Flexible Nonparametric Curve Estimation
Springer International Publishing (Verlag)
978-3-031-66500-4 (ISBN)
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.
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.
- 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.
| Erscheinungsdatum | 06.09.2024 |
|---|---|
| Zusatzinfo | VIII, 304 p. 79 illus., 50 illus. in color. |
| Verlagsort | Cham |
| Sprache | englisch |
| Maße | 155 x 235 mm |
| Themenwelt | Mathematik / Informatik ► Mathematik ► Wahrscheinlichkeit / Kombinatorik |
| Schlagworte | Kernel estimator • nonparametric estimation • Probability density function • Regresssion • survival function |
| ISBN-10 | 3-031-66500-7 / 3031665007 |
| ISBN-13 | 978-3-031-66500-4 / 9783031665004 |
| Zustand | Neuware |
| Informationen gemäß Produktsicherheitsverordnung (GPSR) | |
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