Read Introduction to Likelihood-Based Estimation and Inference

Introduction to Likelihood-Based Estimation and Inference

Heino Bohn Nielsen

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Description:Introduction to Likelihood-Based Estimation and Inference provides a thorough and self-contained introduction to statistical modelling within the framework of the likelihood function.Based on worked-through examples, the book presents the main principles for estimation of parameters using the likelihood approach, the principles for applying basic probability theory to characterize the stochastic properties of estimators and test statistics, as well as ideas for basic model control and misspecification testing. Examples include models for Bernoulli trials, models for Poisson count data, and the linear regression model as a special case of the likelihood analysis of a Gaussian model.The book also introduces numerical optimization of the likelihood function, which provides the tools for building and analyzing more complicated – and empirically relevant – cases.We have made it easy for you to find a PDF Ebooks without any digging. And by having access to our ebooks online or by storing it on your computer, you have convenient answers with Introduction to Likelihood-Based Estimation and Inference. To get started finding Introduction to Likelihood-Based Estimation and Inference, you are right to find our website which has a comprehensive collection of manuals listed.
Our library is the biggest of these that have literally hundreds of thousands of different products represented.

Pages: —

Format: PDF, EPUB & Kindle Edition

Publisher: Hans Reitzel

Release: 2017

ISBN

Description: Introduction to Likelihood-Based Estimation and Inference provides a thorough and self-contained introduction to statistical modelling within the framework of the likelihood function.Based on worked-through examples, the book presents the main principles for estimation of parameters using the likelihood approach, the principles for applying basic probability theory to characterize the stochastic properties of estimators and test statistics, as well as ideas for basic model control and misspecification testing. Examples include models for Bernoulli trials, models for Poisson count data, and the linear regression model as a special case of the likelihood analysis of a Gaussian model.The book also introduces numerical optimization of the likelihood function, which provides the tools for building and analyzing more complicated – and empirically relevant – cases.We have made it easy for you to find a PDF Ebooks without any digging. And by having access to our ebooks online or by storing it on your computer, you have convenient answers with Introduction to Likelihood-Based Estimation and Inference. To get started finding Introduction to Likelihood-Based Estimation and Inference, you are right to find our website which has a comprehensive collection of manuals listed.
Our library is the biggest of these that have literally hundreds of thousands of different products represented.

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Introduction to Likelihood-Based Estimation and Inference

Introduction to Likelihood-Based Estimation and Inference

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