Posterior contraction in group sparse logit models for categorical responses

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This paper studies posterior contraction rates in multi-category logit models with priors incorporating group sparse structures. We consider a general class of logit models that includes the well-known multinomial logit models as a special case. Group sparsity is useful when predictor variables are naturally clustered and particularly useful for variable selection in the multinomial logit models. We provide a unified platform for posterior contraction rates of group-sparse logit models that include binary logistic regression under individual sparsity. No size restriction is directly imposed on the true signal in this study. In addition to establishing the first-ever contraction properties for multi-category logit models under group sparsity, this work also refines recent findings on the Bayesian theory of binary logistic regression.

Original languageEnglish
Pages (from-to)266-278
Number of pages13
JournalJournal of Statistical Planning and Inference
Publication statusPublished - 2022 Jul

Bibliographical note

Funding Information:
This research was supported by the Yonsei University Research Fund of 2021-22-0032 (South Korea).

Publisher Copyright:
© 2022 Elsevier B.V.

All Science Journal Classification (ASJC) codes

  • Statistics and Probability
  • Statistics, Probability and Uncertainty
  • Applied Mathematics


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