VC-PCR: A prediction method based on variable selection and clustering

Rebecca Marion, Johannes Lederer, Bernadette Goevarts and Rainer von Sachs ()
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Rebecca Marion: Université catholique de Louvain, LIDAM/ISBA, Belgium
Johannes Lederer: University of Hamburg
Bernadette Goevarts: Université catholique de Louvain, LIDAM/ISBA, Belgium
Rainer von Sachs: Université catholique de Louvain, LIDAM/ISBA, Belgium

No 2024023, LIDAM Reprints ISBA from Université catholique de Louvain, Institute of Statistics, Biostatistics and Actuarial Sciences (ISBA)

Abstract: Sparse linear prediction methods suffer from decreased prediction accuracy when the predictor variables have cluster structure (e.g., highly correlated groups of variables). To improve prediction accuracy, various methods have been proposed to identify variable clusters from the data and integrate cluster information into a sparse modeling process. But none of these methods achieve satisfactory performance for prediction, variable selection and variable clustering performed simultaneously. This paper presents Variable Cluster Principal Component Regression (VC-PCR), a prediction method that uses variable selection and variable clustering in order to solve this problem. Experiments with real and simulated data demonstrate that, compared to competitor methods, VC-PCR is the only method that achieves simultaneously good prediction, variable selection, and clustering performance when cluster structure is present.

Keywords: Dimensionality reduction; latent variables; nonnegative matrix factorization; sparsity; variable clustering (search for similar items in EconPapers)
Pages: 25
Date: 2024-08-20
Note: In: Statistica Neerlandica, 2024
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Persistent link: https://EconPapers.repec.org/RePEc:aiz:louvar:2024023

DOI: 10.1111/stan.12358

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