Maximum Likelihood Estimation: Logic and Practice
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- Synopsis
- In this volume the underlying logic and practice of maximum likelihood (ML) estimation is made clear by providing a general modeling framework that utilizes the tools of ML methods. This framework offers readers a flexible modeling strategy since it accommodates cases from the simplest linear models to the most complex nonlinear models that link a system of endogenous and exogenous variables with non-normal distributions. Using examples to illustrate the techniques of finding ML estimators and estimates, Eliason discusses: what properties are desirable in an estimator; basic techniques for finding ML solutions; the general form of the covariance matrix for ML estimates; the sampling distribution of ML estimators; the application of ML in the normal distribution as well as in other useful distributions; and some helpful illustrations of likelihoods.
- Copyright:
- 1993
Book Details
- Book Quality:
- Publisher Quality
- ISBN-13:
- 9781506315904
- Related ISBNs:
- 9781452209425, 9780803941076
- Publisher:
- SAGE Publications
- Date of Addition:
- 11/02/17
- Copyrighted By:
- Sage Publications, Inc.
- Adult content:
- No
- Language:
- English
- Has Image Descriptions:
- No
- Categories:
- Nonfiction, Social Studies
- Submitted By:
- Bookshare Staff
- Usage Restrictions:
- This is a copyrighted book.