Statistical inference under order restrictions on both rows and columns of a matrix, with an application in toxicology

TL;DR

This paper introduces a new iterative algorithm for statistical inference on matrix parameters with order restrictions on both rows and columns, applicable to various designs and validated through simulations and a toxicology case study.

Contribution

It develops a broad, convergent iterative algorithm for order-restricted inference on matrices, extending previous methods and including a bootstrap test for practical application.

Findings

Algorithm converges under broad conditions

Bootstrap test performs well in simulations

Method applied successfully to toxicology data

Abstract

We present a general methodology for performing statistical inference on the components of a real-valued matrix parameter for which rows and columns are subject to order restrictions. The proposed estimation procedure is based on an iterative algorithm developed by Dykstra and Robertson (1982) for simple order restriction on rows and columns of a matrix. For any order restrictions on rows and columns of a matrix, sufficient conditions are derived for the algorithm to converge in a single application of row and column operations. The new algorithm is applicable to a broad collection of order restrictions. In practice, it is easy to design a study such that the sufficient conditions derived in this paper are satisfied. For instance, the sufficient conditions are satisfied in a balanced design. Using the estimation procedure developed in this article, a bootstrap test for order restrictions on rows and columns of a matrix is proposed. Computer simulations for ordinal data were performed to compare the proposed test with some existing test procedures in terms of size and power. The new methodology is illustrated by applying it to a set of ordinal data obtained from a toxicological study.