Statistical analysis high dimensional data (23 risultati)
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Paperback. Condizione: new. Paperback. In regression the objective is to determine an appropriate function which reflects reality as accurate as possible but also eliminates irregularities from data noise and is therefore easy to interpret. A popular and flexible approach for estimating the true underlying function is the additi…ve model. One possible approach for fitting additive models is the expansion in B-splines which allows direct calculation of the estimators. If the number of B-splines is too large the estimated functions become wiggly and tend to be very close to the observed data. To avoid this problem of overfitting we use a penalization approach characterized by smoothing parameters. In this thesis we propose the use of genetic algorithms for smoothing parameter optimization. Genetic algorithms are rarely applied in the field of statistics and refer to the principle that better adapted individuals win against their competitors under equal conditions. Apart from smoothing parameter optimization the user often faces datasets containing large numbers of relevant and irrelevant explanatory variables.Appropriate variable selection approaches allow to reduce the number of variables to subsets of relevant variables. We propose to consider the problems of variable selection and choice of smoothing parameters simultaneously by using genetic algorithms. Our approach bases on an appropriate combination of the genetic algorithms for smoothing parameter optimization and variable selection. Shipping may be from multiple locations in the US or from the UK, depending on stock availability.
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Paperback. Condizione: new. Paperback. In regression the objective is to determine an appropriate function which reflects reality as accurate as possible but also eliminates irregularities from data noise and is therefore easy to interpret. A popular and flexible approach for estimating the true underlying function is the additi…ve model. One possible approach for fitting additive models is the expansion in B-splines which allows direct calculation of the estimators. If the number of B-splines is too large the estimated functions become wiggly and tend to be very close to the observed data. To avoid this problem of overfitting we use a penalization approach characterized by smoothing parameters. In this thesis we propose the use of genetic algorithms for smoothing parameter optimization. Genetic algorithms are rarely applied in the field of statistics and refer to the principle that better adapted individuals win against their competitors under equal conditions. Apart from smoothing parameter optimization the user often faces datasets containing large numbers of relevant and irrelevant explanatory variables.Appropriate variable selection approaches allow to reduce the number of variables to subsets of relevant variables. We propose to consider the problems of variable selection and choice of smoothing parameters simultaneously by using genetic algorithms. Our approach bases on an appropriate combination of the genetic algorithms for smoothing parameter optimization and variable selection. Shipping may be from our Sydney, NSW warehouse or from our UK or US warehouse, depending on stock availability.
Lingua: Inglese
Editore: Springer 2018
Serie: Abel Symposia, Libro 11 di 15. Libro 11 di 15 - Abel Symposia
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Editore: Springer 2016
Serie: Abel Symposia, Libro 11 di 15. Libro 11 di 15 - Abel Symposia
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Statistical Analysis for High-dimensional Data : The Abel Symposium 2014
Frigessi, Arnoldo (EDT); Bühlmann, Peter (EDT); Glad, Ingrid (EDT); Langaas, Mette (EDT); Richardson, Sylvia (EDT)
Lingua: Inglese
Editore: Springer 2018
Serie: Abel Symposia, Libro 11 di 15. Libro 11 di 15 - Abel Symposia
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Statistical Analysis for High-dimensional Data : The Abel Symposium 2014
Frigessi, Arnoldo (EDT); Bühlmann, Peter (EDT); Glad, Ingrid K. (EDT); Langaas, Mette (EDT); Richardson, Sylvia (EDT)
Lingua: Inglese
Editore: Springer 2016
Serie: Abel Symposia, Libro 11 di 15. Libro 11 di 15 - Abel Symposia
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Statistical Analysis for High-dimensional Data : The Abel Symposium 2014
Frigessi, Arnoldo (EDT); Bühlmann, Peter (EDT); Glad, Ingrid (EDT); Langaas, Mette (EDT); Richardson, Sylvia (EDT)
Lingua: Inglese
Editore: Springer 2018
Serie: Abel Symposia, Libro 11 di 15. Libro 11 di 15 - Abel Symposia
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Statistical Analysis for High-Dimensional Data
Frigessi, Arnoldo|Bühlmann, Peter|Glad, Ingrid|Langaas, Mette|Richardson, Sylvia|Vannucci, Marina
Lingua: Inglese
Editore: Springer International Publishing 2018
Serie: Abel Symposia, Libro 11 di 15. Libro 11 di 15 - Abel Symposia
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Statistical Analysis for High Dimensional Data
Frigessi, Arnoldo|Bühlmann, Peter|Glad, Ingrid K.|Langaas, Mette|Richardson, Sylvia|Vannucci, Marina
Lingua: Inglese
Editore: Springer International Publishing 2016
Serie: Abel Symposia, Libro 11 di 15. Libro 11 di 15 - Abel Symposia
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Lingua: Inglese
Editore: Springer 2018
Serie: Abel Symposia, Libro 11 di 15. Libro 11 di 15 - Abel Symposia
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Condizione: New. pp. 318.
