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.