โ Scribed by Alexander Korostelev, Olga Korosteleva
No coin nor oath required. For personal study only.
The SM (PDF) is available for Free from Author's website
http://www.ams.org/publications/authors/books/postpub/gsm-119
Chapter 1 The Fisher Efficiency
Chapter 2 The Bayes and Minimax Estimators
Chapter 3 Asymptotic Minimaxity
Chapter 4 Some Irrigular Statistical Experiments
Chapter 5 Change-Point Problem
Chapter 6 Sequential Estimators
Chapter 7 Linear Parametric Regression
Chapter 8 Estimation in Nonparametric Regression
Chapter 9 Local Polynomial Approximation of Regression Function
Chapter 10 Estimation of Regression in Global Norms
Chapter 11 Estimation by Splines
Chapter 12 Asymptotic Optimality in Global Norms
Chapter 13 Estimation of Functionals
Chapter 14 Dimension and Structure in Nonparametric Regression
Chapter 15 Adaptive Estimation
Chapter 16 Testing of Nonparametric Hypotheses
ะะฐัะตะผะฐัะธะบะฐ;ะขะตะพัะธั ะฒะตัะพััะฝะพััะตะน ะธ ะผะฐัะตะผะฐัะธัะตัะบะฐั ััะฐัะธััะธะบะฐ;ะะฐัะตะผะฐัะธัะตัะบะฐั ััะฐัะธััะธะบะฐ;
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This book is designed to bridge the gap between traditional textbooks in statistics and more advanced books that include the sophisticated nonparametric techniques. It covers topics in parametric and nonparametric large-sample estimation theory. The exposition is based on a collection of relatively
This book is designed to bridge the gap between traditional textbooks in statistics and more advanced books that include the sophisticated nonparametric techniques. It covers topics in parametric and nonparametric large-sample estimation theory. The exposition is based on a collection of relatively
This book is designed to bridge the gap between traditional textbooks in statistics and more advanced books that include the sophisticated nonparametric techniques. It covers topics in parametric and nonparametric large-sample estimation theory. The exposition is based on a collection of relatively
<span>This book is designed to bridge the gap between traditional textbooks in statistics and more advanced books that include the sophisticated nonparametric techniques. It covers topics in parametric and nonparametric large-sample estimation theory. The exposition is based on a collection of relat