Survival Analysis
Efficient Nonparametric Curve Estimation For Censored Data with R Examples
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Beschreibung
This textbook provides a unified account of estimating the survival function, hazard rate, cumulative hazard, density, regression, conditional distributions, and linear functionals for the current status censored and right-censored data. The book contains the theory and methodology of efficient estimation, adaptation, dimension reduction, and confidence bands as well as the universal E-estimator for small samples. Exercises and a large number of simulated and real-life examples that can be repeated and modified using the complementary R package are also included. This coverage, together with the intuitive style of presentation, is ideal for people entering this field. The context is suitable for self-study or a one-semester course for graduate students with majors ranging from biostatistics and data analytics to econometrics and actuarial science.
In addition, this book:
• Presents a self-contained review of needed facts from probability as well as parametric and nonparametric statistics
• Introduces the asymptotic theory and the proposed methodology of efficient estimation
• Formulates topics for future research for both Ph.D. students and specialists
About the Author
Sam Efromovich, Ph.D., is an Endowed Professor and the Head of Actuarial Program in the Department of Mathematical Sciences at The University of Texas at Dallas. He is an Elected Fellow of the American Statistical Association and of the Institute of Mathematical Statistics and is well known for his work on the theory and applications of nonparametric curve estimation.
This textbook provides a unified account of estimating the survival function, hazard rate, cumulative hazard, density, regression, conditional distributions, and linear functionals for the current status censored and right-censored data. The book contains the theory and methodology of efficient estimation, adaptation, dimension reduction, and confidence bands as well as the universal E-estimator for small samples. Exercises and a large number of simulated and real-life examples that can be repeated and modified using the complementary R package are also included. This coverage, together with the intuitive style of presentation, is ideal for people entering this field. The context is suitable for self-study or a one-semester course for graduate students with majors ranging from biostatistics and data analytics to econometrics and actuarial science.
Presents a self-contained review of needed facts from probability as well as parametric and nonparametric statistics Includes exercises throughout with different levels of difficulty that utilize R as the computational application Explains what can be done if the rate of censoring is high and how to aggregate censored and uncensored observations
Autor*in
Sam Efromovich
Themen in »Survival Analysis«
Nonparametric Estimation Random Variables Current Status Censoring Multivariate Regression Right-Censored Data Nonparametric Regression Bivariate Survival Analysis