Review [of Peter Rawlings\u27 \u3cem\u3eHenry James and the Abuse of the Past\u3c/em\u3e]

[...] the abuse of the past becomes in James\u27s hands an art of fiction and the framework of an autobiography (67-68). According to Rawlings, Henry James\u27s late fiction specializes in constructing, within the volatile framework of philosophies of time then current, decadent mutations of America\u27s vanishing dreamers, characters arrested . . . by the forlorn realization that \u27we shall never be again as we were!\u27 (141-42)

On the occurrence times of componentwise maxima and bias in likelihood inference for multivariate max-stable distributions

Full likelihood-based inference for high-dimensional multivariate extreme value distributions, or max-stable processes, is feasible when incorporating occurrence times of the maxima; without this information, $d$-dimensional likelihood inference is usually precluded due to the large number of terms in the likelihood. However, some studies have noted bias when performing high-dimensional inference that incorporates such event information, particularly when dependence is weak. We elucidate this phenomenon, showing that for unbiased inference in moderate dimensions, dimension $d$ should be of a magnitude smaller than the square root of the number of vectors over which one takes the componentwise maximum. A bias reduction technique is suggested and illustrated on the extreme value logistic model.Comment: 7 page