For a binary response, consider the random effects model logit P(Y it 1 u i ) t u i , t 1, , T, where u i are independent N(0, 2 ), and the marginal model logit P(Y t 1) t , t 1, , T For identifiability, T T 0 Explain why all t 0 implies that all t 0 Is the converse true ...

For a binary response, consider the random effects model logit[P(Y it = 1|u i )] =

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For a binary response, consider the random effects model

logit[P(Y_it = 1|u_i)] = α + β_t + u_i, t = 1,..., T,

where {u_i} are independent N(0, σ²), and the marginal model

logit[P(Y_t = 1)] = α + β_t^*, t = 1,..., T.

For identifiability, β_T = β_T^* = 0. Explain why all β_t = 0 implies that all β_t^* = 0. Is the converse true?

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Categorical Data Analysis

ISBN: 9780471360933

2nd Edition

Authors: Alan Agresti

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Question Posted: Nov 12, 2018 08:38 AM

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For a binary response, consider the random effects model logit[P(Y it = 1|u i )] =

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Categorical Data Analysis

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