# Improving the output quality of official statistics based on machine learning algorithms

arXiv: https://arxiv.org/abs/2103.00834 · HTML: https://mlsanity.com/paper/2103.00834/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 1 Mar 2021.

## Built on by (most cited first, 5 of 5)

- [How to Correctly Report LLM-as-a-Judge Evaluations](https://mlsanity.com/paper/2511.21140/) (2025)
- [A step towards the integration of machine learning and classic model-based survey methods](https://mlsanity.com/paper/2402.07521/) (2024)
- [Continuous Sweep for Binary Quantification Learning](https://mlsanity.com/paper/2308.08387/) (2023)
- [Survey-aware Machine Learning: A Guideline for Valid Population Health Inference based on Scoping Review](https://mlsanity.com/paper/2605.08963/) (2026)
- [Machine Learning for Coding Retail Product Names to Consumer-Price Categories: A Rule-plus-Bag-of-Words Pipeline with Reliability-Weighted Human-in-the-Loop Labeling](https://mlsanity.com/paper/2606.02004/) (2026)

## Abstract (arXiv)

National statistical institutes currently investigate how to improve the output quality of official statistics based on machine learning algorithms. A key obstacle is concept drift, i.e., when the joint distribution of independent variables and a dependent (categorical) variable changes over time. Under concept drift, a statistical model requires regular updating to prevent it from becoming biased. However, updating a model asks for additional data, which are not always available. In the literature, we find a variety of bias correction methods as a promising solution. In the paper, we will compare two popular correction methods: the misclassification estimator and the calibration estimator. For prior probability shift (a specific type of concept drift), we investigate the two correction methods theoretically as well as experimentally. Our theoretical results are expressions for the bias and variance of both methods. As experimental result, we present a decision boundary (as a function of (a) model accuracy, (b) class distribution and (c) test set size) for the relative performance of the two methods. Close inspection of the results will provide a deep insight into the effect of prior probability shift on output quality, leading to practical recommendations on the use of machine learning algorithms in official statistics.