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Two Essays on Maximum Likelihood Estimation

maximum likelihood estimation of DSGE models. The first essay focuses on a monetary DSGE model of term structure, and the estimation of its;

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Maximum Likelihood Estimation with Stata, Fourth Edition

introduction to the theory of maximum likelihood estimation with particular attention on the practical implications for applied work. Individual;

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Econometric Modelling with Time Series

estimation by maximum likelihood, but other methods are also discussed, including quasi-maximum likelihood estimation, generalised method of moments;

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Econometric Modelling with Time Series

estimation by maximum likelihood, but other methods are also discussed, including quasi-maximum likelihood estimation, generalised method of moments;

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Maximum Likelihood Estimation and Inference

This book takes a fresh look at the popular and well-established method of maximum likelihood for statistical estimation and inference. It;

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Parametric Inference: An Introduction

variance unbiased estimator. There is a separate chapter on simultaneous estimation of several parameters. Large sample theory of estimation, based;

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Estimation, Inference and Specification Analysis

of likelihood-based methods of statistical estimation and interference. Professor White first explores the underlying motivation for maximum-likelihood;

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Maximum Likelihood Estimation for Sample Surveys

population and standard application of likelihood methods can lead to biased and inefficient estimates. Maximum Likelihood Estimation for Sample;

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Estimation, Inference and Specification Analysis

of likelihood-based methods of statistical estimation and interference. Professor White first explores the underlying motivation for maximum-likelihood;

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Fundamentals Of Item Response Theory

parameters, such as maximum likelihood estimation, marginal maximum likelihood estimation, and Bayesian estimation in such a way that the reader does;

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Methods of Statistical Model Estimation

maximum likelihood estimation, iteratively reweighted least squares regression, the EM algorithm, and MCMC sampling. Fully developed, working R;

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Methods of Statistical Model Estimation

maximum likelihood estimation, iteratively reweighted least squares regression, the EM algorithm, and MCMC sampling. Fully developed, working R;

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Simultaneous Equations Estimation

This volume comprises the classic articles on methods of identification and estimation of simultaneous equations econometric models. It;

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Econometric Applications of Maximum Likelihood Methods

intricate models is usually based on the method of Maximum Likelihood, which is a well-established branch of mathematical statistics. Its use;

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Introductory Statistics

estimation, maximum likelihood estimation and Bayesian estimation and decisions.;

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Mathematical Foundations of Infinite-Dimensional Statistical Models

maximum likelihood estimation. In a final chapter the theory of adaptive inference in nonparametric models is developed, including Lepski's method;

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Modeling and Estimation of Structural Damage

of probability theory and random processes Covers both theory and algorithms for implementing maximum likelihood and Bayesian estimation approaches;

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Processing Random Data

Two features of Processing Random Data differentiate it from other similar books: the focus on computing the reproducibility error for;

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Antedependence Models for Longitudinal Data

methods. They then present formal likelihood-based procedures for normal antedependence models, including maximum likelihood and residual maximum;

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Multi-Pitch Estimation

illustrated. The presented methods include both single- and multi-pitch estimators based on statistical approaches, like maximum likelihood and;

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Econometric Analysis Of Time Series

of maximum likelihood playing a key role in both estimation and testing. Exercises are included and the book is suitable as a general text for final;

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Multi-Pitch Estimation

illustrated. The presented methods include both single- and multi-pitch estimators based on statistical approaches, like maximum likelihood and;

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Blind Identification and Separation of Complex-valued Signals

are based on algebraic methods as well as iterative algorithms based on maximum likelihood theory. Contents 1. Mathematical Preliminaries. 2;

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Introductory Statistical Inference with the Likelihood Function

psychometricians. After a brief introduction, there are chapters on estimation, hypothesis testing, and maximum likelihood modeling. The book concludes;

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Copula Modeling

copulas is that estimation and inference are based on standard maximum likelihood procedures. Thus, copulas can be estimated using desktop;

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Cambridge Series in Statistical and Probabilistic Mathematics

-estimation, namely maximum likelihood and least squares. The theory also covers estimation methods using penalties and sieves. Many illustrative;

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Regression Models

model. Elaborating on the modeling of censored and sample-selected data via maximum likelihood, he shows the close links between the models;

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