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Missing and Modified Data in Nonparametric Estimation

This book presents a systematic and unified approach for modern nonparametric treatment of missing and modified data via examples;

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Missing and Modified Data in Nonparametric Estimation

This book presents a systematic and unified approach for modern nonparametric treatment of missing and modified data via examples;

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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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Selection Models for Nonignorable Missing Data

models for nonignorable missing data. The theory of selection models is described and illustrated by examples. Maximum Likelihood as well as;

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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 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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Nonparametric Methods in Statistics with SAS Applications

apply nonparametric techniques to statistical data. It starts with the tests of hypotheses and moves on to regression modeling, time-to-event;

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Nonparametric Methods In Statistics With Sas Applications

apply nonparametric techniques to statistical data. It starts with the tests of hypotheses and moves on to regression modeling, time-to-event;

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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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Applied Statistical Science II

Case; Estimation of Parameters Using Modified Ranked Set Sampling; Certain Families of Distributions.;

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Nonparametric Econometric Methods

nonparametric environmental econometrics and nonparametric models with non-stationary data. There exist theoretical papers dealing with novel approaches;

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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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Deep Learning and Missing Data in Engineering Systems

Deep Learning and Missing Data in Engineering Systems uses deep learning and swarm intelligence methods to cover missing data estimation;

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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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Nonlinear Time Series

methods in model estimation, specification testing, and selection of time series data. After a brief introduction, the book examines;

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Nonlinear Time Series

methods in model estimation, specification testing, and selection of time series data. After a brief introduction, the book examines;

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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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Semiparametric Theory and Missing Data

This book summarizes current knowledge regarding the theory of estimation for semiparametric models with missing data, in an organized and;

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Nonparametric Statistical Methods Using R

nonparametric methods and rank-based analyses, including estimation and inference for models ranging from simple location models to general linear and;

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Nonparametric Statistical Methods Using R

traditional nonparametric methods and rank-based analyses, including estimation and inference for models ranging from simple location models to;

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Missing Data Methods

Volume 27 of Advances in Econometrics , entitled Missing Data Methods , contains 16 chapters authored by specialists in the field;

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Missing Data Methods

Volume 27 of Advances in Econometrics , entitled Missing Data Methods , contains 16 chapters authored by specialists in the field;

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Kernel Smoothing

estimation method that is applicable when parametric descriptions of the data are not sufficiently adequate. This book explores theory and methods;

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Multivariate Nonparametric Regression And Visualization

graduate-level courses on nonparametric function estimation, advanced topics in statistics, and quantitative finance. The book is also an excellent;

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Sequential Estimation

accelerated sequential sampling procedures. It integrates the diversities in sequential estimation in a logical, treating both classical and modern;

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Nonparametric Statistical Methods

data sets, and new problems from real-life situations. The book continues to emphasize the importance of nonparametric methods as a significant;

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