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Flexible Nonparametric Curve Estimation 2024 ed. [Kõva köide]

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  • Formaat: Hardback, 304 pages, kõrgus x laius: 235x155 mm, 50 Illustrations, color; 29 Illustrations, black and white; VIII, 304 p. 79 illus., 50 illus. in color., 1 Hardback
  • Ilmumisaeg: 05-Sep-2024
  • Kirjastus: Springer International Publishing AG
  • ISBN-10: 3031665007
  • ISBN-13: 9783031665004
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Flexible Nonparametric Curve Estimation 2024 ed.Suurem pilt
  • Formaat: Hardback, 304 pages, kõrgus x laius: 235x155 mm, 50 Illustrations, color; 29 Illustrations, black and white; VIII, 304 p. 79 illus., 50 illus. in color., 1 Hardback
  • Ilmumisaeg: 05-Sep-2024
  • Kirjastus: Springer International Publishing AG
  • ISBN-10: 3031665007
  • ISBN-13: 9783031665004
Teised raamatud teemal:
Püsilink: https://www.kriso.ee/db/9783031665004.html
Märksõnad:
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.

- 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 ?-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.

Dr. Hassan Doosti is a senior lecturer in Statistics at Macquarie University, where he also holds the position of Program Director for the Master of Data Science program. With a primary focus on nonparametric curve estimation, Dr. Doosti has made significant contributions to the field, with a publication record of over 50 research papers. His expertise encompasses a wide range of topics, including probability density, quantile density, and regression functions tailored for incomplete and biased samples.