Open Access. Powered by Scholars. Published by Universities.®
Articles 1 - 2 of 2
Full-Text Articles in Entire DC Network
Optimizing The Expected Overlap Of Survey Samples Via The Northwest Corner Rule, Lenka Mach, Philip T. Reiss, Ioana Schiopu-Kratina
Optimizing The Expected Overlap Of Survey Samples Via The Northwest Corner Rule, Lenka Mach, Philip T. Reiss, Ioana Schiopu-Kratina
Philip T. Reiss
In survey sampling there is often a need to coordinate the selection of pairs of samples drawn from two overlapping populations so as to maximize or minimize their expected overlap, subject to constraints on the marginal probabilities determined by the respective designs. For instance, maximizing the expected overlap between repeated samples can stabilize the resulting estimates of change and reduce the costs of first contacts; minimizing the expected overlap can avoid overburdening respondents with multiple surveys. We focus on the important special case in which both samples are selected by simple random sampling without replacement (SRSWOR) conducted independently within each …
Regression With Signals And Images As Predictors, Philip T. Reiss
Regression With Signals And Images As Predictors, Philip T. Reiss
Philip T. Reiss
Signal regression and image regression, in which the outcomes are scalars and the predictors are one-dimensional signals or multidimensional images, are of interest in many scientific fields. The principal statistical challenge is how to reduce the dimension of the predictors in what would otherwise be a severely ill-posed problem. A pair of novel methods, functional principal component regression (FPCR) and functional partial least squares (FPLS), combine two existing approaches to the dimension reduction problem: selection of most relevant components, as is done in ordinary principal component regression (PCR) and partial least squares (PLS), and restriction of the coefficient function to …