Ordinal Mixed-Effects Random Forest

• URL: http://arxiv.org/abs/2406.03130v1
• Date: Wed, 5 Jun 2024 10:30:40 GMT
• Title: Ordinal Mixed-Effects Random Forest
• Authors: Giulia Bergonzoli, Lidia Rossi, Chiara Masci,
• Abstract summary: We propose an innovative statistical method, called Ordinal Mixed-Effect Random Forest (OMERF) It extends the use of random forest to the analysis of hierarchical data and ordinal responses.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: We propose an innovative statistical method, called Ordinal Mixed-Effect Random Forest (OMERF), that extends the use of random forest to the analysis of hierarchical data and ordinal responses. The model preserves the flexibility and ability of modeling complex patterns of both categorical and continuous variables, typical of tree-based ensemble methods, and, at the same time, takes into account the structure of hierarchical data, modeling the dependence structure induced by the grouping and allowing statistical inference at all data levels. A simulation study is conducted to validate the performance of the proposed method and to compare it to the one of other state-of-the art models. The application of OMERF is exemplified in a case study focusing on predicting students performances using data from the Programme for International Student Assessment (PISA) 2022. The model identifies discriminating student characteristics and estimates the school-effect.

Related papers

• Model-free Methods for Event History Analysis and Efficient Adjustment (PhD Thesis) [55.2480439325792]
  This thesis is a series of independent contributions to statistics unified by a model-free perspective. The first chapter elaborates on how a model-free perspective can be used to formulate flexible methods that leverage prediction techniques from machine learning. The second chapter studies the concept of local independence, which describes whether the evolution of one process is directly influenced by another.
  arXiv  Detail & Related papers  (2025-02-11T19:24:09Z)
• Supervised Score-Based Modeling by Gradient Boosting [49.556736252628745]
  We propose a Supervised Score-based Model (SSM) which can be viewed as a gradient boosting algorithm combining score matching. We provide a theoretical analysis of learning and sampling for SSM to balance inference time and prediction accuracy. Our model outperforms existing models in both accuracy and inference time.
  arXiv  Detail & Related papers  (2024-11-02T07:06:53Z)
• Tree-based variational inference for Poisson log-normal models [47.82745603191512]
  hierarchical trees are often used to organize entities based on proximity criteria. Current count-data models do not leverage this structured information. We introduce the PLN-Tree model as an extension of the PLN model for modeling hierarchical count data.
  arXiv  Detail & Related papers  (2024-06-25T08:24:35Z)
• Multi-Layer Personalized Federated Learning for Mitigating Biases in Student Predictive Analytics [8.642174401125263]
  We propose a Multi-Layer Personalized Federated Learning (MLPFL) methodology to optimize inference accuracy over different layers of student grouping criteria. In our approach, personalized models for individual student subgroups are derived from a global model. Experiments on three real-world online course datasets show significant improvements achieved by our approach over existing student modeling benchmarks.
  arXiv  Detail & Related papers  (2022-12-05T17:27:28Z)
• A Statistical-Modelling Approach to Feedforward Neural Network Model Selection [0.8287206589886881]
  Feedforward neural networks (FNNs) can be viewed as non-linear regression models. A novel model selection method is proposed using the Bayesian information criterion (BIC) for FNNs. The choice of BIC over out-of-sample performance leads to an increased probability of recovering the true model.
  arXiv  Detail & Related papers  (2022-07-09T11:07:04Z)
• Mixed Effects Neural ODE: A Variational Approximation for Analyzing the Dynamics of Panel Data [50.23363975709122]
  We propose a probabilistic model called ME-NODE to incorporate (fixed + random) mixed effects for analyzing panel data. We show that our model can be derived using smooth approximations of SDEs provided by the Wong-Zakai theorem. We then derive Evidence Based Lower Bounds for ME-NODE, and develop (efficient) training algorithms.
  arXiv  Detail & Related papers  (2022-02-18T22:41:51Z)
• Scalable Intervention Target Estimation in Linear Models [52.60799340056917]
  Current approaches to causal structure learning either work with known intervention targets or use hypothesis testing to discover the unknown intervention targets. This paper proposes a scalable and efficient algorithm that consistently identifies all intervention targets. The proposed algorithm can be used to also update a given observational Markov equivalence class into the interventional Markov equivalence class.
  arXiv  Detail & Related papers  (2021-11-15T03:16:56Z)
• Robust Finite Mixture Regression for Heterogeneous Targets [70.19798470463378]
  We propose an FMR model that finds sample clusters and jointly models multiple incomplete mixed-type targets simultaneously. We provide non-asymptotic oracle performance bounds for our model under a high-dimensional learning framework. The results show that our model can achieve state-of-the-art performance.
  arXiv  Detail & Related papers  (2020-10-12T03:27:07Z)
• An Epistemic Approach to the Formal Specification of Statistical Machine Learning [1.599072005190786]
  We introduce a formal model for supervised learning based on a Kripke model. We then formalize various notions of the classification performance, robustness, and fairness of statistical classifiers.
  arXiv  Detail & Related papers  (2020-04-27T12:16:45Z)
• A Bootstrap-based Method for Testing Network Similarity [0.0]
  This paper studies the matched network inference problem. The goal is to determine if two networks, defined on a common set of nodes, exhibit a specific form of similarity. Two notions of similarity are considered: (i) equality, i.e., testing whether the networks arise from the same random graph model, and (ii) scaling, i.e., testing whether their probability are proportional for some unknown scaling constant.
  arXiv  Detail & Related papers  (2019-11-15T20:50:22Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.