a. For a univariate response, how is quasi-likelihood (QL) inference different from ML inference? When are they

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a. For a univariate response, how is quasi-likelihood (QL) inference different from ML inference? When are they equivalent?

b. Explain the sense in which GEE methodology is a multivariate version of QL.

c. Summarize the advantages and disadvantages of the QL approach.

d. Describe conditions under which GEE parameter estimators are consistent and conditions under which they are not. For conditions in which they are consistent, explain why.

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Categorical Data Analysis

ISBN: 9780471360933

2nd Edition

Authors: Alan Agresti

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Question Posted: Oct 19, 2018 05:50 AM

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a. For a univariate response, how is quasi-likelihood (QL) inference different from ML inference? When are they

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a QL assumes only a variance structure but not a particular distribution They are equival...View the full answer

Categorical Data Analysis

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