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Multivariate Kernel Smoothing and Its Applications

in composite solutions to complex data challenges. Multivariate Kernel Smoothing and Its Applications offers a comprehensive overview of both;

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Multivariate Kernel Smoothing and Its Applications

in composite solutions to complex data challenges. Multivariate Kernel Smoothing and Its Applications offers a comprehensive overview of both;

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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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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 of Multivariate Data

An applied treatment of the key methods and state-of-the-art tools for visualizing and understanding statistical data Smoothing;

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Asymmetric Kernel Smoothing

This is the first book to provide an accessible and comprehensive introduction to a newly developed smoothing technique using asymmetric;

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Econometric Society Monographs

equivalent to kernel smoothing. To simplify the exposition, kernel smoothers are introduced and discussed in great detail. Building on this;

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Kernel Smoothing in MATLAB

optimal bandwidth and a special procedure for simultaneous choice of the bandwidth, the kernel and its order are implemented. The toolbox;

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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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Smoothing Splines

A general class of powerful and flexible modeling techniques, spline smoothing has attracted a great deal of research attention in recent;

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Smoothing Splines

A general class of powerful and flexible modeling techniques, spline smoothing has attracted a great deal of research attention in recent;

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Multivariate Nonparametric Regression And Visualization

A modern approach to statistical learning and its applications through visualization methods With a unique and innovative presentation;

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Kernel Learning Algorithms for Face Recognition

kernel learning algorithms and its application on face recognition. This book also focuses on the theoretical deviation, the system framework 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 Methods for Remote Sensing Data Analysis

Kernel methods have long been established as effective techniques in the framework of machine learning and pattern recognition, and have;

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Econometric Modeling and Inference

approach by using a small number of estimation techniques, mainly generalized method of moments (GMM) estimation and kernel smoothing. The choice;

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The GETMe Mesh Smoothing Framework

smoothing-like computational efficiency with a global optimization-like effectiveness. The method is straightforward to implement and its variants;

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The GETMe Mesh Smoothing Framework

smoothing-like computational efficiency with a global optimization-like effectiveness. The method is straightforward to implement and its variants;

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Multivariate Time Series Analysis and Applications

years, dimension will become a more serious problem. Multivariate Time Series Analysis and its Applications provides some initial solutions;

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Nonparametric Smoothing and Lack-of-Fit Tests

of kernel and Fourier series type, and could be used as the foundation for a graduate level course on nonparametric function estimation. The;

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Multivariate Density Estimation

in kernel statistics, smoothing, advanced computational statistics, and general forms of statistical distributions.;

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Data-Variant Kernel Analysis

Describes and discusses the variants of kernel analysis methods for data types that have been intensely studied in recent years This book;

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Multivariate Statistical Inference And Applications

The most accessible introduction to the theory and practice of multivariate analysis Multivariate Statistical Inference and Applications;

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Kernel Mean Embedding of Distributions

and statistical inference. The basic idea behind this framework is to map distributions into a reproducing kernel Hilbert space (RKHS;

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Kernel Adaptive Filtering

Reproducing kernel Hilbert spaces is a topic of great current interest for applications in signal processing, communications, and controls;

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Continuous Multivariate Distributions

, methodology, inferential procedures, computational and simulational aspects, and applications of continuous multivariate distributions. In-depth;

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Spline Models for Observational Data

observations on a finite number of its values. The estimate is a polynomial smoothing spline. By placing this smoothing problem in the setting;

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