A1071
Title: Beta regression for double-bounded response with correlated high-dimensional covariates
Authors: Jianxuan Liu - Syracuse University (United States) [presenting]
Abstract: Continuous responses measured on a standard unit interval (0,1) are ubiquitous in many scientific disciplines. Statistical models built upon a normal error structure do not generally work because they can produce biased estimates or result in predictions outside either bound. In real-life applications, data are often high-dimensional, correlated, and consist of a mixture of various data types. Little literature is available to address the unique data challenge. A semiparametric approach is proposed to analyze the association between a double-bounded response and high-dimensional correlated covariates of mixed types. The proposed method makes full use of all available data through one or several linear combinations of the covariates without losing information from the data. The only assumption made is that the response variable follows a beta distribution, and no additional assumption is required. The resulting estimators are consistent and efficient. The proposed method is illustrated in simulation studies and is demonstrated in a real-life data application. The semiparametric approach contributes to the sufficient dimension reduction literature for its novelty in investigating double-bounded response, which is absent in the current literature. A new tool is also provided for data practitioners to analyze the association between a popular unit interval response and mixed types of high-dimensional correlated covariates.
