دانلود کتاب تجزیه و تحلیل بقا

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 estima-tors and tests are aptly handled in the counting process framework which is drawn from the larger arm of stochastic calculus. With the 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 the complex nature of survival data. Nonparametric nonlinear methods, commonly employed in statistical inference for analysis of sur-vival data are best envisaged in the Machine Learning setting. 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 the analysis of survival data. A special feature of the offering is to give an exposure to the machine learn-ing trends for lifetime data analysis.