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Books > Science & Mathematics > Mathematics > Probability & statistics
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Missing and Modified Data in Nonparametric Estimation - With R Examples (Hardcover)
Loot Price: R2,921
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Missing and Modified Data in Nonparametric Estimation - With R Examples (Hardcover)
Series: Chapman & Hall/CRC Monographs on Statistics and Applied Probability
Expected to ship within 10 - 15 working days
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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.
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