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Practical Smoothing

This is a practical guide to P-splines, a simple, flexible and powerful tool for smoothing. P-splines combine regression on B-splines with;

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Nonparametric Regression and Spline Smoothing

Provides a unified account of the most popular approaches to nonparametric regression smoothing. This edition contains discussions;

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Nonparametric Regression and Spline Smoothing

Provides a unified account of the most popular approaches to nonparametric regression smoothing. This edition contains discussions;

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Flexible Regression and Smoothing

. In particular, the GAMLSS statistical framework enables flexible regression and smoothing models to be fitted to the data. The GAMLSS model assumes that;

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Flexible Regression and Smoothing

. In particular, the GAMLSS statistical framework enables flexible regression and smoothing models to be fitted to the data. The GAMLSS model assumes that;

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Smoothing Splines

smoothing spline ANOVA, extended and generalized smoothing spline ANOVA, vector spline, nonparametric nonlinear regression, semiparametric;

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Smoothing Splines

smoothing spline ANOVA, extended and generalized smoothing spline ANOVA, vector spline, nonparametric nonlinear regression, semiparametric;

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Vibration Reduction for Flexible Structures by Smoothing Commands

smoothers are robust to changes in the system parameters and working conditions. The command smoothing technique has been successfully applied to;

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Econometric Society Monographs

Applied Nonparametric Regression is the first book to bring together in one place the techniques for regression curve smoothing involving;

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Nonparametric Smoothing and Lack-of-Fit Tests

An exploration of the use of smoothing methods in testing the fit of parametric regression models. The book reviews many of the existing;

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Kernel Smoothing

of kernel smoothing in a variety of contexts, considering independent and correlated data e.g. with short-memory and long-memory correlations, as;

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Smoothing Methods in Statistics

, nonparametric regression estimation, categorical data smoothing, and applications of smoothing to other areas of statistics. It will thus be;

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Kernel Smoothing

nonparametric regression. They pay particular attention to the problem of choosing the smoothing parameter of a kernel smoother, and also treat the;

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Partially Linear Models

In the last ten years, there has been increasing interest and activity in the general area of partially linear regression smoothing;

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Flexible Bayesian Regression Modelling

; methods which model non-central parts of the distribution (such as quantile regression); and finally models that allow the mean function to be;

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Image Processing And Jump Regression Analysis

. Key topics include conventional smoothing procedures, estimation of jump regression curves, edge detection in image processing, and edge;

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Semiparametric Regression

ordinary parametric regression, this user-friendly book explains the techniques and benefits of semiparametric regression in a concise and modular;

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Semiparametric Regression

ordinary parametric regression, this user-friendly book explains the techniques and benefits of semiparametric regression in a concise and modular;

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Nonparametric Regression and Generalized Linear Models

In recent years, there has been a great deal of interest and activity in the general area of nonparametric smoothing in statistics. This;

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Generalized Additive Models

This book describes an array of power tools for data analysis that are based on nonparametric regression and smoothing techniques. These;

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Transformation and Weighting in Regression

influence diagnostics, robustness, bootstrapping, nonparametric data smoothing, quasi-likelihood methods, errors-in-variables, and random;

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Transformation and Weighting in Regression

influence diagnostics, robustness, bootstrapping, nonparametric data smoothing, quasi-likelihood methods, errors-in-variables, and random;

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Handbook of Regression Analysis With Applications in R

Handbook and reference guide for students and practitioners of statistical regression-based analyses in R Handbook of Regression;

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Kernel Smoothing in MATLAB

function, regression function, hazard function, indices of quality and bivariate density. Specifically, methods for choosing a choice of the;

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Mathematical Statistics

, normal regression analysis, and normal analysis of variance. In addition, non-parametric statistics and vectorial statistics are considered, as;

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Statistical Methods in the Atmospheric Sciences

>Includes new sections on ANOVA, quantile regression, the lasso and other regularization methods, regression trees, changepoint detection, ensemble;

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Introduction To Nonparametric Regression

the basic concepts underlying nonparametric regression and features: thorough explanations of various techniques, which avoid complex;

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