Inference and Prediction for High Dimensional Data via Penalized Regression and Kernel Machine Methods
Contributors: Cai, Tianxi; DeGruttola, Victor; Lin, Xihong
Published: 2012
Language: eng
Description: Analysis of high dimensional data often seeks to identify a subset of important features and assess their effects on the outcome. Furthermore, the ultimate goal is often to build a prediction model with these features that accurately assesses risk for future subjects. Such statistical challenges arise in the study of genetic associations with health outcomes. However, accurate inference and prediction with genetic information remains challenging, in part due to the complexity in the genetic architecture of human health and disease.
Notes: Keywords: biostatistics, high dimensional data, kernel machine learning, prediction, statistical genetics, statistics.

Thesis Ph.D. Harvard University 2012