Advanced data analysis methods for tau-estimators

01 March 2013 → 31 August 2014
Regional and community funding: Special Research Fund
Research disciplines
  • Natural sciences
    • Applied mathematics in specific fields
    • Statistics and numerical methods
categorical covariates resampling methods model selection robust inference missing data
Project description

Development of model building, model selection, confidence intervals, testing, and prediction methods for the robust and efficient tau-estimators. Extend tau-estimators and robust LARS so that they can handle categorical covariates and missing data. Robust inference procedures will be based on robust and computationally efficient resampling methods.