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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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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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Nonparametric Regression Methods For Longitudinal Data Analysis

nonparametric regression techniques for longitudinal data analysis and systematically investigates the incorporation of mixed-effects modeling;

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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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Nonparametric Smoothing and Lack-of-Fit Tests

smoothing ideas. The first four chapters introduce the problem of estimating regression functions by nonparametric smoothers, primarily those;

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Image Processing And Jump Regression Analysis

nonparametric jump regression analysis by outlining procedures that are easy to use, simple to compute, and have proven statistical theory behind them;

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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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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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Spline Regression Models

Spline Regression Models shows the nuts-and-bolts of using dummy variables to formulate and estimate various spline regression models. For;

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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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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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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

natural and the environmental sciences, and others. Nonparametric density estimation, nonparametric and semiparametric regression, trend and;

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Nonparametric Econometric Methods

Methods', and the papers selected for inclusion in this Volume span a range of nonparametric techniques including kernel smoothing, empirical;

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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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Nonparametric Methods in Statistics with SAS Applications

Fisher exact tests. It then discusses smoothing techniques (loess and thin-plate splines) for classical nonparametric regression as well as;

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Nonparametric Methods In Statistics With Sas Applications

Fisher exact tests. It then discusses smoothing techniques (loess and thin-plate splines) for classical nonparametric regression as well as;

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Spline and Spline Wavelet Methods with Applications to Signal and Image Processi

. In particular, interpolating, smoothing, and shift-orthogonal splines are presented.;

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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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Multivariate Nonparametric Regression And Visualization

, Multivariate Nonparametric Regression and Visualization provides readers with the core statistical concepts to obtain complete and accurate predictions;

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Spline Models for Observational Data

This book serves well as an introduction into the more theoretical aspects of the use of spline models. It develops a theory and practice;

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Nonparametric Models for Longitudinal Data

. Features: * Provides an overview of parametric and semiparametric methods * Shows smoothing methods for unstructured nonparametric models;

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Local Polynomial Modelling and Its Applications

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;

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Statistical Methods in the Atmospheric Sciences

science Contains revised and expanded sections on nonparametric tests, test multiplicity and quality uncertainty descriptors

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