Rakhi Singh
IITB-Monash Research Academy
Journal of statistical theory and practice | 2018
Feng-Shun Chai; Ashish Das; Rakhi Singh
Under the multinomial logit model, designs for choice experiments are usually based on an a priori assumption that either only the main effects of the factors or the main effects and all two-factor interaction effects are to be estimated. However, in practice, there are situations where interest lies in the estimation of main plus some two-factor interaction effects. For example, interest on such specified two-factor interaction effects arise in situations when one or two factor(s) like price and/or brand of a product interact individually with the other factors of the product. For two-level choice experiments with n factors, we consider a model involving the main plus all two-factor interaction effects, with our interest lying in the estimation of the main effects and a specified set of two-factor interaction effects. The two-factor interaction effects of interest are either (i) one factor interacting with each of the remaining n − 1 factors or (ii) each of the two factors interacting with each of the remaining n − 2 factors. For the two models, we first characterize the information matrix and then construct universally optimal choice designs for choice set sizes 3 and 4.
Biometrika | 2015
Rakhi Singh; Feng-Shun Chai; Ashish Das
Archive | 2016
Ashish Das; Rakhi Singh
Statistics & Probability Letters | 2019
Rakhi Singh
Statistica Sinica | 2019
Rakhi Singh; Ashish Das; Feng-Shun Chai
Journal of Combinatorial Designs | 2018
Daniel Horsley; Rakhi Singh
Statistics & Probability Letters | 2017
Feng-Shun Chai; Ashish Das; Rakhi Singh
Metrika | 2017
Aloke Dey; Rakhi Singh; Ashish Das
Journal of Statistical Planning and Inference | 2017
Ching-Shui Cheng; Ashish Das; Rakhi Singh; Pi Wen Tsai
Archive | 2015
Rakhi Singh; Ashish Das
