Spatial Shrinkage Prior: A Probabilistic Approach to Model for Categorical Variables with Many Levels

Research output: Contribution to conferencePaperpeer-review

Abstract

One of the most commonly used methods to prevent overfitting and select relevant variables in regression models with many predictors is the penalized regression technique. Under such approaches, variable selection is performed in a non-probabilistic way, using some optimization criterion. A Bayesian approach to penalized regression has been proposed by assuming a prior distribution for the regression coefficients that plays a similar role as the penalty term in classical statistics: To shrink non-significant coefficients toward zero and assign a significant probability mass to non-negligible coefficients. These prior distributions, called shrinkage priors, usually assume independence among the covariates, which may not be an appropriate assumption in many cases. We propose two shrinkage priors to model the uncertainty about coefficients that are spatially correlated. The proposed priors are considered as an alternative approach to model the uncertainty about the coefficients of categorical variables with many levels. To illustrate their use, we consider the linear regression model. We evaluate the proposed method through several simulation studies.

Original languageEnglish (US)
DOIs
StatePublished - Aug 28 2023
Event2023 IEEE Colombian Conference on Applications of Computational Intelligence, ColCACI 2023 - Bogota, Colombia
Duration: Jul 26 2023Jul 28 2023

Conference

Conference2023 IEEE Colombian Conference on Applications of Computational Intelligence, ColCACI 2023
Country/TerritoryColombia
CityBogota
Period7/26/237/28/23

All Science Journal Classification (ASJC) codes

  • Artificial Intelligence
  • Computer Science Applications
  • Computer Vision and Pattern Recognition
  • Control and Optimization

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