arXiv subjects

Tomasz Żądło

Publications and source records attributed to Tomasz Żądło.

3 recordsLinked to original sources

arXiv2025

A step towards the integration of machine learning and classic model-based survey methods

The usage of machine learning methods in traditional surveys including official statistics, is still very limited. Therefore, we propose a predictor supported by these algorithms, which can be used to predict any population or subpopulation characteristics. Machine learning methods have already been shown to be very powerful in identifying and modelling complex and nonlinear relationships between the variables, which means they have very good properties in case of strong departures from the classic assumptions. Therefore, we analyse the performance of our proposal under a different set-up, which, in our opinion, is of greater importance in real-life surveys. We study only small departures from the assumed model to show that our proposal is a good alternative, even in comparison with optimal methods under the model. Moreover, we propose the method of the ex ante accuracy estimation of machine learning predictors, giving the possibility of the accuracy comparison with classic methods. The solution to this problem is indicated in the literature as one of the key issues in integrating these approaches. The simulation studies are based on a real, longitudinal dataset, where the prediction of subpopulation characteristics is considered.

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arXiv2025

Improving ex ante accuracy assessment in predicting house price dispersion: evidence from the USA

The study focuses on improving the ex ante prediction accuracy assessment in the case of forecasting various house price dispersion measures in the USA. It addresses a critical gap in real estate market forecasting by proposing a novel method for assessing ex ante prediction accuracy under unanticipated shocks. The proposal is based on a parametric bootstrap approach under a misspecified model, allowing for the simulation of future values and estimation of prediction errors in case of unexpected price changes. The study highlights the limitations of the traditional approach that fails to account for unforeseen market events and provides a more in-depth understanding of how prediction accuracy changes under unexpected scenarios. The proposed methods offers valuable insights for real estate market management by enabling more robust risk assessment and decision-making in the face of unexpected market fluctuations. Real data application is based on longitudinal U.S. data on real estate transactions.

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arXiv2024

WASP: Voting-based ex Ante method for Selecting joint Prediction strategy

This paper addresses the topic of choosing a prediction strategy when using parametric or nonparametric regression models. It emphasizes the importance of ex ante prediction accuracy, ensemble approaches, and forecasting not only the values of the dependent variable but also a function of these values, such as total income or median loss. It proposes a method for selecting a strategy for predicting the vector of functions of the dependent variable using various ex ante accuracy measures. The final decision is made through voting, where the candidates are prediction strategies and the voters are diverse prediction models with their respective prediction errors. Because the method is based on a Monte Carlo simulation, it allows for new scenarios, not previously observed, to be considered. The first part of the article provides a detailed theoretical description of the proposed method, while the second part presents its practical use in managing a portfolio of communication insurance. The example uses data from the Polish insurance market. All calculations are performed using the R programme.

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