# Asymptotic based bootstrap approach for matched pairs with missingness in a single-arm

arXiv: https://arxiv.org/abs/1912.04902 · HTML: https://mlsanity.com/paper/1912.04902/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 10 Dec 2019.

## Built on by (most cited first, 2 of 2)

- [On the Relation between Prediction and Imputation Accuracy under Missing Covariates](https://mlsanity.com/paper/2112.05248/) (2021)
- [Hypothesis testing for matched pairs with missing data by maximum mean discrepancy: An application to continuous glucose monitoring](https://mlsanity.com/paper/2206.01590/) (2022)

## Abstract (arXiv)

The issue of missing values is an arising difficulty when dealing with paired data. Several test procedures are developed in the literature to tackle this problem. Some of them are even robust under deviations and control type-I error quite accurately. However, most these methods are not applicable when missing values are present only in a single arm. For this case, we provide asymptotic correct resampling tests that are robust under heteroscedasticity and skewed distributions. The tests are based on a clever restructuring of all observed information in a quadratic form-type test statistic. An extensive simulation study is conducted exemplifying the tests for finite sample sizes under different missingness mechanisms. In addition, an illustrative data example based on a breast cancer gene study is analyzed.