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Improved Parametric and Nonparametric Classification Techniques

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International Symposia in Economic Theory and Econometrics

selection models, (ii) nonparametric and semi-parametric approaches to qualitative and sample selection models, and (iii) nonlinear estimation;

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Nonlinear Statistical Modeling

selection models, (ii) nonparametric and semi-parametric approaches to qualitative and sample selection models, and (iii) nonlinear estimation;

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Introduction to Robust Estimation and Hypothesis Testing

easy-to-use software. This trusted resource provides an overview of modern robust methods, including improved techniques for dealing with;

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Local Polynomial Modelling and Its Applications

data well. The use of these nonparametric functions with parametric techniques can yield very powerful data analysis tools. Local polynomial;

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Nonparametric Techniques in Statistical Inference

as in its applications. Nonparametric methods are mathematically elegant, and they also yield significantly improved performances;

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3D Parametric and Nonparametric Description of Surface Topography in Manufacturing Processes

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Sequential Estimation

techniques and including parametric and nonparametric methods.;

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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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Nonparametrics for Sensory Science

parametric methodology. Nonparametric methods are appropriate when facts are only available in nominal or ordinal form, and when the model;

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Semiparametic Regression for the Applied Econometrician

This book provides an accessible collection of techniques for analyzing nonparametric and semiparametric regression models. Worked examples;

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Themes in Modern Econometrics

This book provides an accessible collection of techniques for analyzing nonparametric and semiparametric regression models. Worked examples;

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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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Bootstrap Methods and their Application

intervals, and significance tests. The methods apply for any level of modelling, and so can be used for fully parametric, semiparametric, and;

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Combined Parametric-Nonparametric Identification of Block-Oriented Systems

parametric-nonparametric algorithms, proposed in the book, can be selected dependently on the prior knowledge of the system and signals. Most of them;

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Quantitative Anthropology

dispersion, and including hypothesis testing with both parametric and nonparametric statistical tests. Increasingly complex exercises build on;

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Applied Statistical Designs for the Researcher

general data distribution, skewness, and outliers. It features a unique classification of the nonparametric analogs of their parametric;

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Applied Statistical Designs for the Researcher

general data distribution, skewness, and outliers. It features a unique classification of the nonparametric analogs of their parametric;

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A Primer on Nonparametric Analysis, Volume I

. The method provides an alternative for such cases and is often nearly as powerful as parametric statistics. Another advantage of nonparametric;

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A Primer on Nonparametric Analysis, Volume II

. The method provides an alternative for such cases and is often nearly as powerful as parametric statistics. Another advantage of nonparametric;

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Nonparametric Tests for Censored Data

This book concerns testing hypotheses in non-parametric models. Generalizations of many non-parametric tests to the case of censored and;

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Modelling Nonlinear Economic Time Series

relationships. It considers nonlinear models in stationary and nonstationary frameworks, and both parametric and nonparametric models are discussed. The;

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Nonparametric Tests for Complete Data

This book concerns testing hypotheses in non-parametric models. Classical non-parametric tests (goodness-of-fit, homogeneity, randomness;

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Multiscale Signal Analysis and Modeling

, feature extraction and classification, signal and image representation/transmission, coding, nonparametric statistical signal processing, and;

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An Introduction to Wavelets and Other Filtering Methods in Finance and Economics

to a wide spectrum of parametric and nonparametric filtering methods. Examples and empirical applications will show readers the capabilities;

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Bayesian Thinking, Modeling and Computation

point of view. It further describes parametric and nonparametric Bayesian methods for modelling and how to use modern computational methods to;

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