I checked 7 public opinion journals on Friday, October 09, 2026 using the Crossref API. For the period October 02 to October 08, I found 2 new paper(s) in 2 journal(s).

Journal of Official Statistics

Applying Machine Learning to Longitudinal Administrative Data: A Case Study in Education
Fabrizio De Fausti, Marco Di Zio, Romina Filippini, Simona Toti
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This paper presents the results of an extensive empirical evaluation study of several machine learning methods used to predict the attained level of education (ALE) in Italy based exclusively on longitudinal administrative data. The assessment compares model predictions with observed ALE values available in administrative sources, evaluating both distributional preservation and unit-level prediction accuracy. Our findings show that machine learning techniques can effectively exploit longitudinal administrative information to generate accurate and coherent ALE estimates, providing valuable support for the production of register-based statistics.

Journal of Survey Statistics and Methodology

Spatially Balanced Sampling in Real Time
Wilmer Prentius, Fabio Carrer, Anton Grafström, Juha Heikkinen
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Probability sampling in real time or streaming scenarios presents unique challenges, especially when the population size and the spatial locations of units are unknown a priori. Standard methods like Poisson sampling can be applied but often result in spatially clustered samples that are not representative of the population, leading to inefficient estimation. We introduce a novel adaptation of spatially correlated Poisson sampling (SCPS) designed specifically for real-time applications. The proposed algorithm sequentially decides on the inclusion of newly encountered units by dynamically estimating the required spatial neighborhood for weight distribution, using only information from previously observed units and a running estimate of the total population size. Through Monte Carlo simulations on both uniform and clustered populations, we demonstrate that the method greatly improves spatial balance compared to conventional Poisson sampling. Furthermore, it substantially reduces the variability of the achieved sample size, offering more predictable survey costs and effort. This approach provides a practical and efficient tool for obtaining spatially balanced samples from populations where a complete sampling frame is unavailable in advance.