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Kernel Smoothing

Kernel smoothing refers to a general methodology for recovery of underlying structure in data sets.The basic principle is that local;

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Kernel Smoothing

Comprehensive theoretical overview of kernel smoothing methods with motivating examples Kernel smoothing is a flexible nonparametric curve;

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Asymmetric Kernel Smoothing

to nonparametric estimation methods employing asymmetric kernel smoothing.;

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Kernel Smoothing in MATLAB

Methods of kernel estimates represent one of the most effective nonparametric smoothing techniques. These methods are simple to understand;

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Multivariate Kernel Smoothing and Its Applications

Kernel smoothing has greatly evolved since its inception to become an essential methodology in the data science tool kit for the 21st;

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Multivariate Kernel Smoothing and Its Applications

Kernel smoothing has greatly evolved since its inception to become an essential methodology in the data science tool kit for the 21st;

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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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Smoothing Splines

fixed/mixed smoothing spline models. The theory of reproducing kernel Hilbert space (RKHS) is used to present various smoothing spline models;

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Smoothing Splines

/mixed smoothing spline models. The theory of reproducing kernel Hilbert space (RKHS) is used to present various smoothing spline models in a;

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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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Spline Models for Observational Data

of reproducing kernel Hilbert spaces, a theory is developed which includes univariate smoothing splines, thin plate splines in d dimensions, splines on;

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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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The Statistical Analysis of Doubly Truncated Data - With Applications in R

of the influence of bandwidth in the performance of kernel-type estimators and guidelines for the selection of the smoothing parameter In-depth;

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Linux Kernel Development

Linux Kernel Development details the design and implementation of the Linux kernel, presenting the content in a manner that is beneficial;

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The GETMe Mesh Smoothing Framework

Laplacian smoothing, with a high computational efficiency but possible low resulting mesh quality, to global optimization-based methods, resulting;

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The GETMe Mesh Smoothing Framework

Laplacian smoothing, with a high computational efficiency but possible low resulting mesh quality, to global optimization-based methods, resulting;

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Kernel Learning Algorithms for Face Recognition

Kernel Learning Algorithms for Face Recognition covers the framework of kernel based face recognition. This book discusses the advanced;

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Nonlinear Numerical Analysis in Reproducing Kernel Space

Although the application of reproducing kernel has been explored in different fields in the past twenty to thirty years and the relevant;

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Linux Kernel Programming Part 2 - Char Device Drivers and Kernel Synchronization

understanding of working with hardware interrupts and kernel synchronization Key Features Delve into hardware interrupt handling, threaded IRQs;

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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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Modeling Energy Demand using Nonparametric and Extreme Value Theory

can be modeled. In this book, kernel smoothing based conditional quantile approach, a nonparametric procedure is used to model volatile demand;

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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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Kernel Mean Embedding of Distributions

A Hilbert space embedding of a distribution-in short, a kernel mean embedding-has recently emerged as a powerful tool for machine learning;

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Linux Device Driver Development Cookbook

Over 30 recipes to develop custom drivers for your embedded Linux applications. Key Features Use Kernel facilities to develop powerful;

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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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