Density Estimation for Statistics and Data Analysis

Density Estimation for Statistics and Data Analysis presents a practical, accessible account of density estimation with the goal of facilitating broader practical application of density estimation and encouraging further research. The author discusses several applications, including the analysis and presentation of data, nonparametric discriminant analysis, cluster analysis, simulation and the bootstrap, bump hunting, projection pursuit, and the estimation of hazard rates. The book includes a general survey of methods available for density estimation and discusses the kernel method, adaptive methods, and methods based on penalized likelihood. More the 50 graphs and figures complement the text.