Leave-Group-Out Cross-Validation for Latent Gaussian Models

  • Zhedong Liu

Student thesis: Doctoral Thesis


Cross-validation is a widely used technique in statistics and machine learning for predictive performance assessment and model selection. It involves dividing the available data into multiple sets, training the model on some of the data and testing it on the rest, and repeating this process multiple times. The goal of cross-validation is to assess the model’s predictive performance on unseen data. Two standard methods for cross-validation are leave-one-out cross-validation and K-fold cross-validation. However, these methods may not be suitable for structured models with many potential prediction tasks, as they do not take into account the structure of the data. As a solution, leave-group-out cross-validation is an extension of cross-validation that allows the left-out groups to make training sets and testing points to adapt to different prediction tasks. In this dissertation, we propose an automatic group construction procedure for leave-group-out cross-validation to estimate the predictive performance of the model when the prediction task is not specified. We also propose an efficient approximation of leave-group-out cross-validation for latent Gaussian models. Both of these procedures are implemented in the R-INLA software. We demonstrate the usefulness of our proposed leave-group-out cross-validation method through its application in the joint modeling of survival data and longitudinal data. The example shows the effectiveness of this method in real-world scenarios.
Date of AwardApr 2023
Original languageEnglish (US)
Awarding Institution
  • Computer, Electrical and Mathematical Sciences and Engineering
SupervisorHaavard Rue (Supervisor)


  • Cross-Validation
  • Latent Gaussian Models

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