William Li : Using Prior Information for Intelligent Factor Allocation and Design Selection

Posted by Min Yang , part of the Statistics and Data Science Seminar.

At

Feb. 27, 2019, 4 p.m.

In

636 SEO

Abstract

While literature on constructing efficient experimental designs has been plentiful, how best to incorporate prior information when assigning factors to the columns has received little attention. This talk summarizes a series of recent studies that focus on information of individual columns. For regular designs, we propose the individual word length pattern (iWLP) that can be used to rank columns. With prior information on how likely a factor is important, iWLP can be used to intelligently assign factors to columns, and select the best designs to accommodate such prior information. This criterion is then extended to study nonregular designs, which we denote as the individual generalized word length pattern (iGWLP). We illustrate how iGWLP helps to identify important differences in the aliasing that is likely otherwise missed. Given the complexity of characterizing partial aliasing, iGWLP will help practitioners make more informed assignment of factors to columns when utilizing nonregular fractions. The theoretical justifications of the proposed iGWLP are provided in terms of statistical model and projection properties. In the third part, we consider clear effects involving an individual column (iCE). Motivated by a real application, we introduce the clear effects pattern, derived from iCE, and propose a class of designs called maximized clear effects pattern (MCEP) designs. We compare MCEP designs with commonly used minimum aberration designs and MaxC2 designs that maximize the number of clear two-factor interaction. We also extend the definition of iCE and MCEP designs by considering blocking schemes.