REMAXINT: a two-mode clustering-based method for statistical inference on two-way interaction

Zaheer Ahmed, Alberto Cassese, Gerard van Breukelen, Jan Schepers*

*Corresponding author for this work

Research output: Contribution to journalArticleAcademicpeer-review

Abstract

We present a novel method, REMAXINT, that captures the gist of two-way interaction in row by column (i.e., two-mode) data, with one observation per cell. REMAXINTis a probabilistic two-mode clustering model that yields two-mode partitions with maximal interaction between row and column clusters. For estimation of the parameters of REMAXINT, we maximize a conditional classification likelihood in which the random row (or column) main effects are conditioned out. For testing the null hypothesis of no interaction between row and column clusters, we propose a max - F test statistic and discuss its properties. We develop a Monte Carlo approach to obtain its sampling distribution under the null hypothesis. We evaluate the performance of the method through simulation studies. Specifically, for selected values of data size and (true) numbers of clusters, we obtain critical values of the max - F statistic, determine empirical Type I error rate of the proposed inferential procedure and study its power to reject the null hypothesis. Next, we show that the novel method is useful in a variety of applications by presenting two empirical case studies and end with some concluding remarks.

Original languageEnglish
Pages (from-to)987-1013
Number of pages27
JournalAdvances in Data Analysis and Classification
Volume15
Issue number4
Early online date27 Apr 2021
DOIs
Publication statusPublished - Dec 2021

Keywords

  • COMPLEXITIES
  • Conditional classification likelihood
  • DYNAMICS
  • ENVIRONMENT DATA
  • IDENTIFICATION
  • Interaction effect parameters
  • MODELS
  • NUMBER
  • PARAMETRIC BOOTSTRAP
  • PERSONALITY
  • SELECTION
  • TRIALS
  • Two-mode clustering
  • max - F test

Cite this