6 In Example 14 4 consider the following variant on (14 12), namely log(i jk) M i j k i j (1i 2 j )k where 2 1 for unique labelling and each set of parameters sum to 1 ...

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6. In Example 14.4 consider the following variant on (14.12), namely log(i jk) = M + i...

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6. In Example 14.4 consider the following variant on (14.12), namely log(φi jk) = M + γi + δ j + ηk + αi j + (ω1i + ω2 j )ξk where ξ2 > ξ1 for unique labelling and each set of ω parameters sum to 1.

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Bayesian Statistical Modelling

ISBN: 9788126545131

2nd Edition

Authors: Peter D. Congdon

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Question Posted: Nov 07, 2024 06:00 AM

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6. In Example 14.4 consider the following variant on (14.12), namely log(i jk) = M + i + j + k + i j + (1i + 2 j )k where 2 > 1 for unique labelling and each set of parameters sum to 1.
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6. In Example 14.4 consider the following variant on (14.12), namely log(i jk) = M + i...

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Bayesian Statistical Modelling

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