arXiv subjects

Sjoerd Hermes

Publications and source records attributed to Sjoerd Hermes.

6 recordsLinked to original sources

arXiv2026

Multi-Attribute Preferences: A Transfer Learning Approach

We introduce a transfer-learning method based on the Bradley--Terry model for multi-attribute pairwise-comparison data. The aim is to estimate the log-worth parameters of one primary attribute while using information from related secondary attributes. The method first pools the primary data with data from informative secondary attributes. It then corrects this pooled estimate using the primary likelihood, with a ridge penalty controlling the size of the correction. When the informative set is unknown, we use held-out primary data to select secondary attributes. For a known informative set and under a pooled Bradley--Terry compatibility condition, we derive high-probability $\ell_\infty$ and $\ell_2$ error bounds. Under additional conditions, these bounds can have a smaller asymptotic order than the corresponding primary-only Bradley--Terry bounds. We also establish asymptotic normality for a one-step estimator. A simulation study evaluates the method under more general settings, and an application to consumer preferences for eba, a cassava-derived food product, illustrates its use and interpretation. An R package implementing the method is available at https://CRAN.R-project.org/package=BTTL.

stat.ME↗

arXiv2026

Hierarchical Causal Structure Learning

Traditional statistical approaches primarily model associations between variables, however many scientific and practical questions require causal methods instead. These methods typically rely on assumptions about an underlying structure and are often represented by a Directed Acyclic Graph (DAG). While causal structures can be learned for single-level data, hierarchical or multi-level settings, where units (e.g., plants, students or patients) are nested within groups (e.g., environments, schools or hospitals), lack suitable methods. This article addresses such settings. These multi-level structures frequently arise in fields such as agriculture, where plants grow within different environments. Building on nonlinear structural causal models, or additive noise models, we propose an approach that facilitates causal structure learning for hierarchical causal models with additive unobserved group-level effects and group-specific causal functions. We also propose a simulation-based manner to compute hard interventions for the estimated causal model. In a simulation study, we show that the proposed method is able to identify the underlying hierarchical causal mechanism. A winter-wheat example is used to showcase how the proposed method can be used in practice and how to interpret its results.

stat.ME↗

arXiv2025

Joint Learning from Heterogeneous Rank Data

The statistical modelling of ranking data has a long history and encompasses various perspectives on how observed rankings arise. One of the most common models, the Plackett-Luce model, is frequently used to aggregate rankings from multiple rankers into a single ranking that corresponds to the underlying quality of the ranked objects. Given that rankers frequently exhibit heterogeneous preferences, mixture-type models have been developed to group rankers with more or less homogeneous preferences together to reduce bias. However, occasionally, these preference groups are known a-priori. Under these circumstances, current practice consists of fitting Plackett-Luce models separately for each group. Nevertheless, there might be some commonalities between different groups of rankers, such that separate estimation implies a loss of information. We propose an extension of the Plackett-Luce model, the Sparse Fused Plackett-Luce model, that allows for joint learning of such heterogeneous rank data, whereby information from different groups is utilised to achieve better model performance. The observed rankings can be considered a function of variables pertaining to the ranked objects. As such, we allow for these types of variables, where information on the coefficients is shared across groups. Moreover, as not all variables might be relevant for the ranking of an object, we impose sparsity on the coefficients to improve interpretability, estimation and prediction of the model. Simulations studies indicate superior performance of the proposed method compared to existing approaches. To illustrate the usage and interpretation of the method, an application on data consisting of consumer preferences regarding various sweet potato varieties is provided. An R package containing the proposed methodology can be found on https://CRAN.R-project.org/package=SFPL.

stat.ME↗

arXiv2025

A Statistical Interpretation of Multi-Item Rating and Recommendation Problems

Ordinal user-provided ratings across multiple items are frequently encountered in both scientific and commercial applications. Whilst recommender systems are known to do well on these type of data from a predictive point of view, their typical reliance on large sample sizes and frequent lack of interpretability and uncertainty quantification limits their applicability in inferential problems. Taking a fully Bayesian approach, this article introduces a novel statistical method that is designed with interpretability and uncertainty quantification in mind. Whilst parametric assumptions ensure that the method is applicable to data with modest sample sizes, the model is simultaneously designed to remain flexible in order to handle a wide variety of situations. Model performance, i.e. parameter estimation and prediction, is shown by means of a simulation study, both on simulated data and against commonly used recommender systems on real data. These simulations indicate that the proposed method performs competitively. Finally, to illustrate the applicability of the proposed method on real life problems that are of interest to economists, the method is applied on speed dating data, where novel insights into the partner preference problem are obtained. An R package containing the proposed methodology can be found on https://CRAN.R-project.org/package=StatRec.

stat.ME↗

arXiv2024

A Spatial Autoregressive Graphical Model with Applications in Intercropping

Within the statistical literature, a significant gap exists in methods capable of modeling asymmetric multivariate spatial effects that elucidate the relationships underlying complex spatial phenomena. For such a phenomenon, observations at any location are expected to arise from a combination of within- and between- location effects, where the latter exhibit asymmetry. This asymmetry is represented by heterogeneous spatial effects between locations pertaining to different categories, that is, a feature inherent to each location in the data, such that based on the feature label, asymmetric spatial relations are postulated between neighbouring locations with different labels. Our novel approach synergises the principles of multivariate spatial autoregressive models and the Gaussian graphical model. This synergy enables us to effectively address the gap by accommodating asymmetric spatial relations, overcoming the usual constraints in spatial analyses. Using a Bayesian-estimation framework, the model performance is assessed in a simulation study. We apply the model on intercropping data, where spatial effects between different crops are unlikely to be symmetric, in order to illustrate the usage of the proposed methodology. An R package containing the proposed methodology can be found on https://CRAN.R-project.org/package=SAGM.

stat.ME↗

arXiv2022

Copula Graphical Models for Heterogeneous Mixed Data

This article proposes a graphical model that handles mixed-type, multi-group data. The motivation for such a model originates from real-world observational data, which often contain groups of samples obtained under heterogeneous conditions in space and time, potentially resulting in differences in network structure among groups. Therefore, the i.i.d. assumption is unrealistic, and fitting a single graphical model on all data results in a network that does not accurately represent the between group differences. In addition, real-world observational data is typically of mixed discrete-and-continuous type, violating the Gaussian assumption that is typical of graphical models, which leads to the model being unable to adequately recover the underlying graph structure. The proposed model takes into account these properties of data, by treating observed data as transformed latent Gaussian data, by means of the Gaussian copula, and thereby allowing for the attractive properties of the Gaussian distribution such as estimating the optimal number of model parameter using the inverse covariance matrix. The multi-group setting is addressed by jointly fitting a graphical model for each group, and applying the fused group penalty to fuse similar graphs together. In an extensive simulation study, the proposed model is evaluated against alternative models, where the proposed model is better able to recover the true underlying graph structure for different groups. Finally, the proposed model is applied on real production-ecological data pertaining to on-farm maize yield in order to showcase the added value of the proposed method in generating new hypotheses for production ecologists.

stat.ME↗