Lingua: Inglese
Editore: Springer International Publishing, Springer International Publishing 2018
Serie: Abel Symposia, Libro 11 di 15. Libro 11 di 15 - Abel Symposia
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Taschenbuch. Condizione: Neu. Druck auf Anfrage Neuware - Printed after ordering - This book features research contributions fromThe Abel Symposium on Statistical Analysis for High Dimensional Data, held inNyvågar, Lofoten, Norway, in May 2014.The focus of the symposium was on statisticaland machine learning methodologies specif…ically developed for inference in 'bigdata' situations, with particular reference to genomic applications. Thecontributors, who are among the most prominent researchers on the theory ofstatistics for high dimensional inference, present new theories and methods, aswell as challenging applications and computational solutions. Specific themesinclude, among others, variable selection and screening, penalised regression,sparsity, thresholding, low dimensional structures, computational challenges,non-convex situations, learning graphical models, sparse covariance andprecision matrices, semi- and non-parametric formulations, multiple testing,classification, factor models, clustering, and preselection.Highlighting cutting-edge researchand casting light on future research directions, the contributions will benefitgraduate students and researchers in computational biology, statistics and themachine learning community.
Lingua: Inglese
Editore: Springer International Publishing 2016
Serie: Abel Symposia, Libro 11 di 15. Libro 11 di 15 - Abel Symposia
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Da: AHA-BUCH GmbH, Einbeck, GermaniaAHA-BUCH GmbH
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Buch. Condizione: Neu. Druck auf Anfrage Neuware - Printed after ordering - This book features research contributions fromThe Abel Symposium on Statistical Analysis for High Dimensional Data, held inNyvågar, Lofoten, Norway, in May 2014.The focus of the symposium was on statisticaland machine learning methodologies specifically…developed for inference in 'bigdata' situations, with particular reference to genomic applications. Thecontributors, who are among the most prominent researchers on the theory ofstatistics for high dimensional inference, present new theories and methods, aswell as challenging applications and computational solutions. Specific themesinclude, among others, variable selection and screening, penalised regression,sparsity, thresholding, low dimensional structures, computational challenges,non-convex situations, learning graphical models, sparse covariance andprecision matrices, semi- and non-parametric formulations, multiple testing,classification, factor models, clustering, and preselection.Highlighting cutting-edge researchand casting light on future research directions, the contributions will benefitgraduate students and researchers in computational biology, statistics and themachine learning community.
Lingua: Inglese
Editore: Springer 2016
Serie: Abel Symposia, Libro 11 di 15. Libro 11 di 15 - Abel Symposia
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Lingua: Inglese
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Lingua: Inglese
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Statistical Analysis for High-dimensional Data : The Abel Symposium 2014
Frigessi, Arnoldo (EDT); Bühlmann, Peter (EDT); Glad, Ingrid K. (EDT); Langaas, Mette (EDT); Richardson, Sylvia (EDT)
Lingua: Inglese
Editore: Springer 2016
Serie: Abel Symposia, Libro 11 di 15. Libro 11 di 15 - Abel Symposia
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Taschenbuch. Condizione: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -This book features research contributions fromThe Abel Symposium on Statistical Analysis for High Dimensional Data, held inNyvågar, Lofoten, Norway, in May 2014.The focus of the symposium was on statisticaland machine learning meth…odologies specifically developed for inference in 'bigdata' situations, with particular reference to genomic applications. Thecontributors, who are among the most prominent researchers on the theory ofstatistics for high dimensional inference, present new theories and methods, aswell as challenging applications and computational solutions. Specific themesinclude, among others, variable selection and screening, penalised regression,sparsity, thresholding, low dimensional structures, computational challenges,non-convex situations, learning graphical models, sparse covariance andprecision matrices, semi- and non-parametric formulations, multiple testing,classification, factor models, clustering, and preselection.Highlighting cutting-edge researchand casting light on future research directions, the contributions will benefitgraduate students and researchers in computational biology, statistics and themachine learning community. 320 pp. Englisch.
