Local Polynomial Modelling and Its Applications Local Polynomial Modelling and Its Applications
Chapman & Hall/CRC Monographs on Statistics and Applied Probability

Local Polynomial Modelling and Its Applications

Monographs on Statistics and Applied Probability 66

    • USD 209.99
    • USD 209.99

Descripción editorial

Data-analytic approaches to regression problems, arising from many scientific disciplines are described in this book. The aim of these nonparametric methods is to relax assumptions on the form of a regression function and to let data search for a suitable function that describes the data well. The use of these nonparametric functions with parametric techniques can yield very powerful data analysis tools. Local polynomial modeling and its applications provides an up-to-date picture on state-of-the-art nonparametric regression techniques. The emphasis of the book is on methodologies rather than on theory, with a particular focus on applications of nonparametric techniques to various statistical problems. High-dimensional data-analytic tools are presented, and the book includes a variety of examples. This will be a valuable reference for research and applied statisticians, and will serve as a textbook for graduate students and others interested in nonparametric regression.

GÉNERO
Ciencia y naturaleza
PUBLICADO
2018
2 de mayo
IDIOMA
EN
Inglés
EXTENSIÓN
360
Páginas
EDITORIAL
CRC Press
VENDEDOR
Taylor & Francis Group
TAMAÑO
13.6
MB
Contemporary Experimental Design, Multivariate Analysis and Data Mining Contemporary Experimental Design, Multivariate Analysis and Data Mining
2020
Selected Works of Peter J. Bickel Selected Works of Peter J. Bickel
2012
Hierarchical Modeling and Analysis for Spatial Data Hierarchical Modeling and Analysis for Spatial Data
2025
Robust Small Area Estimation Robust Small Area Estimation
2025
Robust Nonparametric Statistical Methods Robust Nonparametric Statistical Methods
2010
Statistical Methods for Stochastic Differential Equations Statistical Methods for Stochastic Differential Equations
2012
Maximum Likelihood Estimation for Sample Surveys Maximum Likelihood Estimation for Sample Surveys
2012
Components of Variance Components of Variance
2002