What question did this study set out to answer?

The study aims to enhance high-dimensional linear regression by addressing the challenges of categorical predictors with numerous levels.

April 14, 2026Journal of the Royal Statistical Society Series B (Statistical Methodology)

Modelling with categorical features via exact fusion and sparsity regularization

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The study aims to enhance high-dimensional linear regression by addressing the challenges of categorical predictors with numerous levels.
Developed a new estimation approach incorporating clustering and sparsity regularization.
Formulated mixed integer programming models to enable efficient estimation.
Implemented a custom row generation procedure to expedite computation.
Created a fast approximate algorithm based on block coordinate descent.
Established a dynamic programming algorithm for the univariate case.
The proposed estimator outperformed leading existing methods in numerical experiments.
The model demonstrated effective clustering of regression coefficients.
The approach provided strong theoretical guarantees for prediction and clustering performance.

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Implication

This research proposes a new method for improving regression prediction in high-dimensional settings, suggesting better accuracy with categorical features.

Cite This Study

Behdin et al. (2026) studied this question.

synapsesocial.com/papers/69ddd9b1e195c95cdefd7186 https://doi.org/10.1093/jrsssb/qkag062

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Implication

This research proposes a new method for improving regression prediction in high-dimensional settings, suggesting better accuracy with categorical features.

Modelling with categorical features via exact fusion and sparsity regularization

Key Points

Discussion

Implication

Cite This Study

Modelling with categorical features via exact fusion and sparsity regularization

Key Points

Discussion

Implication

Cite This Study

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