Density Estimation For Statistics And Data Analysis(1st Edition)

Book details

Book Price $0 : Bernard W. Silverman's 'Density Estimation for Statistics and Data Analysis' is an essential text that delves into the methodologies of density estimation—an important aspect of non-parametric statistics. The book is particularly renowned for providing a thorough exploration of kernel density estimation and its applications. It elucidates multiple approaches through both theoretical frameworks and practical implementations, offering insights into data smoothing techniques and their efficacy in statistical data analysis. Readers will appreciate the comprehensive organization, highlighted by the book’s detailed table of contents, which systematically guides statisticians, data scientists, and advanced students through intricate concepts like bandwidth selection and cross-validation. Beyond the core content, the text also discusses neoteric methods and contemporary applications that have facilitated recent advances in data analysis. Although absent of a traditional plot or characters, the narrative drives the reader through the transformative journey of statistical learning. For those delving deeper into academic exercises, a solution manual accompanies the text, offering an answer key for complex exercises and further probing the reader's understanding. Since its initial publication, this book has garnered positive reception for its clarity and practical utility, serving both as a valuable reference for professionals and a learning tool for students embarking on statistical endeavors. Extensive use of graphical illustrations enhances comprehension of density functions and estimation procedures, making it an indispensable resource in the statistical arena. With comprehensive coverage spanning both traditional methodologies and novel approaches, it remains a pivotal contribution to statistical literature and education. This book’s cheap availability and user-friendly approach facilitate learning without overwhelming students with complicated jargon. See more