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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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Nonparametric Finance

of Multivariate Data: Density Estimation and Visualization and Multivariate Nonparametric Regression and Visualization: With R and Applications to Finance.;

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Regression And Time Series Model Selection

selection techniques for univariate and multivariate regression models, univariate and multivariate autoregressive models, nonparametric (including;

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Multivariate Density Estimation

Clarifies modern data analysis through nonparametric density estimation for a complete working knowledge of the theory and methods;

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Smoothing of Multivariate Data

of visualization tools. Rather than outlining the theoretical concepts of classification and regression, this book focuses on the procedures for estimating;

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Graphics for Statistics and Data Analysis with R

visualization and representation. Added in the second edition are coverage of the ggplot2 graphics package, material on human visualization and color;

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Graphics for Statistics and Data Analysis with R

graphical displays for data visualization and representation. Added in the second edition are coverage of the ggplot2 graphics package, material on;

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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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An Introduction to Nonparametric Statistics

techniques include one-sample testing and estimation, multi-sample testing and estimation, and regression. Attention is paid to the intellectual;

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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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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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Modern Nonparametric, Robust and Multivariate Methods

Written by leading experts in the field, this edited volume brings together the latest findings in the area of nonparametric, robust and;

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Optimality and adaptivity in nonparametric regression

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A Distribution-Free Theory of Nonparametric Regression

This book provides a systematic in-depth analysis of nonparametric regression with random design. It covers almost all known estimates. The;

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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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Cambridge Series in Statistical and Probabilistic Mathematics

This book introduces basic and advanced concepts of categorical regression with a focus on the structuring constituents of regression;

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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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Observed Confidence Levels

associated with smoothing methods, including nonparametric density estimation and regression. The author also describes applications in generalized;

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Observed Confidence Levels

associated with smoothing methods, including nonparametric density estimation and regression. The author also describes applications in generalized;

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Ranking of Multivariate Populations

analysis by image processing. A web-based nonparametric combination global ranking software is also described. Designed for practitioners and;

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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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Multivariate Permutation Tests

results. Permutation and nonparametric combination methods provide flexible solutions to complex problems by reducing the problem down to a set;

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Nonparametric Smoothing and Lack-of-Fit Tests

problems. As such, the book is of interest to practitioners of statistics and researchers investigating either lack-of-fit tests or nonparametric;

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Theory Of Preliminary Test And Stein-Type Estimation With Applications

constraints; regression with autocorrelated errors; ridge regression; and multivariate and discrete data models Normal, non-normal, and nonparametric;

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All of Nonparametric Statistics

method, nonparametric regression, density estimation, orthogonal function methods, minimax estimation, nonparametric confidence sets, and;

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Nonparametric Statistical Methods Using R

A Practical Guide to Implementing Nonparametric and Rank-Based Procedures Nonparametric Statistical Methods Using R covers traditional;

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