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Nonparametric Estimation Shape Constrnts

This book treats the latest developments in the theory of order-restricted inference, with special attention to nonparametric methods 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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All of Nonparametric Statistics

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

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Applied Nonparametric Econometrics

advanced nonparametric methods. The analysis starts with density estimation and motivates the procedures through methods that should be familiar to;

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Applied Nonparametric Econometrics

advanced nonparametric methods. The analysis starts with density estimation and motivates the procedures through methods that should be familiar to;

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Nonparametric Methods in Statistics with SAS Applications

analysis, density estimation, and resampling methods. The text begins with classical nonparametric hypotheses testing, including the sign;

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Nonparametric Methods In Statistics With Sas Applications

analysis, density estimation, and resampling methods. The text begins with classical nonparametric hypotheses testing, including the sign;

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Nonparametric Econometric Methods

to keep abreast of some of the most important current developments in the field of nonparametric estimation.;

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Nonparametric Finance

estimation is an important part of machine learning, which is becoming increasingly important in quantitative finance. Nonparametric Finance provides;

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Sequential Estimation

This book devoted to sequential estimation presents the advances of the past fifteen years including those in the areas of three--stage;

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Spectral Analysis of Signals

challenge is how to extend the existing spectral estimation techniques to deal with these missing-data samples. Recently, nonparametric adaptive;

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Nonparametric Inference on Manifolds

, nonparametric Bayesian theory is adapted and extended to manifolds for the purposes of density estimation, regression and classification. Ideal for;

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Nonparametric Inference On Manifolds

, nonparametric Bayesian theory is adapted and extended to manifolds for the purposes of density estimation, regression and classification. Ideal for;

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Missing and Modified Data in Nonparametric Estimation

of density and hazard rate estimation, nonparametric regression, filtering signals, and time series analysis. All basic types of missing at random;

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Missing and Modified Data in Nonparametric Estimation

of density and hazard rate estimation, nonparametric regression, filtering signals, and time series analysis. All basic types of missing at random;

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Handbook Of Applied Econometrics And Statistical Inference

ofsample size requirements for estimation in SUR models, innovative developments in nonparametric models, and more.;

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Handbook Of Applied Econometrics And Statistical Inference

ofsample size requirements for estimation in SUR models, innovative developments in nonparametric models, and more.;

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Semiparametric and Nonparametric Methods in Econometrics

misleading. Nonparametric and semiparametric statistical methods provide a way to reduce the strength of the assumptions required for estimation and;

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Mathematical Foundations of Infinite-Dimensional Statistical Models

of decision theory. This includes the basic theory of convolution kernel and projection estimation, but also Bayesian nonparametrics and nonparametric;

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Applied Statistical Science II

Contents: Nonparametric Perspectives in Regression Analysis; Record Values of Univariate Distribution; A Transfer Theorem for Multitype;

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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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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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Modeling Energy Demand using Nonparametric and Extreme Value Theory

Modeling demand and price data using nonparametric methods and extreme value theory provides an up-to-date picture on how extreme events;

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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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Image Processing And Jump Regression Analysis

in nonparametric jump regression analysis and image processing as well as a reference on image processing or curve/surface estimation.;

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Mathematical Statistics

nonparametric techniques. It covers topics in parametric and nonparametric large-sample estimation theory. The exposition is based on a collection;

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