Description: Research in quantile regression and other semiparemetric methods has accelerated in recent years. These methods do not rely strictly on parametric likelihood but avoid the curse of dimensionality associated with many nonparametric models. Challenges remain in improving computational efficiency and statistical efficiency, in developing reliable inferential tools, and in extending the methodologies to address practical issues in applications. Those issues include missing data, multivariate responses, and high dimensional predictors. Existing methods need to be improved, and new methods need to be developed to meet those challenges.
This track focuses on quantile modeling and other semiparmetric methods in statistics and econometrics. Particular topics for contributions are:
Full papers containing a strong computational or data analytic component will be considered for publication in the Special Issue of Quantile Regression and Semiparametric Methods of the journal Computational Statistics and Data Analysis. All submissions must contain original unpublished work not being considered for publication elsewhere. Submissions will be refereed according to standard procedures for CSDA.