Tag: Nonparametric

Free Download Nonparametric Statistical Methods Using R: Second Edition
by John Kloke
English | 2024 | ISBN: 0367651351 | 466 pages | True PDF | 32.62 MB

Free Download Flexible Nonparametric Curve Estimation
English | 2024 | ISBN: 3031665007 | 312 Pages | PDF EPUB (True) | 51 MB
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.

Free Download Nonparametric Statistics with Applications to Science and Engineering by Paul H. Kvam, Brani Vidakovic
English | PDF | 2007 | 431 Pages | ISBN : 0470081473 | 18.4 MB
A thorough and definitive book that fully addresses traditional and modern-day topics of nonparametric statistics

Free Download Nonparametric Analysis of Univariate Heavy‐Tailed Data: Research and Practice by Natalia Markovich
English | PDF | 2007 | 328 Pages | ISBN : 0470510870 | 32.1 MB
Heavy-tailed distributions are typical for phenomena in complex multi-component systems such as biometry, economics, ecological systems, sociology, web access statistics, internet traffic, biblio-metrics, finance and business. The analysis of such distributions requires special methods of estimation due to their specific features. These are not only the slow decay to zero of the tail, but also the violation of Cramer’s condition, possible non-existence of some moments, and sparse observations in the tail of the distribution.

Free Download Principles of Nonparametric Learning by László Györfi
English | PDF (True) | 2002 | 344 Pages | ISBN : 3211836888 | 24 MB
The book provides systematic in-depth analysis of nonparametric learning. It covers the theoretical limits and the asymptotical optimal algorithms and estimates, such as pattern recognition, nonparametric regression estimation, universal prediction, vector quantization, distribution and density estimation and genetic programming. The book is mainly addressed to postgraduates in engineering, mathematics, computer science, and researchers in universities and research institutions.