2026-10-14T13:35:33Z https://api.figshare.com/v2/oai

oai:figshare.com:article/33985809 2026-09-24T13:41:54Z category_15 category_21 category_734 category_931 portal_87 item_type_3 month_year_09_2026

Hierarchical neural networks for clustered regression analysis: an application to income data in canada Sanjoy K. Sinha (25104222) Abhijit Sinha (25104225) Neuroscience Biotechnology Biological Sciences not elsewhere classified Information Systems not elsewhere classified Clustered data embedding hierarchical neural networks Huber-type loss linear mixed model <p>Neural networks have gained popularity in recent years due to increased computing power and the development of powerful software for machine learning. In the context of regression analysis, neural networks are often useful when there is uncertainty in the mean response structure or deviation from standard distributional assumptions. In this paper, we present a hierarchical neural network framework for regression analysis of clustered correlated data and investigate its predictive performance relative to the classical linear mixed model. Specifically, we examine how the neural network compares to commonly used linear mixed models for analyzing clustered correlated or repeated measures data under various types of model misspecifications, including nonlinearity in the mean response function and outliers in the response variable. To handle outliers, we use a Huber-type loss function when predicting outcomes with the neural network. The performance of the neural network is studied through a simulation. An application is provided using real data obtained from a household survey in Canada.</p> 2026-09-24T13:41:54Z Dataset Dataset 10.6084/m9.figshare.33985809.v1 https://figshare.com/articles/dataset/Hierarchical_neural_networks_for_clustered_regression_analysis_an_application_to_income_data_in_canada/33985809 CC BY 4.0