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Publicado por Auerbach Publications 10/8/2024, 2024
ISBN 10: 1032073675 ISBN 13: 9781032073675
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Publicado por Taylor & Francis Ltd, London, 2024
ISBN 10: 1032073675 ISBN 13: 9781032073675
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Añadir al carritoPaperback. Condición: new. Paperback. Survival analysis uses statistics to calculate time to failure. Survival Analysis with Python takes a fresh look at this complex subject by explaining how to use the Python programming language to perform this type of analysis. As the subject itself is very mathematical and full of expressions and formulations, the book provides detailed explanations and examines practical implications. The book begins with an overview of the concepts underpinning statistical survival analysis. It then delves into Parametric models with coverage of Concept of maximum likelihood estimate (MLE) of a probability distribution parameter MLE of the survival function Common probability distributions and their analysis Analysis of exponential distribution as a survival function Analysis of Weibull distribution as a survival function Derivation of Gumbel distribution as a survival function from Weibull Non-parametric models including KaplanMeier (KM) estimator, a derivation of expression using MLE Fitting KM estimator with an example dataset, Python code and plotting curves Greenwoods formula and its derivation Models with covariates explaining The concept of time shift and the accelerated failure time (AFT) model Weibull-AFT model and derivation of parameters by MLE Proportional Hazard (PH) model Cox-PH model and Breslows method Significance of covariates Selection of covariates The Python lifelines library is used for coding examples. By mapping theory to practical examples featuring datasets, this book is a hands-on tutorial as well as a handy reference. Survival analysis uses statistics to calculate time to failure. The book takes a fresh look at this complex subject by explaining how to use the Python programming language to perform this type of analysis. Shipping may be from multiple locations in the US or from the UK, depending on stock availability.
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ISBN 10: 1032967358 ISBN 13: 9781032967356
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Idioma: Inglés
Publicado por Auerbach Publications 2021-12-17, 2021
ISBN 10: 1032148268 ISBN 13: 9781032148267
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Añadir al carritoTaschenbuch. Condición: Neu. Survival Analysis with Python | Avishek Nag | Taschenbuch | Einband - flex.(Paperback) | Englisch | 2024 | Auerbach Publications | EAN 9781032073675 | Verantwortliche Person für die EU: Libri GmbH, Europaallee 1, 36244 Bad Hersfeld, gpsr[at]libri[dot]de | Anbieter: preigu.
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Publicado por Taylor & Francis Ltd, London, 2024
ISBN 10: 1032073675 ISBN 13: 9781032073675
Librería: AussieBookSeller, Truganina, VIC, Australia
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Añadir al carritoPaperback. Condición: new. Paperback. Survival analysis uses statistics to calculate time to failure. Survival Analysis with Python takes a fresh look at this complex subject by explaining how to use the Python programming language to perform this type of analysis. As the subject itself is very mathematical and full of expressions and formulations, the book provides detailed explanations and examines practical implications. The book begins with an overview of the concepts underpinning statistical survival analysis. It then delves into Parametric models with coverage of Concept of maximum likelihood estimate (MLE) of a probability distribution parameter MLE of the survival function Common probability distributions and their analysis Analysis of exponential distribution as a survival function Analysis of Weibull distribution as a survival function Derivation of Gumbel distribution as a survival function from Weibull Non-parametric models including KaplanMeier (KM) estimator, a derivation of expression using MLE Fitting KM estimator with an example dataset, Python code and plotting curves Greenwoods formula and its derivation Models with covariates explaining The concept of time shift and the accelerated failure time (AFT) model Weibull-AFT model and derivation of parameters by MLE Proportional Hazard (PH) model Cox-PH model and Breslows method Significance of covariates Selection of covariates The Python lifelines library is used for coding examples. By mapping theory to practical examples featuring datasets, this book is a hands-on tutorial as well as a handy reference. Survival analysis uses statistics to calculate time to failure. The book takes a fresh look at this complex subject by explaining how to use the Python programming language to perform this type of analysis. This item is printed on demand. Shipping may be from our Sydney, NSW warehouse or from our UK or US warehouse, depending on stock availability.