Binary functional linear models under choice-based sampling

TL;DR: Using MISFIT, this work is able to clearly demonstrate that the development of pathologic conditions related to macrocephaly is associated with both the overall head circumference of the children as well as the velocity of their head growth.

TL;DR: In this paper, a nonparametric scan method for functional data indexed in space is introduced and associated scan statistic is derived from the Wilcoxon-Mann-Whitney test statistic defined for infinite dimensional data.

TL;DR: In this paper , a new approach, called MISFIT, is presented for fitting generalized functional linear regression models with sparsely and irregularly sampled data, which has the potential to produce consistent estimates without such an assumption.

TL;DR: A new spatial scan statistic designed to adjust cluster detection for longitudinal confounding factors indexed in space is introduced and it is found that the cluster detection procedure is adjusted more accurately than other methods.

TL;DR: The authors study rare events data, binary dependent variables with dozens to thousands of times fewer events than zeros (nonevents) and recommend corrections that outperform existing methods and change the estimates of absolute and relative risks by as much as some estimated effects reported in the literature.

TL;DR: It is shown that more efficient sampling designs exist for making valid inferences, such as sampling all available events and a tiny fraction of nonevents, which enables scholars to save as much as 99% of their (nonfixed) data collection costs or to collect much more meaningful explanatory variables.

TL;DR: In this article, an introductory text in functional analysis aimed at the graduate student with a firm background in integration and measure theory is presented, which helps the student to develop an intuitive feel for the subject.