Details

<p>- 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.</p>

<p><strong>Dr. Hassan Doosti</strong> 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.</p>

<p>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.</p>

<p>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.</p>

<p>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.</p>

Includes the latest advancements in nonparametric curve estimation methods Enhances practitioners’ skills with essential nonparametric estimation techniques Provides a deep dive into nonparametric estimation with real-world examples, including biased data scenarios