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Functional Estimation For Density, Regression Models And Processes

of estimators for functionals of processes and densities, and provides asymptotic expansions and optimality properties from smooth estimators.It also;

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Gaussian Process Regression Analysis for Functional Data

methodological developments for high dimensional data and variable selection. The remainder of the text explores advanced topics of functional regression;

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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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Radiation Risk Estimation

studies. Contents: Part I - Estimation in regression models with errors in covariates Measurement error models Linear models 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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Asymptotic Theory for Econometricians

applications, specifically unit root regression, spurious regression, and regression with cointegrated processes* Updated material on:

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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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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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Methods of Statistical Model Estimation

Methods of Statistical Model Estimation examines the most important and popular methods used to estimate parameters for statistical models;

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Methods of Statistical Model Estimation

Methods of Statistical Model Estimation examines the most important and popular methods used to estimate parameters for statistical models;

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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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Functional and High-Dimensional Statistics and Related Fields

methodological and computational aspects, as well as real-world applications. It covers topics like classification, confidence bands, density estimation;

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Theory Of Preliminary Test And Stein-Type Estimation With Applications

Theory of Preliminary Test and Stein-Type Estimation with Applications provides a com-prehensive account of the theory and methods;

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Nonparametric Regression and Spline Smoothing

of boundary corrections for trigonometric series estimators; detailed asymptotics for polynomial regression; testing goodness-of-fit; estimation;

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Nonparametric Regression and Spline Smoothing

of boundary corrections for trigonometric series estimators; detailed asymptotics for polynomial regression; testing goodness-of-fit; estimation;

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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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Nonparametric Function Estimation, Modeling, and Simulation

, and parallel based algorithms. New methods discussed are quick nonparametric density estimation based techniques for resampling and simulation;

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Introduction to Spatial Econometrics

likelihood and Bayesian estimation, various types of spatial regression specifications, and applied modeling situations involving different;

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Introduction to Spatial Econometrics

likelihood and Bayesian estimation, various types of spatial regression specifications, and applied modeling situations involving different;

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Simultaneous Inference in Regression

of parameter estimation and hypothesis testing. Simultaneous Inference in Regression provides a thorough overview of the construction methods and;

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Simultaneous Inference in Regression

of parameter estimation and hypothesis testing. Simultaneous Inference in Regression provides a thorough overview of the construction methods and;

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Nonparametric Econometric Methods

models with time series data, exponential series estimators of empirical copulas, estimation of multivariate CDFs and bias-reduction methods for;

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Regression Models as a Tool in Medical Research

results is often challenging for beginners. Regression Models as a Tool in Medical Research presents the fundamental concepts and important;

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Regression Models As A Tool In Medical Research

results is often challenging for beginners. Regression Models as a Tool in Medical Research presents the fundamental concepts and important;

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Density Ratio Estimation in Machine Learning

to machine learning. The book provides mathematical theories for density ratio estimation including parametric and non-parametric convergence;

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