Springer, 2021. — 540 p. — ISBN 978-981-15-9002-3. The book presents the fundamental concepts from asymptotic statistical inference theory, elaborating on some basic large sample optimality properties of estimators and some test procedures. The most desirable property of consistency of an estimator and its large sample distribution, with suitable normalization, are discussed,...
Springer, 2018. — 339 p. — (Springer Series in Statistics). — ISBN: 303002184X. This book provides a coherent framework for understanding shrinkage estimation in statistics. The term refers to modifying a classical estimator by moving it closer to a target which could be known a priori or arise from a model. The goal is to construct estimators with improved statistical...
Springer International Publishing AG, 2018. — 197 p. – (Studies in Big Data 37) – ISBN: 3319716875. This book describes computational problems related to kernel density estimation (KDE) - one of the most important and widely used data smoothing techniques. A very detailed description of novel FFT-based algorithms for both KDE computations and bandwidth selection are presented....
Springer, 2021. — 619 p. — ISBN 973030637569. This advanced textbook explores small area estimation techniques, covers the underlying mathematical and statistical theory and offers hands-on support with their implementation. It presents the theory in a rigorous way and compares and contrasts various statistical methodologies, helping readers understand how to develop new...
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