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This book provides an introduction to the use of statistical
concepts and methods to model and analyze financial data. The ten
chapters of the book fall naturally into three sections. Chapters 1
to 3 cover some basic concepts of finance, focusing on the
properties of returns on an asset. Chapters 4 through 6 cover
aspects of portfolio theory and the methods of estimation needed to
implement that theory. The remainder of the book, Chapters 7
through 10, discusses several models for financial data, along with
the implications of those models for portfolio theory and for
understanding the properties of return data. The audience for the
book is students majoring in Statistics and Economics as well as in
quantitative fields such as Mathematics and Engineering. Readers
are assumed to have some background in statistical methods along
with courses in multivariate calculus and linear algebra.
One of the greatest changes in the sports world in the past 20
years has been the use of mathematical methods to analyze
performances, recognize trends and patterns, and predict results.
Analytic Methods in Sports: Using Mathematics and Statistics to
Understand Data from Baseball, Football, Basketball, and Other
Sports, Second Edition provides a concise yet thorough introduction
to the analytic and statistical methods that are useful in studying
sports. The book gives you all the tools necessary to answer key
questions in sports analysis. It explains how to apply the methods
to sports data and interpret the results, demonstrating that the
analysis of sports data is often different from standard
statistical analyses. The book integrates a large number of
motivating sports examples throughout and offers guidance on
computation and suggestions for further reading in each chapter.
Features Covers numerous statistical procedures for analyzing data
based on sports results Presents fundamental methods for describing
and summarizing data Describes aspects of probability theory and
basic statistical concepts that are necessary to understand and
deal with the randomness inherent in sports data Explains the
statistical reasoning underlying the methods Illustrates the
methods using real data drawn from a wide variety of sports Offers
many of the datasets on the author's website, enabling you to
replicate the analyses or conduct related analyses New to the
Second Edition R code included for all calculations A new chapter
discussing several more advanced methods, such as binary response
models, random effects, multilevel models, spline methods, and
principal components analysis, and more Exercises added to the end
of each chapter, to enable use for courses and self-study Full
solutions manual available to course instructors.
One of the greatest changes in the sports world in the past 20
years has been the use of mathematical methods to analyze
performances, recognize trends and patterns, and predict results.
Analytic Methods in Sports: Using Mathematics and Statistics to
Understand Data from Baseball, Football, Basketball, and Other
Sports, Second Edition provides a concise yet thorough introduction
to the analytic and statistical methods that are useful in studying
sports. The book gives you all the tools necessary to answer key
questions in sports analysis. It explains how to apply the methods
to sports data and interpret the results, demonstrating that the
analysis of sports data is often different from standard
statistical analyses. The book integrates a large number of
motivating sports examples throughout and offers guidance on
computation and suggestions for further reading in each chapter.
Features Covers numerous statistical procedures for analyzing data
based on sports results Presents fundamental methods for describing
and summarizing data Describes aspects of probability theory and
basic statistical concepts that are necessary to understand and
deal with the randomness inherent in sports data Explains the
statistical reasoning underlying the methods Illustrates the
methods using real data drawn from a wide variety of sports Offers
many of the datasets on the author's website, enabling you to
replicate the analyses or conduct related analyses New to the
Second Edition R code included for all calculations A new chapter
discussing several more advanced methods, such as binary response
models, random effects, multilevel models, spline methods, and
principal components analysis, and more Exercises added to the end
of each chapter, to enable use for courses and self-study Full
solutions manual available to course instructors.
Likelihood methods play a central role in statistical theory and methodology. Recently, a new approach to likelihood inference has been developed that often leads to substantial improvements over classical methods. This book gives a detailed introduction to this modern theory of likelihood methods.
This book provides an introduction to the use of statistical
concepts and methods to model and analyze financial data. The ten
chapters of the book fall naturally into three sections. Chapters 1
to 3 cover some basic concepts of finance, focusing on the
properties of returns on an asset. Chapters 4 through 6 cover
aspects of portfolio theory and the methods of estimation needed to
implement that theory. The remainder of the book, Chapters 7
through 10, discusses several models for financial data, along with
the implications of those models for portfolio theory and for
understanding the properties of return data. The audience for the
book is students majoring in Statistics and Economics as well as in
quantitative fields such as Mathematics and Engineering. Readers
are assumed to have some background in statistical methods along
with courses in multivariate calculus and linear algebra.
This detailed introduction to distribution theory uses no measure
theory, making it suitable for students in statistics and
econometrics as well as for researchers who use statistical
methods. Good backgrounds in calculus and linear algebra are
important and a course in elementary mathematical analysis is
useful, but not required. An appendix gives a detailed summary of
the mathematical definitions and results that are used in the book.
Topics covered range from the basic distribution and density
functions, expectation, conditioning, characteristic functions,
cumulants, convergence in distribution and the central limit
theorem to more advanced concepts such as exchangeability, models
with a group structure, asymptotic approximations to integrals,
orthogonal polynomials and saddlepoint approximations. The emphasis
is on topics useful in understanding statistical methodology; thus,
parametric statistical models and the distribution theory
associated with the normal distribution are covered
comprehensively.
This detailed introduction to distribution theory uses no measure
theory, making it suitable for students in statistics and
econometrics as well as for researchers who use statistical
methods. Good backgrounds in calculus and linear algebra are
important and a course in elementary mathematical analysis is
useful, but not required. An appendix gives a detailed summary of
the mathematical definitions and results that are used in the book.
Topics covered range from the basic distribution and density
functions, expectation, conditioning, characteristic functions,
cumulants, convergence in distribution and the central limit
theorem to more advanced concepts such as exchangeability, models
with a group structure, asymptotic approximations to integrals,
orthogonal polynomials and saddlepoint approximations. The emphasis
is on topics useful in understanding statistical methodology; thus,
parametric statistical models and the distribution theory
associated with the normal distribution are covered
comprehensively.
Semiparametric Efficiency Bounds for Microeconometric Models offers
a partial review of the vast literature in econometrics and
statistics on calculating semiparametric efficiency bounds for a
large class of models used in applied economics research. The main
role of the efficiency bound is to give a lower bound to the
asymptotic variance of an estimator. An estimator with asymptotic
variance equal to the efficiency bound can therefore be said to be
asymptotically efficient. These bounds are also useful for
understanding how the features of a given model affect the accuracy
of parameter estimation. This monograph will help researchers learn
more about efficiency bounds, their calculation, and their
usefulness in semiparametric estimation, in an accessible manner.
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Kathy Willis
Hardcover
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Discovery Miles 4 690
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