Mathematical Statistics Basic Ideas and Selected Topics Volume II

479 Pages·2018·5.24 MB·english

by  Peter Bickel

Texts in Statistical Science
Volume II
Statistics
Mathematical
Mathematical Statistics: Basic Ideas and Selected Topics, Volume II presents important M
statistical concepts, methods, and tools not covered in the authors’ previous volume. This
second volume focuses on inference in non- and semiparametric models. It not only reexamines
a
the procedures introduced in the first volume from a more sophisticated point of view but also
Statistics
addresses new problems originating from the analysis of estimation of functions and other t
complex decision procedures and large-scale data analysis. h
e
The book covers asymptotic efficiency in semiparametric models from the Le Cam and Fisherian
points of view as well as some finite sample size optimality criteria based on Lehmann–
m
Basic Ideas and
Scheffé theory. It develops the theory of semiparametric maximum likelihood estimation with
applications to areas such as survival analysis. It also discusses methods of inference based
a
on sieve models and asymptotic testing theory. The remainder of the book is devoted to model
Selected Topics
and variable selection, Monte Carlo methods, nonparametric curve estimation, and prediction, t
i
classification, and machine learning topics.
c
Features
a Volume II
• Develops basic asymptotic tools, including weak convergence for random processes,
empirical process theory, and the functional delta method l

