Flexible Nonparametric Curve Estimation

• Titel: Flexible Nonparametric Curve Estimation
• Mitwirkende: Doosti, Hassan [HerausgeberIn]
• Verf.angabe: edited by Hassan Doosti
• Ausgabe: 1st ed. 2024.
• Verlagsort: Cham
• Verlagsort: Cham
• Verlag: Springer International Publishing
• Verlag: Imprint: Springer
• E-Jahr: 2024
• Jahr: 2024.
• Jahr: 2024.
• Umfang: 1 Online-Ressource(VIII, 304 p. 79 illus., 50 illus. in color.)
• ISBN: 978-3-031-66501-1
• DOI: doi:10.1007/978-3-031-66501-1
• Datenträger: Online-Ressource
• Sprache: eng
• Bibliogr. Hinweis: Erscheint auch als : Druck-Ausgabe
• Bibliogr. Hinweis: Erscheint auch als : Druck-Ausgabe
• Bibliogr. Hinweis: Erscheint auch als : Druck-Ausgabe
• K10plus-PPN: 1902232933
• K10plus-PPN:
  Universitätsbibliothek
• Lokale URL UB: Zum Volltext

Abstract

- Tilted Nonparametric Regression Function Estimation -- Some Asymptotic Properties of Kernel Density Estimation Under Length-Biased and Right-Cencored Data -- Functional Data Analysis: Key Concepts and Applications -- Convolution Process revisited in finite location mixtures and GARFISMA long memory time series -- Non-parametric Estimation of Tsallis Entropy and Residual Tsallis Entropy Under ρ-mixing Dependent Data -- Non-parametric intensity estimation for spatial point patterns with R -- A Censored Semicontinuous Regression for Modeling Clustered /Longitudinal Zero-Inflated Rates and Proportions: An Application to Colorectal Cancer -- Singular Spectrum Analysis -- Hellinger-Bhattacharyya cross-validation for shape-preserving multivariate wavelet thresholding -- Bayesian nonparametrics and mixture modelling -- A kernel scale mixture of the skew-normal distribution -- M-estimation of an intensity function and an underlying population size under random right truncation.

Abstract

This book delves into the realm of nonparametric estimations, offering insights into essential notions such as probability density, regression, Tsallis Entropy, Residual Tsallis Entropy, and intensity functions. Through a series of carefully crafted chapters, the theoretical foundations of flexible nonparametric estimators are examined, complemented by comprehensive numerical studies. From theorem elucidation to practical applications, the text provides a deep dive into the intricacies of nonparametric curve estimation. Tailored for postgraduate students and researchers seeking to expand their understanding of nonparametric statistics, this book will serve as a valuable resource for anyone who wishes to explore the applications of flexible nonparametric techniques.