Buch, Englisch, 304 Seiten, Format (B × H): 160 mm x 241 mm, Gewicht: 635 g
ISBN: 978-3-031-66500-4
Verlag: Springer
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.
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Weitere Infos & Material
- 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.
