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This book presents a systematic and unified approach for modern
nonparametric treatment of missing and modified data via examples
of density and hazard rate estimation, nonparametric regression,
filtering signals, and time series analysis. All basic types of
missing at random and not at random, biasing, truncation,
censoring, and measurement errors are discussed, and their
treatment is explained. Ten chapters of the book cover basic cases
of direct data, biased data, nondestructive and destructive
missing, survival data modified by truncation and censoring,
missing survival data, stationary and nonstationary time series and
processes, and ill-posed modifications. The coverage is suitable
for self-study or a one-semester course for graduate students with
a prerequisite of a standard course in introductory probability.
Exercises of various levels of difficulty will be helpful for the
instructor and self-study. The book is primarily about practically
important small samples. It explains when consistent estimation is
possible, and why in some cases missing data should be ignored and
why others must be considered. If missing or data modification
makes consistent estimation impossible, then the author explains
what type of action is needed to restore the lost information. The
book contains more than a hundred figures with simulated data that
explain virtually every setting, claim, and development. The
companion R software package allows the reader to verify, reproduce
and modify every simulation and used estimators. This makes the
material fully transparent and allows one to study it
interactively. Sam Efromovich is the Endowed Professor of
Mathematical Sciences and the Head of the Actuarial Program at the
University of Texas at Dallas. He is well known for his work on the
theory and application of nonparametric curve estimation and is the
author of Nonparametric Curve Estimation: Methods, Theory, and
Applications. Professor Sam Efromovich is a Fellow of the Institute
of Mathematical Statistics and the American Statistical
Association.
Medical Product Safety Evaluation: Biological Models and
Statistical Methods presents cutting-edge biological models and
statistical methods that are tailored to specific objectives and
data types for safety analysis and benefit-risk assessment. Some
frequently encountered issues and challenges in the design and
analysis of safety studies are discussed with illustrative
applications and examples. Medical Product Safety Evaluation:
Biological Models and Statistical Methods presents cutting-edge
biological models and statistical methods that are tailored to
specific objectives and data types for safety analysis and
benefit-risk assessment. Some frequently encountered issues and
challenges in the design and analysis of safety studies are
discussed with illustrative applications and examples. The book is
designed not only for biopharmaceutical professionals, such as
statisticians, safety specialists, pharmacovigilance experts, and
pharmacoepidemiologists, who can use the book as self-learning
materials or in short courses or training programs, but also for
graduate students in statistics and biomedical data science for a
one-semester course. Each chapter provides supplements and problems
as more readings and exercises.
Medical Product Safety Evaluation: Biological Models and
Statistical Methods presents cutting-edge biological models and
statistical methods that are tailored to specific objectives and
data types for safety analysis and benefit-risk assessment. Some
frequently encountered issues and challenges in the design and
analysis of safety studies are discussed with illustrative
applications and examples. Medical Product Safety Evaluation:
Biological Models and Statistical Methods presents cutting-edge
biological models and statistical methods that are tailored to
specific objectives and data types for safety analysis and
benefit-risk assessment. Some frequently encountered issues and
challenges in the design and analysis of safety studies are
discussed with illustrative applications and examples. The book is
designed not only for biopharmaceutical professionals, such as
statisticians, safety specialists, pharmacovigilance experts, and
pharmacoepidemiologists, who can use the book as self-learning
materials or in short courses or training programs, but also for
graduate students in statistics and biomedical data science for a
one-semester course. Each chapter provides supplements and problems
as more readings and exercises.
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