x

Our Privacy Policy has changed. By using this site, you agree to the Privacy Policy.

Maximum Likelihood Estimation Logic and Practice

ISBN-10: 0803941072
ISBN-13: 9780803941076
Edition: 1993
Authors: Scott R. Eliason
List price: $19.00 Buy it from $16.65
eBook available
30 day, 100% satisfaction guarantee

If an item you ordered from TextbookRush does not meet your expectations due to an error on our part, simply fill out a return request and then return it by mail within 30 days of ordering it for a full refund of item cost.

Learn more about our returns policy

Description: In this volume the underlying logic and practice of maximum likelihood (ML) estimation is made clear by providing a general modelling framework that utilizes the tools of ML methods. This framework offers readers a flexible modelling strategy since  More...

Used Starting from $16.65
New Starting from $23.01
eBooks Starting from $18.00
Buy
what's this?
Rush Rewards U
Members Receive:
coins
coins
You have reached 400 XP and carrot coins. That is the daily max!
You could win $10,000

Get an entry for every item you buy, rent, or sell.

Study Briefs

Limited time offer: Get the first one free! (?)

All the information you need in one place! Each Study Brief is a summary of one specific subject; facts, figures, and explanations to help you learn faster.

Add to cart
Study Briefs
Calculus 1 Online content $4.95 $1.99
Add to cart
Study Briefs
Algebra Online content $4.95 $1.99
Add to cart
Study Briefs
Introduction to Logic Online content $4.95 $1.99
Add to cart
Study Briefs
Business Math Formulas Online content $4.95 $1.99

Customers also bought

Loading
Loading
Loading
Loading
Loading
Loading
Loading
Loading
Loading
Loading

Book details

List price: $19.00
Copyright year: 1993
Publisher: SAGE Publications, Incorporated
Publication date: 8/9/1993
Binding: Paperback
Pages: 96
Size: 5.50" wide x 8.50" long x 0.25" tall
Weight: 0.242
Language: English

In this volume the underlying logic and practice of maximum likelihood (ML) estimation is made clear by providing a general modelling framework that utilizes the tools of ML methods. This framework offers readers a flexible modelling 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.

Introduction
The Logic of Maximum Likelihood
A General Modeling Framework Using Maximum Likelihood Methods
An Introduction to Basic Estimation Techniques
Further Empirical Examples
Additional Likelihoods
Conclusions

×
Free shipping on orders over $35*

*A minimum purchase of $35 is required. Shipping is provided via FedEx SmartPost® and FedEx Express Saver®. Average delivery time is 1 – 5 business days, but is not guaranteed in that timeframe. Also allow 1 - 2 days for processing. Free shipping is eligible only in the continental United States and excludes Hawaii, Alaska and Puerto Rico. FedEx service marks used by permission."Marketplace" orders are not eligible for free or discounted shipping.

Learn more about the TextbookRush Marketplace.

×