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"This very informative book introduces classical and novel
statistical methods that can be used by theoretical and applied
biostatisticians to develop efficient solutions for real-world
problems encountered in clinical trials and epidemiological
studies. The authors provide a detailed discussion of
methodological and applied issues in parametric, semi-parametric
and nonparametric approaches, including computationally extensive
data-driven techniques, such as empirical likelihood, sequential
procedures, and bootstrap methods. Many of these techniques are
implemented using popular software such as R and SAS."- Vlad
Dragalin, Professor, Johnson and Johnson, Spring House, PA "It is
always a pleasure to come across a new book that covers nearly all
facets of a branch of science one thought was so broad, so diverse,
and so dynamic that no single book could possibly hope to capture
all of the fundamentals as well as directions of the field. The
topics within the book's purview-fundamentals of measure-theoretic
probability; parametric and non-parametric statistical inference;
central limit theorems; basics of martingale theory; Monte Carlo
methods; sequential analysis; sequential change-point detection-are
all covered with inspiring clarity and precision. The authors are
also very thorough and avail themselves of the most recent
scholarship. They provide a detailed account of the state of the
art, and bring together results that were previously scattered
across disparate disciplines. This makes the book more than just a
textbook: it is a panoramic companion to the field of
Biostatistics. The book is self-contained, and the concise but
careful exposition of material makes it accessible to a wide
audience. This is appealing to graduate students interested in
getting into the field, and also to professors looking to design a
course on the subject." - Aleksey S. Polunchenko, Department of
Mathematical Sciences, State University of New York at Binghamton
This book should be appropriate for use both as a text and as a
reference. This book delivers a "ready-to-go" well-structured
product to be employed in developing advanced courses. In this book
the readers can find classical and new theoretical methods, open
problems and new procedures. The book presents biostatistical
results that are novel to the current set of books on the market
and results that are even new with respect to the modern scientific
literature. Several of these results can be found only in this
book.
Empirical Likelihood Methods in Biomedicine and Health provides a
compendium of nonparametric likelihood statistical techniques in
the perspective of health research applications. It includes
detailed descriptions of the theoretical underpinnings of recently
developed empirical likelihood-based methods. The emphasis
throughout is on the application of the methods to the health
sciences, with worked examples using real data. Provides a
systematic overview of novel empirical likelihood techniques.
Presents a good balance of theory, methods, and applications.
Features detailed worked examples to illustrate the application of
the methods. Includes R code for implementation. The book material
is attractive and easily understandable to scientists who are new
to the research area and may attract statisticians interested in
learning more about advanced nonparametric topics including various
modern empirical likelihood methods. The book can be used by
graduate students majoring in biostatistics, or in a related field,
particularly for those who are interested in nonparametric methods
with direct applications in Biomedicine.
Statistical Testing Strategies in the Health Sciences provides a
compendium of statistical approaches for decision making, ranging
from graphical methods and classical procedures through
computationally intensive bootstrap strategies to advanced
empirical likelihood techniques. It bridges the gap between
theoretical statistical methods and practical procedures applied to
the planning and analysis of health-related experiments. The book
is organized primarily based on the type of questions to be
answered by inference procedures or according to the general type
of mathematical derivation. It establishes the theoretical
framework for each method, with a substantial amount of chapter
notes included for additional reference. It then focuses on the
practical application for each concept, providing real-world
examples that can be easily implemented using corresponding
statistical software code in R and SAS. The book also explains the
basic elements and methods for constructing correct and powerful
statistical decision-making processes to be adapted for complex
statistical applications. With techniques spanning robust
statistical methods to more computationally intensive approaches,
this book shows how to apply correct and efficient testing
mechanisms to various problems encountered in medical and
epidemiological studies, including clinical trials. Theoretical
statisticians, medical researchers, and other practitioners in
epidemiology and clinical research will appreciate the book's novel
theoretical and applied results. The book is also suitable for
graduate students in biostatistics, epidemiology, health-related
sciences, and areas pertaining to formal decision-making
mechanisms.
