. For the body fat data of Problem 12.2, compare the performance of at least three different smoothers used within an additive model of the form given in XXXXXXXXXXCompare the leave-one-out...

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For the body fat data of Problem 12.2, compare the performance of at least three different smoothers used within an additive model of the form given in (12.3). Compare the leave-one-out cross-validation mean squared prediction error for the different smoothers. Is one smoother superior to another in the additive model?

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Example 2.5 describes a generalized linear model for data derived from testing an algorithm for human face recognition. The data are available from the book website. The response variable is binary, with
Yi
= 1 if two images of the same person were correctly matched and
Yi
= 0 otherwise. There are three predictor variables. The first is the absolute difference in eye region mean pixel intensity between the two images of the
ith person. The second is the absolute difference in nose–cheek region mean pixel intensity between the two images. The third predictor compares pixel intensity variability between the two images. For each image of the
ith person, the median absolute deviation (a robust spread measure) of pixel intensity is computed in two image areas: the forehead and nose–cheek regions. The third predictor is the between image ratio of these within-image ratios. Fit a generalized additive model to these data. Plot your results and interpret. Compare your results with the fit of an ordinary logistic regression model.