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Condizione: As New. Unread book in perfect condition.

Condizione: New.

Condizione: New.

Condizione: As New. Unread book in perfect condition.

Condizione: New.

Hardback. Condizione: New. New copy - Usually dispatched within 4 working days.

Condizione: New. In English.

Hardcover. Condizione: Brand New. 296 pages. 9.18x6.12x0.87 inches. In Stock.

Gebunden. Condizione: New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. Prabhanjan Narayanachar Tattar is working as a Lead Data Scientist at British American Tobacco company, Malaysia. The author has published several books in Statistics: A Course in Statistics with R (Wiley), Statistical Application Develo.…

Hardback. Condizione: New. This item is printed on demand. New copy - Usually dispatched within 5-9 working days 185.

Buch. Condizione: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -Survival analysis generally deals with analysis of data arising from clinical trials. Censoring, truncation, and missing data create analytical challenges and the statistical methods and inference require novel and different approaches for analysis. Statistical properties, essentially asymptotic ones, of the estimators and tests are aptly handled in the counting process framework which is drawn from the larger arm of stochastic calculus. With explosion of data generation during the past two decades, survival data has also enlarged assuming a gigantic size. Most statistical methods developed before the millennium were based on a linear approach even in the face of complex nature of survival data. Nonparametric nonlinear methods are best envisaged in the Machine Learning school. This book attempts to cover all these aspects in a concise way.Survival Analysis offers an integrated blend of statistical methods and machine learning useful in analysis of survival data. The purpose of the offering is to give an exposure to the machine learning trends for lifetime data analysis.Features:Classical survival analysis techniques for estimating statistical functional and hypotheses testingRegression methods covering the popular Cox relative risk regression model, Aalen's additive hazards model, etc.Information criteria to facilitate model selection including Akaike, Bayes, and FocusedPenalized methodsSurvival trees and ensemble techniques of bagging, boosting, and random survival forestsA brief exposure of neural networks for survival dataR program illustration throughout the book 304 pp. Englisch.…

Buch. Condizione: Neu. nach der Bestellung gedruckt Neuware - Printed after ordering - Survival analysis generally deals with analysis of data arising from clinical trials. Censoring, truncation, and missing data create analytical challenges and the statistical methods and inference require novel and different approaches for analysis. Statistical properties, essentially asymptotic ones, of the estimators and tests are aptly handled in the counting process framework which is drawn from the larger arm of stochastic calculus. With explosion of data generation during the past two decades, survival data has also enlarged assuming a gigantic size. Most statistical methods developed before the millennium were based on a linear approach even in the face of complex nature of survival data. Nonparametric nonlinear methods are best envisaged in the Machine Learning school. This book attempts to cover all these aspects in a concise way.Survival Analysis offers an integrated blend of statistical methods and machine learning useful in analysis of survival data. The purpose of the offering is to give an exposure to the machine learning trends for lifetime data analysis.Features:Classical survival analysis techniques for estimating statistical functional and hypotheses testingRegression methods covering the popular Cox relative risk regression model, Aalen's additive hazards model, etc.Information criteria to facilitate model selection including Akaike, Bayes, and FocusedPenalized methodsSurvival trees and ensemble techniques of bagging, boosting, and random survival forestsA brief exposure of neural networks for survival dataR program illustration throughout the book.…