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Erschienen in:
Roles of Modeling in Statistical Inference Kapitel lesen Erstes Kapitel lesen

2013 | OriginalPaper | Buchkapitel

2. Likelihood Construction and Estimation

verfasst von : Denni D Boos, L A Stefanski

Erschienen in: Essential Statistical Inference

Verlag: Springer New York

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Abstract

After a statistical model for the observed data has been formulated, the likelihood of the data is the natural starting point for inference in many statistical problems. This function typically leads to essentially automatic methods of inference, including point and interval estimation, and hypothesis testing. In fact, likelihood methods are generally asymptotically optimal provided the assumed statistical model is correct. Thus, the likelihood is the foundation of classical model-based statistical inference. In this chapter we describe, and often derive, basic likelihood methods and results. We start with construction of the likelihood in numerous examples and then move to various inferential techniques. The development in this chapter follows the classical frequentist approach to inference. However, the Bayesian approach to inference is equally dependent on the likelihood, and the material in this chapter is essential to understanding the Bayesian methods in Chapter 4.

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Vorheriges Kapitel Roles of Modeling in Statistical Inference
Nächstes Kapitel Likelihood-Based Tests and Confidence Regions
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Metadaten
Titel
Likelihood Construction and Estimation
verfasst von
Denni D Boos
L A Stefanski
Copyright-Jahr
2013
Verlag
Springer New York
Buch
Essential Statistical Inference

Print ISBN: 978-1-4614-4817-4

Electronic ISBN: 978-1-4614-4818-1

Copyright-Jahr: 2013

DOI
https://doi.org/10.1007/978-1-4614-4818-1_2

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