ENBIS Spring Meeting 2018

4 – 6 June 2018; Florence, Italy Abstract submission: 17 November 2017 – 20 April 2018

Sequential Multi-Aspect Monitoring Multivariate and High-Dimensional Data

5 June 2018, 09:50 – 10:15

Abstract

Submitted by
Amitava Mukherjee
Authors
Amitava Mukherjee (XLRI -Xavier School of Management), Niladri Chakraborty (XLRI -Xavier School of Management), Marco Marozzi (University of Venice)
Abstract
In this paper, we propose a class of distribution-free sequential Phase-II monitoring schemes for multivariate and high-dimensional data. Instead of traditional monitoring of location shift, we consider monitoring multiple aspects of a multivariate population. We also indicate possible modification of the proposed procedure for high-dimensional data. Proposed techniques are based on the simple concepts of ranks, certain distance measures and the permutation tests. We study the performance of the proposed procedure in finite sample situations via Monte-Carlo. We also provide an illustrative example. Proposed method is expected to be very effective in monitoring high-dimensional business processes or multiple aspects related to an item quality.

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