VC-PCR: A Prediction Method based on Supervised Variable Selection and Clustering

Rebecca Marion, Johannes Lederer, Bernadette Govaerts and Rainer von Sachs
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Rebecca Marion: Université catholique de Louvain, LIDAM/ISBA, Belgium
Bernadette Govaerts: Université catholique de Louvain, LIDAM/ISBA, Belgium
Rainer von Sachs: Université catholique de Louvain, LIDAM/ISBA, Belgium

No 2021040, LIDAM Discussion Papers 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. there are 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 simultaneously. This paper presents Variable Cluster Principal Component Regression (VC-PCR), a prediction method that supervises 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 achieves better prediction, variable selection and clustering performance when cluster structure is present.

Keywords: Variable clustering; dimensionality reduction; nonnegative matrix factorization; latent variables; sparsity; prediction (search for similar items in EconPapers)
Pages: 37
Date: 2021-12-17
New Economics Papers: this item is included in nep-ecm and nep-ore
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