# A Regularized MANOVA Test for Semicontinuous High‐Dimensional Data

## TL;DR

This paper introduces a new MANOVA test for semicontinuous data that works even when the number of variables is larger than the sample size.

## Contribution

The novelty lies in the use of a regularized likelihood ratio test for high-dimensional semicontinuous data.

## Key findings

- The proposed test avoids computational overheads by using closed-form regularized estimators.
- Simulation studies show the test maintains good power and level performance.
- The method is applied to microRNA expression and plant invasion data, demonstrating its practical utility.

## Abstract

We propose a MANOVA test for semicontinuous data that is applicable also when the dimension exceeds the sample size. The test statistic is obtained as a likelihood ratio, where the numerator and denominator are computed at the maxima of penalized likelihood functions under each hypothesis. Closed form solutions for the regularized estimators allow us to avoid computational overheads. We derive the null distribution using a permutation scheme. The power and level of the resulting test are evaluated in a simulation study. We illustrate the new methodology with two original data analyses, one regarding microRNA expression in human blastocyst cultures, and another regarding alien plant species invasion in the island of Socotra (Yemen).