Smoothing-inspired lack-of-fit tests based on ranks

TL;DR

This paper introduces a nonparametric, rank-based lack-of-fit test for assessing the effect of a regressor on a response variable, which is distribution-free, consistent against smooth alternatives, and has favorable efficiency properties.

Contribution

It develops a rank-based version of the order selection test with identical asymptotic distribution and provides exact critical values, enhancing robustness and applicability.

Findings

The rank-based test is distribution-free for all sample sizes.

It has asymptotic distribution identical to the raw data test.

The test shows high efficiency, comparable to Wilcoxon and t-tests.

Abstract

A rank-based test of the null hypothesis that a regressor has no effect on a response variable is proposed and analyzed. This test is identical in structure to the order selection test but with the raw data replaced by ranks. The test is nonparametric in that it is consistent against virtually any smooth alternative, and is completely distribution free for all sample sizes. The asymptotic distribution of the rank-based order selection statistic is obtained and seen to be the same as that of its raw data counterpart. Exact small sample critical values of the test statistic are provided as well. It is shown that the Pitman-Noether efficiency of the proposed rank test compares very favorably with that of the order selection test. In fact, their asymptotic relative efficiency is identical to that of the Wilcoxon signed rank and $t$-tests. An example involving microarray data illustrates the usefulness of the rank test in practice.