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Synopsis
The aim of survival analysis is to explain and predict the survival, usually defined along the time domain. In this work we study it by means of regression models. In statistical data analysis it is common to consider the regression set up in which a given response variable depends on some factors and/or covariates. The model selection problem mainly consists in choosing the covariates which better explain the dependent variable in a precise and hopefully fast manner. This process usually has several steps: the first one is to collect considerations from an expert about the set of covariates, then the statistician derives a prior on model parameters and constructs a tool to solve the model selection problem. We consider the model selection problem in survival analysis when the response variable is the time to event. Under an objective Bayesian approach, some commonly used tools in literature are the Intrinsic Bayes factor (IBF) and the Fractional Bayes factor (FBF). In this thesis we deal with the variable selection problem for censored data.
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Présentation de l'éditeur
The aim of survival analysis is to explain and predict the survival, usually defined along the time domain. In this work we study it by means of regression models. In statistical data analysis it is common to consider the regression set up in which a given response variable depends on some factors and/or covariates. The model selection problem mainly consists in choosing the covariates which better explain the dependent variable in a precise and hopefully fast manner. This process usually has several steps: the first one is to collect considerations from an expert about the set of covariates, then the statistician derives a prior on model parameters and constructs a tool to solve the model selection problem. We consider the model selection problem in survival analysis when the response variable is the time to event. Under an objective Bayesian approach, some commonly used tools in literature are the Intrinsic Bayes factor (IBF) and the Fractional Bayes factor (FBF). In this thesis we deal with the variable selection problem for censored data.
Biographie de l'auteur
Silvia Perra was born in Cagliari in 1985. In 2007 she graduated with honors in Mathematics and in 2009 she completed a master in Mathematics (University of Cagliari). In 2013 she obtained her PhD in Computer Science (University of Cagliari) with a thesis in Statistics. She currently performs data analysis for clinicians and she is doing research.
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Objective Bayesian Variable Selection for Censored Data
Edité par
LAP LAMBERT Academic Publishing, 2013
ISBN 10 : 365942451X
ISBN 13 : 9783659424519
Neuf
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Vendeur : moluna, Greven, Allemagne
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Etat : New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. Autor/Autorin: Perra SilviaSilvia Perra was born in Cagliari in 1985. In 2007 she graduated with honors in Mathematics and in 2009 she completed a master in Mathematics (University of Cagliari). In 2013 she obtained her PhD in Computer Science (Uni. N° de réf. du vendeur 5155412
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Objective Bayesian Variable Selection for Censored Data
Edité par
LAP LAMBERT Academic Publishing Aug 2013, 2013
ISBN 10 : 365942451X
ISBN 13 : 9783659424519
Neuf
Taschenbuch
Vendeur : BuchWeltWeit Ludwig Meier e.K., Bergisch Gladbach, Allemagne
Évaluation du vendeur 5 sur 5 étoiles
Taschenbuch. Etat : Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -The aim of survival analysis is to explain and predict the survival, usually defined along the time domain. In this work we study it by means of regression models. In statistical data analysis it is common to consider the regression set up in which a given response variable depends on some factors and/or covariates. The model selection problem mainly consists in choosing the covariates which better explain the dependent variable in a precise and hopefully fast manner. This process usually has several steps: the first one is to collect considerations from an expert about the set of covariates, then the statistician derives a prior on model parameters and constructs a tool to solve the model selection problem. We consider the model selection problem in survival analysis when the response variable is the time to event. Under an objective Bayesian approach, some commonly used tools in literature are the Intrinsic Bayes factor (IBF) and the Fractional Bayes factor (FBF). In this thesis we deal with the variable selection problem for censored data. 176 pp. Englisch. N° de réf. du vendeur 9783659424519
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Objective Bayesian Variable Selection for Censored Data
Edité par
LAP LAMBERT Academic Publishing, 2013
ISBN 10 : 365942451X
ISBN 13 : 9783659424519
Neuf
Taschenbuch
Vendeur : AHA-BUCH GmbH, Einbeck, Allemagne
Évaluation du vendeur 5 sur 5 étoiles
Taschenbuch. Etat : Neu. nach der Bestellung gedruckt Neuware - Printed after ordering - The aim of survival analysis is to explain and predict the survival, usually defined along the time domain. In this work we study it by means of regression models. In statistical data analysis it is common to consider the regression set up in which a given response variable depends on some factors and/or covariates. The model selection problem mainly consists in choosing the covariates which better explain the dependent variable in a precise and hopefully fast manner. This process usually has several steps: the first one is to collect considerations from an expert about the set of covariates, then the statistician derives a prior on model parameters and constructs a tool to solve the model selection problem. We consider the model selection problem in survival analysis when the response variable is the time to event. Under an objective Bayesian approach, some commonly used tools in literature are the Intrinsic Bayes factor (IBF) and the Fractional Bayes factor (FBF). In this thesis we deal with the variable selection problem for censored data. N° de réf. du vendeur 9783659424519
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Objective Bayesian Variable Selection for Censored Data
Edité par
LAP LAMBERT Academic Publishing Aug 2013, 2013
ISBN 10 : 365942451X
ISBN 13 : 9783659424519
Neuf
Taschenbuch
Vendeur : buchversandmimpf2000, Emtmannsberg, BAYE, Allemagne
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Taschenbuch. Etat : Neu. This item is printed on demand - Print on Demand Titel. Neuware -The aim of survival analysis is to explain and predict the survival, usually defined along the time domain. In this work we study it by means of regression models. In statistical data analysis it is common to consider the regression set up in which a given response variable depends on some factors and/or covariates. The model selection problem mainly consists in choosing the covariates which better explain the dependent variable in a precise and hopefully fast manner. This process usually has several steps: the first one is to collect considerations from an expert about the set of covariates, then the statistician derives a prior on model parameters and constructs a tool to solve the model selection problem. We consider the model selection problem in survival analysis when the response variable is the time to event. Under an objective Bayesian approach, some commonly used tools in literature are the Intrinsic Bayes factor (IBF) and the Fractional Bayes factor (FBF). In this thesis we deal with the variable selection problem for censored data.Books on Demand GmbH, Überseering 33, 22297 Hamburg 176 pp. Englisch. N° de réf. du vendeur 9783659424519
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Objective Bayesian Variable Selection for Censored Data
Edité par
VDM Verlag Dr. Mueller Aktiengesellschaft & Co. KG, 2013
ISBN 10 : 365942451X
ISBN 13 : 9783659424519
Neuf
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Vendeur : Books Puddle, New York, NY, Etats-Unis
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Etat : New. pp. 176. N° de réf. du vendeur 26128789670
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Objective Bayesian Variable Selection for Censored Data
Edité par
VDM Verlag Dr. Mueller Aktiengesellschaft & Co. KG, 2013
ISBN 10 : 365942451X
ISBN 13 : 9783659424519
Neuf
Couverture souple
Vendeur : Majestic Books, Hounslow, Royaume-Uni
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Etat : New. Print on Demand pp. 176 2:B&W 6 x 9 in or 229 x 152 mm Perfect Bound on Creme w/Gloss Lam. N° de réf. du vendeur 131765113
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Objective Bayesian Variable Selection for Censored Data
Edité par
VDM Verlag Dr. Mueller Aktiengesellschaft & Co. KG, 2013
ISBN 10 : 365942451X
ISBN 13 : 9783659424519
Neuf
Couverture souple
Vendeur : Biblios, Frankfurt am main, HESSE, Allemagne
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Etat : New. PRINT ON DEMAND pp. 176. N° de réf. du vendeur 18128789676
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