chain Monte Carlo method (MCMC) is often used to generate independent (pseudo) random numbers from a distribution with a density that is known only up to a normalising constant. With the MCMC method it is not necessary to compute the normalising constant (see e.g. Tierney, 1994; Besag, 2000). In mis note we want to show that the well-k...

The Makov Chain Monte Carlo method (MCMC) is often used to generate independent (pseudo) random numbers from a distribution with a density that is known only up to a normalising constant. With the MCMC method it is not necessary to compute the normalising constant (see e.g. Tierney, 1994; Besag, 2000). In mis note we want to show that the well-known acceptance-rejection algorithm also works with unnormalised densities, and so this algorithm can be a much simpler alternative to the MCMC method in many cases. Minimize

Publisher:

Helmut-Schmidt-Universität, Universität der Bundeswehr ; Fakultät Wirtschafts- und Sozialwissenschaften. Institut für Statistik und Quantitative Ökonomik

Year of Publication:

2002

Source:

Discussion papers in statistics and quantitative economics ; 97

Discussion papers in statistics and quantitative economics ; 97 Minimize

Document Type:

ResearchPaper

Language:

eng

Subjects:

General statistics

General statistics Minimize

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http://edoc.sub.uni-hamburg.de/hsu/doku/urheberrecht.php

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Title:

Alternatives to the MCMC method

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Description:

The Makov Chain Monte Carlo method (MCMC) is often used to generate independent (pseudo) random numbers from a distribution with a density that is known only up to a normalising constant. With the MCMC method it is not necessary to compute the normalising constant (see e.g. Tierney, 1994; Besag, 2000). In this paper we show that the well-known a...

The Makov Chain Monte Carlo method (MCMC) is often used to generate independent (pseudo) random numbers from a distribution with a density that is known only up to a normalising constant. With the MCMC method it is not necessary to compute the normalising constant (see e.g. Tierney, 1994; Besag, 2000). In this paper we show that the well-known acceptance-rejection algorithm also works with unnormalised densities, and so this algorithm can be used to confirm the results of the MCMC method in simple cases. We present an example with real data. Minimize

Publisher:

Techn. Univ.; Sonderforschungsbereich 386, Statistische Analyse Diskreter Strukturen München

Year of Publication:

2003

Document Type:

doc-type:workingPaper

Language:

eng

Subjects:

ddc:310

ddc:310 Minimize

Rights:

http://www.econstor.eu/dspace/Nutzungsbedingungen

http://www.econstor.eu/dspace/Nutzungsbedingungen Minimize

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Discussion paper // Sonderforschungsbereich 386 der Ludwig-Maximilians-Universität München 367

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Title:

Singulärwert-Zerlegung und Methode der kleinsten Quadrate

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The method of least squares is an important instrument to determine the optimal linear estimators in regression models. By means of the singular value decomposition we can find the least squares estimators without differentiation, without solving the normal equations and without assumptions on the rank of the data matrix. Even in case of multico...

The method of least squares is an important instrument to determine the optimal linear estimators in regression models. By means of the singular value decomposition we can find the least squares estimators without differentiation, without solving the normal equations and without assumptions on the rank of the data matrix. Even in case of multicollinearity we can find the simple and natural solutions. The results in the paper are not new, they have been developed mainly in numerical publications, but they are hardly to be found in statistical textbooks. Minimize

Year of Publication:

2008-10-20

Document Type:

doc-type:workingPaper ; Paper ; NonPeerReviewed

Language:

deu

Subjects:

Technische Reports

Technische Reports Minimize

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http://epub.ub.uni-muenchen.de/4400/5/tr031.pdf ; Knüsel, Leo (2008): Singulärwert-Zerlegung und Methode der kleinsten Quadrate. Department of Statistics: Technical Reports, Nr. 31

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Title:

Factor Analysis: Chisquare as Rotation Criterion

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The rotation problem in factor analysis consists in finding an orthogonal transformation of the initial factor loadings so that the rotated loadings have a simple structure that can be easily interpreted. The most popular orthogonal transformations are the quartimax and varimax procedure with Kaiser normalization. In this paper we propose the cl...

The rotation problem in factor analysis consists in finding an orthogonal transformation of the initial factor loadings so that the rotated loadings have a simple structure that can be easily interpreted. The most popular orthogonal transformations are the quartimax and varimax procedure with Kaiser normalization. In this paper we propose the classical chisquare contingency measure as a rotation criterion. We think that this is a very natural and attractive criterion, not only for rotations but also for oblique transformations, that is not to be found in our popular statistical packages up to now. Minimize

Year of Publication:

2008-01-01

Document Type:

doc-type:workingPaper ; Paper ; NonPeerReviewed

Language:

eng

Subjects:

Technische Reports

Technische Reports Minimize

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http://epub.ub.uni-muenchen.de/6350/1/tr040.pdf ; Knüsel, Leo (2008): Factor Analysis: Chisquare as Rotation Criterion. Department of Statistics: Technical Reports, Nr. 40

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Title:

The General Linear Model and the Generalized Singular Value Decomposition

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The general linear model with correlated error variables can be transformed by means of the generalized singular value decomposition to a very simple model (canonical form) where the least squares solution is obvious. The method works also if X and the covariance matrix of the error variables do not have full rank or are nearly rank deficient (r...

The general linear model with correlated error variables can be transformed by means of the generalized singular value decomposition to a very simple model (canonical form) where the least squares solution is obvious. The method works also if X and the covariance matrix of the error variables do not have full rank or are nearly rank deficient (rank-k approximation). By backtransformation one obtains the solution for the original model. Minimize

Year of Publication:

2009-01-01

Document Type:

doc-type:workingPaper ; Paper ; NonPeerReviewed

Language:

eng

Subjects:

Technische Reports ; ddc:510

Technische Reports ; ddc:510 Minimize

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http://epub.ub.uni-muenchen.de/9185/1/tr048.pdf ; Knüsel, Leo (2009): The General Linear Model and the Generalized Singular Value Decomposition. Department of Statistics: Technical Reports, Nr. 48

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Title:

The General Linear Model and the Generalized Singular Value Decomposition; Some Examples

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The general linear model with correlated error variables can be transformed by means of the generalized singular value decomposition to a very simple model (canonical form) where the least squares solution is obvious. The method works also if X and the covariance matrix of the error variables do not have full rank or are nearly rank deficient (r...

The general linear model with correlated error variables can be transformed by means of the generalized singular value decomposition to a very simple model (canonical form) where the least squares solution is obvious. The method works also if X and the covariance matrix of the error variables do not have full rank or are nearly rank deficient (rank-k approximation). By backtransformation one obtains the solution for the original model. In this paper we demonstrate the method with some examples. Minimize

Year of Publication:

2009-01-01

Document Type:

doc-type:workingPaper ; Paper ; NonPeerReviewed

Language:

eng

Subjects:

Technische Reports ; ddc:510

Technische Reports ; ddc:510 Minimize

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http://epub.ub.uni-muenchen.de/9186/2/tr049.pdf ; Knüsel, Leo (2009): The General Linear Model and the Generalized Singular Value Decomposition; Some Examples. Department of Statistics: Technical Reports, Nr. 49

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Title:

Beiträge zu den Theoremen von KOLMOGOROV und SMIRNOV.

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Year of Publication:

1965/66

Source:

Knüsel, Leo;: Commentarii Mathematici Helvetici. 40 1965/66

Document Type:

Text

Subjects:

510.Mathematics

510.Mathematics Minimize

Rights:

Mathematics ; Open Access

Mathematics ; Open Access Minimize

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Commentarii Mathematici Helvetici. 1965/66

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