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Provides theoretical insights and justification of the statistical
procedures for the analysis of high-dimensional data Presents a
general framework of regularization methods Covers feature
screening for ultrahigh-dimensional data Describes large-scale
covariance estimation
Data-analytic approaches to regression problems, arising from many
scientific disciplines are described in this book. The aim of these
nonparametric methods is to relax assumptions on the form of a
regression function and to let data search for a suitable function
that describes the data well. The use of these nonparametric
functions with parametric techniques can yield very powerful data
analysis tools. Local polynomial modeling and its applications
provides an up-to-date picture on state-of-the-art nonparametric
regression techniques. The emphasis of the book is on methodologies
rather than on theory, with a particular focus on applications of
nonparametric techniques to various statistical problems.
High-dimensional data-analytic tools are presented, and the book
includes a variety of examples. This will be a valuable reference
for research and applied statisticians, and will serve as a
textbook for graduate students and others interested in
nonparametric regression.
The collection and analysis of data play an important role in many
fields of science and technology, such as computational biology,
quantitative finance, information engineering, machine learning,
neuroscience, medicine, and the social sciences. Especially in the
era of big data, researchers can easily collect data characterised
by massive dimensions and complexity. In celebration of Professor
Kai-Tai Fang's 80th birthday, we present this book, which furthers
new and exciting developments in modern statistical theories,
methods and applications. The book features four review papers on
Professor Fang's numerous contributions to the fields of
experimental design, multivariate analysis, data mining and
education. It also contains twenty research articles contributed by
prominent and active figures in their fields. The articles cover a
wide range of important topics such as experimental design,
multivariate analysis, data mining, hypothesis testing and
statistical models.
The collection and analysis of data play an important role in many
fields of science and technology, such as computational biology,
quantitative finance, information engineering, machine learning,
neuroscience, medicine, and the social sciences. Especially in the
era of big data, researchers can easily collect data characterised
by massive dimensions and complexity. In celebration of Professor
Kai-Tai Fang's 80th birthday, we present this book, which furthers
new and exciting developments in modern statistical theories,
methods and applications. The book features four review papers on
Professor Fang's numerous contributions to the fields of
experimental design, multivariate analysis, data mining and
education. It also contains twenty research articles contributed by
prominent and active figures in their fields. The articles cover a
wide range of important topics such as experimental design,
multivariate analysis, data mining, hypothesis testing and
statistical models.
This volume presents selections of Peter J. Bickel's major papers,
along with comments on their novelty and impact on the subsequent
development of statistics as a discipline. Each of the eight parts
concerns a particular area of research and provides new commentary
by experts in the area. The parts range from Rank-Based
Nonparametrics to Function Estimation and Bootstrap Resampling.
Peter's amazing career encompasses the majority of statistical
developments in the last half-century or about about half of the
entire history of the systematic development of statistics. This
volume shares insights on these exciting statistical developments
with future generations of statisticians. The compilation of
supporting material about Peter's life and work help readers
understand the environment under which his research was conducted.
The material will also inspire readers in their own research-based
pursuits. This volume includes new photos of Peter Bickel, his
biography, publication list, and a list of his students. These give
the reader a more complete picture of Peter Bickel as a teacher, a
friend, a colleague, and a family man.
This volume presents selections of Peter J. Bickel's major papers,
along with comments on their novelty and impact on the subsequent
development of statistics as a discipline. Each of the eight parts
concerns a particular area of research and provides new commentary
by experts in the area. The parts range from Rank-Based
Nonparametrics to Function Estimation and Bootstrap Resampling.
Peter's amazing career encompasses the majority of statistical
developments in the last half-century or about about half of the
entire history of the systematic development of statistics. This
volume shares insights on these exciting statistical developments
with future generations of statisticians. The compilation of
supporting material about Peter's life and work help readers
understand the environment under which his research was conducted.
The material will also inspire readers in their own research-based
pursuits. This volume includes new photos of Peter Bickel, his
biography, publication list, and a list of his students. These give
the reader a more complete picture of Peter Bickel as a teacher, a
friend, a colleague, and a family man.
This book presents the contemporary statistical methods and theory
of nonlinear time series analysis. The principal focus is on
nonparametric and semiparametric techniques developed in the last
decade. It covers the techniques for modelling in state-space, in
frequency-domain as well as in time-domain. To reflect the
integration of parametric and nonparametric methods in analyzing
time series data, the book also presents an up-to-date exposure of
some parametric nonlinear models, including ARCH/GARCH models and
threshold models. A compact view on linear ARMA models is also
provided. Data arising in real applications are used throughout to
show how nonparametric approaches may help to reveal local
structure in high-dimensional data. Important technical tools are
also introduced. The book will be useful for graduate students,
application-oriented time series analysts, and new and experienced
researchers. It will have the value both within the statistical
community and across a broad spectrum of other fields such as
econometrics, empirical finance, population biology and ecology.
The prerequisites are basic courses in probability and statistics.
Jianqing Fan, coauthor of the highly regarded book Local Polynomial
Modeling, is Professor of Statistics at the University of North
Carolina at Chapel Hill and the Chinese University of Hong Kong.
His published work on nonparametric modeling, nonlinear time
series, financial econometrics, analysis of longitudinal data,
model selection, wavelets and other aspects of methodological and
theoretical statistics has been recognized with the Presidents'
Award from the Committee of Presidents of Statistical Societies,
the Hettleman Prize for Artistic andScholarly Achievement from the
University of North Carolina, and by his election as a fellow of
the American Statistical Association and the Institute of
Mathematical Statistics. Qiwei Yao is Professor of Statistics at
the London School of Economics and Political Science. He is an
elected member of the International Statistical Institute, and has
served on the editorial boards for the Journal of the Royal
Statistical Society (Series B) and the Australian and New Zealand
Journal of Statistics.
In contemporary science and engineering applications, the volume of
available data is growing at an enormous rate. Spectral methods
have emerged as a simple yet surprisingly effective approach for
extracting information from massive, noisy and incomplete data. A
diverse array of applications have been found in machine learning,
imaging science, financial and econometric modeling, and signal
processing.This monograph presents a systematic, yet accessible
introduction to spectral methods from a modern statistical
perspective, highlighting their algorithmic implications in diverse
large-scale applications. The authors provide a unified and
comprehensive treatment that establishes the theoretical
underpinnings for spectral methods, particularly through a
statistical lens.Building on years of research experience in the
field, the authors present a powerful framework, called
leave-one-out analysis, that proves effective and versatile for
delivering fine-grained performance guarantees for a variety of
problems. This book is essential reading for all students,
researchers and practitioners working in Data Science.
Financial econometrics is an interdisciplinary subject that uses
statistical methods and economic theory to address a variety of
quantitative problems in finance. This compact, master's-level
textbook focuses on methodology and includes real financial data
illustrations throughout. The mathematical level is purposely kept
moderate, allowing the power of the quantitative methods to be
understood without too much technical detail. Wherever possible,
the authors indicate where to find the relevant R codes to
implement the various methods. This book grew out of a course at
Princeton University which is one of the world's flagship programs
in computational finance and financial engineering. It will
therefore be useful for those with an economics and finance
background who are looking to sharpen their quantitative skills,
and also for those with strong quantitative skills who want to
learn how to apply them to finance.
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