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Survival Analysis (Hardcover)
Loot Price: R3,056
Discovery Miles 30 560
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Survival Analysis (Hardcover)
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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 testing Regression
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 Focused
Penalized methods Survival trees and ensemble techniques of
bagging, boosting, and random survival forests A brief exposure of
neural networks for survival data R program illustration throughout
the book
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