• Discusses the classical theory of statistical optimality in a decision-theoretic context
S
• Presents inference procedures and their properties in a variety of applications and
models, such as Cox’s regression model, models for censored data, and partial linear t
models a
• Describes properties of Monte Carlo/simulation-based methods, including the bootstrap
t
and Markov chain Monte Carlo (MCMC) i
• Examines the nonparametric estimation of functions of one or more variables s
• Covers many topics related to statistical learning, including support vector machines and
t
classification and regression trees (CART) i
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Using the tools and methods developed in this textbook, you will be ready for advanced
research in modern statistics. Numerous examples and problems illustrate statistical modeling s
and inference concepts. Measure theory is not required for understanding.
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Mathematical
Statistics
Basic Ideas and
Selected Topics
Volume II
CHAPMAN & HALL/CRC
Texts in Statistical Science Series
Series Editors
Francesca Dominici, Harvard School of Public Health, USA
Julian J. Faraway, University of Bath, UK
Martin Tanner, Northwestern University, USA
Jim Zidek, University of British Columbia, Canada
Statistical Theory: A Concise Introduction Statistics for Technology: A Course in Applied
F. Abramovich and Y. Ritov Statistics, Third Edition
C. Chatfield
Practical Multivariate Analysis, Fifth Edition
A. Afifi, S. May, and V.A. Clark Bayesian Ideas and Data Analysis: An
Introduction for Scientists and Statisticians
Practical Statistics for Medical Research
D.G. Altman R. Christensen, W. Johnson, A. Branscum,
and T.E. Hanson
Interpreting Data: A First Course
in Statistics Modelling Binary Data, Second Edition
A.J.B. Anderson D. Collett
Introduction to Probability with R Modelling Survival Data in Medical Research,
K. Baclawski Third Edition
D. Collett
Linear Algebra and Matrix Analysis for
Statistics Introduction to Statistical Methods for
S. Banerjee and A. Roy Clinical Trials
T.D. Cook and D.L. DeMets
Mathematical Statistics: Basic Ideas and
Selected Topics, Volume I, Second Edition Applied Statistics: Principles and Examples
P. J. Bickel and K. A. Doksum D.R. Cox and E.J. Snell
Mathematical Statistics: Basic Ideas and Multivariate Survival Analysis and Competing
Selected Topics, Volume II Risks
P. J. Bickel and K. A. Doksum M. Crowder
Analysis of Categorical Data with R Statistical Analysis of Reliability Data
C. R. Bilder and T. M. Loughin M.J. Crowder, A.C. Kimber,
T.J. Sweeting, and R.L. Smith
Statistical Methods for SPC and TQM
D. Bissell An Introduction to Generalized
Linear Models, Third Edition
Introduction to Probability
A.J. Dobson and A.G. Barnett
J. K. Blitzstein and J. Hwang
Nonlinear Time Series: Theory, Methods, and
Bayesian Methods for Data Analysis,
Applications with R Examples
Third Edition
R. Douc, E. Moulines, and D.S. Stoffer
B.P. Carlin and T.A. Louis
Introduction to Optimization Methods and
Second Edition
Their Applications in Statistics
R. Caulcutt
B.S. Everitt
The Analysis of Time Series: An Introduction,
Extending the Linear Model with R:
Sixth Edition
Generalized Linear, Mixed Effects and
C. Chatfield
Nonparametric Regression Models
Introduction to Multivariate Analysis
J.J. Faraway
C. Chatfield and A.J. Collins
Linear Models with R, Second Edition
Problem Solving: A Statistician’s Guide,
J.J. Faraway
Second Edition
A Course in Large Sample Theory
C. Chatfield
T.S. Ferguson
Multivariate Statistics: A Practical Exercises and Solutions in Statistical Theory
Approach L.L. Kupper, B.H. Neelon, and S.M. O’Brien
B. Flury and H. Riedwyl
Design and Analysis of Experiments with R
Readings in Decision Analysis J. Lawson
S. French
Design and Analysis of Experiments with SAS
Markov Chain Monte Carlo: J. Lawson
Stochastic Simulation for Bayesian Inference,
A Course in Categorical Data Analysis
Second Edition
T. Leonard
D. Gamerman and H.F. Lopes
Statistics for Accountants
Bayesian Data Analysis, Third Edition
S. Letchford
A. Gelman, J.B. Carlin, H.S. Stern, D.B. Dunson,
Introduction to the Theory of Statistical
A. Vehtari, and D.B. Rubin
Inference
Multivariate Analysis of Variance and
H. Liero and S. Zwanzig
Repeated Measures: A Practical Approach for
Statistical Theory, Fourth Edition
Behavioural Scientists
B.W. Lindgren
D.J. Hand and C.C. Taylor
Stationary Stochastic Processes: Theory and
Practical Longitudinal Data Analysis
Applications
D.J. Hand and M. Crowder
G. Lindgren
Logistic Regression Models
Statistics for Finance
J.M. Hilbe
E. Lindström, H. Madsen, and J. N. Nielsen
Richly Parameterized Linear Models:
The BUGS Book: A Practical Introduction to
Additive, Time Series, and Spatial Models
Bayesian Analysis
Using Random Effects
D. Lunn, C. Jackson, N. Best, A. Thomas, and
J.S. Hodges
D. Spiegelhalter
Statistics for Epidemiology
Introduction to General and Generalized
N.P. Jewell
Linear Models
Stochastic Processes: An Introduction,
H. Madsen and P. Thyregod
Second Edition
Time Series Analysis
P.W. Jones and P. Smith
H. Madsen
The Theory of Linear Models
Pólya Urn Models
B. Jørgensen
H. Mahmoud
Principles of Uncertainty
Randomization, Bootstrap and Monte Carlo
J.B. Kadane
Methods in Biology, Third Edition
Graphics for Statistics and Data Analysis with R
B.F.J. Manly
K.J. Keen
Introduction to Randomized Controlled
Mathematical Statistics
Clinical Trials, Second Edition
K. Knight
J.N.S. Matthews
Introduction to Multivariate Analysis:
Statistical Methods in Agriculture and
Linear and Nonlinear Modeling
Experimental Biology, Second Edition
S. Konishi
R. Mead, R.N. Curnow, and A.M. Hasted
Nonparametric Methods in Statistics with SAS
Statistics in Engineering: A Practical Approach
Applications
A.V. Metcalfe
O. Korosteleva
Statistical Inference: An Integrated Approach,
Modeling and Analysis of Stochastic Systems,
Second Edition
Second Edition
H. S. Migon, D. Gamerman, and
V.G. Kulkarni
F. Louzada
Exercises and Solutions in Biostatistical Theory
Beyond ANOVA: Basics of Applied Statistics
L.L. Kupper, B.H. Neelon, and S.M. O’Brien
R.G. Miller, Jr.
A Primer on Linear Models Decision Analysis: A Bayesian Approach
J.F. Monahan J.Q. Smith
Applied Stochastic Modelling, Second Edition Analysis of Failure and Survival Data
B.J.T. Morgan P. J. Smith
Elements of Simulation Applied Statistics: Handbook of GENSTAT
B.J.T. Morgan Analyses
Probability: Methods and Measurement E.J. Snell and H. Simpson
A. O’Hagan Applied Nonparametric Statistical Methods,
Introduction to Statistical Limit Theory Fourth Edition
A.M. Polansky P. Sprent and N.C. Smeeton
Applied Bayesian Forecasting and Time Series Data Driven Statistical Methods
Analysis P. Sprent
A. Pole, M. West, and J. Harrison Generalized Linear Mixed Models:
Statistics in Research and Development, Modern Concepts, Methods and Applications
Time Series: Modeling, Computation, and W. W. Stroup
Inference Survival Analysis Using S: Analysis of
R. Prado and M. West Time-to-Event Data
Introduction to Statistical Process Control M. Tableman and J.S. Kim
P. Qiu Applied Categorical and Count Data Analysis
Sampling Methodologies with Applications W. Tang, H. He, and X.M. Tu
P.S.R.S. Rao Elementary Applications of Probability Theory,
A First Course in Linear Model Theory Second Edition
N. Ravishanker and D.K. Dey H.C. Tuckwell
Essential Statistics, Fourth Edition Introduction to Statistical Inference and Its
D.A.G. Rees Applications with R
M.W. Trosset
Stochastic Modeling and Mathematical
Statistics: A Text for Statisticians and Understanding Advanced Statistical Methods
Quantitative Scientists P.H. Westfall and K.S.S. Henning
F.J. Samaniego Statistical Process Control: Theory and
Statistical Methods for Spatial Data Analysis Practice, Third Edition
O. Schabenberger and C.A. Gotway G.B. Wetherill and D.W. Brown
Bayesian Networks: With Examples in R Generalized Additive Models:
M. Scutari and J.-B. Denis An Introduction with R
S. Wood
Large Sample Methods in Statistics
P.K. Sen and J. da Motta Singer Epidemiology: Study Design and
Data Analysis, Third Edition
Spatio-Temporal Methods in Environmental
M. Woodward
Epidemiology
G. Shaddick and J.V. Zidek Practical Data Analysis for Designed
Experiments
B.S. Yandell
Texts in Statistical Science
Mathematical
Statistics
Basic Ideas and
Selected Topics
Volume II
Peter J. Bickel
University of California
Berkley, California, USA
Kjell A. Doksum
University of Wisconsin
Madison, Wisconsin, USA
CRC Press
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