In Example 4.1, estimate a variance transformation model with (log left(sigma_{i}^{2} ight)=xi_{0}+xi_{1} mu_{i}), and compare its fit

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In Example 4.1, estimate a variance transformation model with \(\log \left(\sigma_{i}^{2}\right)=\xi_{0}+\xi_{1} \mu_{i}\), and compare its fit with a constant variance normal linear regression using the log pseudo marginal likelihood (LPML). It may facilitate estimation (if using OpenBUGS or WinBUGS) to use a transformation applied to the precision rather than variance. Identify the five cases with the highest influence statistics \(K\left(y, y_{[i]}\right)\), as described in Chapter 2, and assess the impact on the regression coefficients on excluding these subjects from the analysis.

Data from Example 4.1

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