A Uniform Central Limit Theorem and Efficiency for Deconvolution Estimators PDF Download
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Author: Jakob Söhl
Publisher:
ISBN:
Category :
Languages : en
Pages :
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Author: Jakob Söhl
Publisher:
ISBN:
Category :
Languages : en
Pages :
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Author: R. M. Dudley
Publisher: Cambridge University Press
ISBN: 0521498848
Category : Mathematics
Languages : en
Pages : 485
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Book Description
This expanded edition of the classic work on empirical processes now boasts several new proved theorems not in the first.
Author: R. M. Dudley
Publisher: Cambridge University Press
ISBN: 1107728886
Category : Mathematics
Languages : en
Pages : 485
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Book Description
In this new edition of a classic work on empirical processes the author, an acknowledged expert, gives a thorough treatment of the subject with the addition of several proved theorems not included in the first edition, including the Bretagnolle–Massart theorem giving constants in the Komlos–Major–Tusnady rate of convergence for the classical empirical process, Massart's form of the Dvoretzky–Kiefer–Wolfowitz inequality with precise constant, Talagrand's generic chaining approach to boundedness of Gaussian processes, a characterization of uniform Glivenko–Cantelli classes of functions, Giné and Zinn's characterization of uniform Donsker classes, and the Bousquet–Koltchinskii–Panchenko theorem that the convex hull of a uniform Donsker class is uniform Donsker. The book will be an essential reference for mathematicians working in infinite-dimensional central limit theorems, mathematical statisticians, and computer scientists working in computer learning theory. Problems are included at the end of each chapter so the book can also be used as an advanced text.
Author: R. M. Dudley
Publisher:
ISBN: 9781107720220
Category : Central limit theorem
Languages : en
Pages : 482
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Author: Evarist Giné
Publisher: Cambridge University Press
ISBN: 1009022784
Category : Mathematics
Languages : en
Pages : 706
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Book Description
In nonparametric and high-dimensional statistical models, the classical Gauss–Fisher–Le Cam theory of the optimality of maximum likelihood estimators and Bayesian posterior inference does not apply, and new foundations and ideas have been developed in the past several decades. This book gives a coherent account of the statistical theory in infinite-dimensional parameter spaces. The mathematical foundations include self-contained 'mini-courses' on the theory of Gaussian and empirical processes, approximation and wavelet theory, and the basic theory of function spaces. The theory of statistical inference in such models - hypothesis testing, estimation and confidence sets - is presented within the minimax paradigm of decision theory. This includes the basic theory of convolution kernel and projection estimation, but also Bayesian nonparametrics and nonparametric maximum likelihood estimation. In a final chapter the theory of adaptive inference in nonparametric models is developed, including Lepski's method, wavelet thresholding, and adaptive inference for self-similar functions. Winner of the 2017 PROSE Award for Mathematics.
Author: Claudia Kirch
Publisher:
ISBN:
Category :
Languages : en
Pages :
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Author: William J. Adams
Publisher: American Mathematical Soc.
ISBN: 0821890549
Category :
Languages : en
Pages : 218
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Author: C. C. Heesterman
Publisher:
ISBN:
Category :
Languages : de
Pages : 13
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Author: Sam Efromovich
Publisher: Springer Science & Business Media
ISBN: 0387226389
Category : Mathematics
Languages : en
Pages : 423
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Book Description
This book gives a systematic, comprehensive, and unified account of modern nonparametric statistics of density estimation, nonparametric regression, filtering signals, and time series analysis. The companion software package, available over the Internet, brings all of the discussed topics into the realm of interactive research. Virtually every claim and development mentioned in the book is illustrated with graphs which are available for the reader to reproduce and modify, making the material fully transparent and allowing for complete interactivity.
Author:
Publisher:
ISBN:
Category : Mathematical statistics
Languages : en
Pages : 948
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Book Description
The Current Index to Statistics (CIS) is a bibliographic index of publications in statistics, probability, and related fields.
