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Sinopsis
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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Reseña del editor
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.
Biografía del autor
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
Publicado por
LAP LAMBERT Academic Publishing Aug 2013, 2013
ISBN 10: 365942451X
ISBN 13: 9783659424519
Nuevo
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Librería: BuchWeltWeit Ludwig Meier e.K., Bergisch Gladbach, Alemania
Calificación del vendedor: 5 de 5 estrellas
Taschenbuch. Condición: 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. Nº de ref. del artículo: 9783659424519
Cantidad disponible: 2 disponibles
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Objective Bayesian Variable Selection for Censored Data
Publicado por
LAP LAMBERT Academic Publishing, 2013
ISBN 10: 365942451X
ISBN 13: 9783659424519
Nuevo
Tapa blanda
Librería: moluna, Greven, Alemania
Calificación del vendedor: 5 de 5 estrellas
Condición: 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. Nº de ref. del artículo: 5155412
Cantidad disponible: Más de 20 disponibles
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Objective Bayesian Variable Selection for Censored Data
Publicado por
LAP LAMBERT Academic Publishing, 2013
ISBN 10: 365942451X
ISBN 13: 9783659424519
Nuevo
Taschenbuch
Librería: AHA-BUCH GmbH, Einbeck, Alemania
Calificación del vendedor: 5 de 5 estrellas
Taschenbuch. Condición: 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. Nº de ref. del artículo: 9783659424519
Cantidad disponible: 1 disponibles
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Objective Bayesian Variable Selection for Censored Data
Publicado por
LAP LAMBERT Academic Publishing Aug 2013, 2013
ISBN 10: 365942451X
ISBN 13: 9783659424519
Nuevo
Taschenbuch
Librería: buchversandmimpf2000, Emtmannsberg, BAYE, Alemania
Calificación del vendedor: 5 de 5 estrellas
Taschenbuch. Condición: 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. Nº de ref. del artículo: 9783659424519
Cantidad disponible: 1 disponibles
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Objective Bayesian Variable Selection for Censored Data
Publicado por
VDM Verlag Dr. Mueller Aktiengesellschaft & Co. KG, 2013
ISBN 10: 365942451X
ISBN 13: 9783659424519
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Librería: Books Puddle, New York, NY, Estados Unidos de America
Calificación del vendedor: 4 de 5 estrellas
Condición: New. pp. 176. Nº de ref. del artículo: 26128789670
Cantidad disponible: 4 disponibles
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Objective Bayesian Variable Selection for Censored Data
Publicado por
VDM Verlag Dr. Mueller Aktiengesellschaft & Co. KG, 2013
ISBN 10: 365942451X
ISBN 13: 9783659424519
Nuevo
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Librería: Majestic Books, Hounslow, Reino Unido
Calificación del vendedor: 5 de 5 estrellas
Condición: 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. Nº de ref. del artículo: 131765113
Cantidad disponible: 4 disponibles
Imagen de archivo
Objective Bayesian Variable Selection for Censored Data
Publicado por
VDM Verlag Dr. Mueller Aktiengesellschaft & Co. KG, 2013
ISBN 10: 365942451X
ISBN 13: 9783659424519
Nuevo
Tapa blanda
Librería: Biblios, Frankfurt am main, HESSE, Alemania
Calificación del vendedor: 4 de 5 estrellas
Condición: New. PRINT ON DEMAND pp. 176. Nº de ref. del artículo: 18128789676
Cantidad disponible: 4 disponibles