Empirical Likelihood Methods in Biomedicine and Health provides a
compendium of nonparametric likelihood statistical techniques in
the perspective of health research applications. It includes
detailed descriptions of the theoretical underpinnings of recently
developed empirical likelihood-based methods. The emphasis
throughout is on the application of the methods to the health
sciences, with worked examples using real data. Provides a
systematic overview of novel empirical likelihood techniques.
Presents a good balance of theory, methods, and applications.
Features detailed worked examples to illustrate the application of
the methods. Includes R code for implementation. The book material
is attractive and easily understandable to scientists who are new
to the research area and may attract statisticians interested in
learning more about advanced nonparametric topics including various
modern empirical likelihood methods. The book can be used by
graduate students majoring in biostatistics, or in a related field,
particularly for those who are interested in nonparametric methods
with direct applications in Biomedicine.
"This very informative book introduces classical and novel
statistical methods that can be used by theoretical and applied
biostatisticians to develop efficient solutions for real-world
problems encountered in clinical trials and epidemiological
studies. The authors provide a detailed discussion of
methodological and applied issues in parametric, semi-parametric
and nonparametric approaches, including computationally extensive
data-driven techniques, such as empirical likelihood, sequential
procedures, and bootstrap methods. Many of these techniques are
implemented using popular software such as R and SAS."- Vlad
Dragalin, Professor, Johnson and Johnson, Spring House, PA "It is
always a pleasure to come across a new book that covers nearly all
facets of a branch of science one thought was so broad, so diverse,
and so dynamic that no single book could possibly hope to capture
all of the fundamentals as well as directions of the field. The
topics within the book's purview-fundamentals of measure-theoretic
probability; parametric and non-parametric statistical inference;
central limit theorems; basics of martingale theory; Monte Carlo
methods; sequential analysis; sequential change-point detection-are
all covered with inspiring clarity and precision. The authors are
also very thorough and avail themselves of the most recent
scholarship. They provide a detailed account of the state of the
art, and bring together results that were previously scattered
across disparate disciplines. This makes the book more than just a
textbook: it is a panoramic companion to the field of
Biostatistics. The book is self-contained, and the concise but
careful exposition of material makes it accessible to a wide
audience. This is appealing to graduate students interested in
getting into the field, and also to professors looking to design a
course on the subject." - Aleksey S. Polunchenko, Department of
Mathematical Sciences, State University of New York at Binghamton
This book should be appropriate for use both as a text and as a
reference. This book delivers a "ready-to-go" well-structured
product to be employed in developing advanced courses. In this book
the readers can find classical and new theoretical methods, open
problems and new procedures. The book presents biostatistical
results that are novel to the current set of books on the market
and results that are even new with respect to the modern scientific
literature. Several of these results can be found only in this
book.
Statistical Testing Strategies in the Health Sciences provides a
compendium of statistical approaches for decision making, ranging
from graphical methods and classical procedures through
computationally intensive bootstrap strategies to advanced
empirical likelihood techniques. It bridges the gap between
theoretical statistical methods and practical procedures applied to
the planning and analysis of health-related experiments. The book
is organized primarily based on the type of questions to be
answered by inference procedures or according to the general type
of mathematical derivation. It establishes the theoretical
framework for each method, with a substantial amount of chapter
notes included for additional reference. It then focuses on the
practical application for each concept, providing real-world
examples that can be easily implemented using corresponding
statistical software code in R and SAS. The book also explains the
basic elements and methods for constructing correct and powerful
statistical decision-making processes to be adapted for complex
statistical applications. With techniques spanning robust
statistical methods to more computationally intensive approaches,
this book shows how to apply correct and efficient testing
mechanisms to various problems encountered in medical and
epidemiological studies, including clinical trials. Theoretical
statisticians, medical researchers, and other practitioners in
epidemiology and clinical research will appreciate the book's novel
theoretical and applied results. The book is also suitable for
graduate students in biostatistics, epidemiology, health-related
sciences, and areas pertaining to formal decision-making
mechanisms.
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