# Data Consistency Approach to Model Validation

arXiv: https://arxiv.org/abs/1808.05889 · HTML: https://mlsanity.com/paper/1808.05889/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 17 Aug 2018.

## Built on by (most cited first, 1 of 1)

- [Diagnostic Tool for Out-of-Sample Model Evaluation](https://mlsanity.com/paper/2206.10982/) (2022)

## Abstract (arXiv)

In scientific inference problems, the underlying statistical modeling assumptions have a crucial impact on the end results. There exist, however, only a few automatic means for validating these fundamental modelling assumptions. The contribution in this paper is a general criterion to evaluate the consistency of a set of statistical models with respect to observed data. This is achieved by automatically gauging the models' ability to generate data that is similar to the observed data. Importantly, the criterion follows from the model class itself and is therefore directly applicable to a broad range of inference problems with varying data types, ranging from independent univariate data to high-dimensional time-series. The proposed data consistency criterion is illustrated, evaluated and compared to several well-established methods using three synthetic and two real data sets.