Lingua: Inglese
Editore: Springer International Publishing Feb 2016 2016
Serie: Abel Symposia, Libro 11 di 15. Libro 11 di 15 - Abel Symposia
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Da: BuchWeltWeit Ludwig Meier e.K., Bergisch Gladbach, , GermaniaBuchWeltWeit Ludwig Meier e.K.
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Buch. Condizione: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -This book features research contributions fromThe Abel Symposium on Statistical Analysis for High Dimensional Data, held inNyvågar, Lofoten, Norway, in May 2014.The focus of the symposium was on statisticaland machine learning methodologi…es specifically developed for inference in 'bigdata' situations, with particular reference to genomic applications. Thecontributors, who are among the most prominent researchers on the theory ofstatistics for high dimensional inference, present new theories and methods, aswell as challenging applications and computational solutions. Specific themesinclude, among others, variable selection and screening, penalised regressionsparsity, thresholding, low dimensional structures, computational challengesnon-convex situations, learning graphical models, sparse covariance andprecision matrices, semi- and non-parametric formulations, multiple testingclassification, factor models, clustering, and preselection.Highlighting cutting-edge researchand casting light on future research directions, the contributions will benefitgraduate students and researchers in computational biology, statistics and themachine learning community. 320 pp. Englisch.
Lingua: Inglese
Editore: Springer 2018
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Condizione: New. Print on Demand pp. 318.
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Serie: Abel Symposia, Libro 11 di 15. Libro 11 di 15 - Abel Symposia
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Da: buchversandmimpf2000, Emtmannsberg, BAYE, Germaniabuchversandmimpf2000
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Buch. Condizione: Neu. This item is printed on demand - Print on Demand Titel. Neuware -This book features research contributions fromThe Abel Symposium on Statistical Analysis for High Dimensional Data, held inNyvågar, Lofoten, Norway, in May 2014.The focus of the symposium was on statisticaland machine learning methodologies s…pecifically developed for inference in ¿bigdatä situations, with particular reference to genomic applications. Thecontributors, who are among the most prominent researchers on the theory ofstatistics for high dimensional inference, present new theories and methods, aswell as challenging applications and computational solutions. Specific themesinclude, among others, variable selection and screening, penalised regressionsparsity, thresholding, low dimensional structures, computational challengesnon-convex situations, learning graphical models, sparse covariance andprecision matrices, semi- and non-parametric formulations, multiple testingclassification, factor models, clustering, and preselection.Highlighting cutting-edge researchand casting light on future research directions, the contributions will benefitgraduate students and researchers in computational biology, statistics and themachine learning community.Springer-Verlag KG, Sachsenplatz 4-6, 1201 Wien 320 pp. Englisch.
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Editore: Springer, Springer International Publishing Mär 2018 2018
Serie: Abel Symposia, Libro 11 di 15. Libro 11 di 15 - Abel Symposia
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Taschenbuch. Condizione: Neu. This item is printed on demand - Print on Demand Titel. Neuware -This book features research contributions fromThe Abel Symposium on Statistical Analysis for High Dimensional Data, held inNyvågar, Lofoten, Norway, in May 2014.The focus of the symposium was on statisticaland machine learning methodol…ogies specifically developed for inference in ¿bigdatä situations, with particular reference to genomic applications. Thecontributors, who are among the most prominent researchers on the theory ofstatistics for high dimensional inference, present new theories and methods, aswell as challenging applications and computational solutions. Specific themesinclude, among others, variable selection and screening, penalised regressionsparsity, thresholding, low dimensional structures, computational challengesnon-convex situations, learning graphical models, sparse covariance andprecision matrices, semi- and non-parametric formulations, multiple testingclassification, factor models, clustering, and preselection.Highlighting cutting-edge researchand casting light on future research directions, the contributions will benefitgraduate students and researchers in computational biology, statistics and themachine learning community.Springer-Verlag KG, Sachsenplatz 4-6, 1201 Wien 320 pp. Englisch.
Lingua: Inglese
Editore: Springer 2018
Serie: Abel Symposia, Libro 11 di 15. Libro 11 di 15 - Abel Symposia
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Condizione: New. PRINT ON DEMAND pp. 318.
