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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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On Copula Density Estimation and Measures of Multivariate Association

generalized to the multivariate case. The performance of these estimators is evaluated in comparison to common kernel density estimation schemes. To;

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Smoothing of Multivariate Data

of Multivariate Data provides an illustrative and hands-on approach to the multivariate aspects of density estimation, emphasizing the use;

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Density Estimation for Statistics and Data Analysis

Although there has been a surge of interest in density estimation in recent years, much of the published research has been concerned with;

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Statistical Modeling Using Local Gaussian Approximation

explores Measuring dependence and testing for independence, Time series dependence and spectral analysis, Multivariate density estimation;

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Density Ratio Estimation in Machine Learning

of systems that learn. This book introduces theories, methods and applications of density ratio estimation, which is a newly emerging paradigm in the;

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Density Ratio Estimation in Machine Learning

of systems that learn. This book introduces theories, methods and applications of density ratio estimation, which is a newly emerging paradigm in the;

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Probability and Statistics

basics of sampling distributions, estimation and hypothesis testing. As a companion for classes for engineers and scientists, the book also;

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Nonparametric Finance

research interests include nonparametric function estimation, density estimation, and data visualization. He is the author of Smoothing;

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Multivariate Kernel Smoothing and Its Applications

not direct applications of density (derivative) estimation but sharing many commonalities with the previous settings, include classification;

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Multivariate Kernel Smoothing and Its Applications

not direct applications of density (derivative) estimation but sharing many commonalities with the previous settings, include classification;

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An Introduction to Nonparametric Statistics

Density estimation is explored The bootstrap and jackknife are discussed This text is intended for a graduate student in applied;

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Smoothing Methods in Statistics

Focussing on applications, this book covers a very broad range, including simple and complex univariate and multivariate density estimation;

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Multivariate, Multilinear and Mixed Linear Models

topics related to estimation and testing of multivariate and mixed linear model parameters. Starting with the theory of multivariate;

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Testing For Normality

Describes the selection, design, theory, and application of tests for normality. Covers robust estimation, test power, and univariate and;

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Testing For Normality

Describes the selection, design, theory, and application of tests for normality. Covers robust estimation, test power, and univariate and;

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Nonparametric Function Estimation, Modeling, and Simulation

Topics emphasized include nonparametric density estimation as an exploratory device plus the deeper models to which the exploratory;

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Random Signal Processing

This book covers random signals and random processes along with estimation of probability density function, estimation of energy spectral;

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Random Signal Processing

This book covers random signals and random processes along with estimation of probability density function, estimation of energy spectral;

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Kernel Smoothing

, applications, and analysis of kernel smoothers. This is facilitated by the authors' focus on the simplest settings, namely density estimation and;

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Applied Statistical Science III

; Multivariate Rank Statistics Processes and Change Point Analysis; Improved Estimation of the Parameters of an Autoagressive Gaussian Process Under;

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Econometrics

builds from the classical regression model to cover large sample theory, disturbance problems and generalised least squares, formation estimation;

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Multivariate Geostatistics

variety of models, methods and techniques for the analysis, estimation and display of multivariate data distributed in space or time. It presents;

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High-Dimensional Covariance Estimation

environmental and physical sciences. High-Dimensional Covariance Estimation provides accessible and comprehensive coverage of the classical and modern;

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Multivariate Geostatistics: An Introduction with Applications

statisticians. Geostatistics offers a variety of models, methods and techniques for the analysis, estimation and display of multivariate data;

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Methods of Multivariate Statistics

of Multivariate Statistics offers an up-to-date account of multivariate methods. Focusing on the maximum likelihood method for estimation, testing;

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Linear Model Theory

for estimation, hypothesis testing, and confidence intervals. The final chapters, 20-23, concentrate on choosing a sample size. Substantial;

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