Grant: Discovery Grants Program - Individual

There are a host of buzzwords in today's data-centric world. We encounter data in all walks of life, and for analytically and objectively minded people, data are crucial to their goals. However, making sense of the data and extracting meaningful information from it may not be an easy task. More and more contaminated data have emerged from different fields including signal processing, eCommerce business, financial economics, and genomic studies. Complex big data analysis is a very challenging but rewarding research area as data sets include a larger number of features, data contamination, structured pattern, and so on. The existence of many predictors for a relatively small number of subjects in the big data era are also defined as high-dimensional data (HDD). Some examples of HDD that have prompted demand are gene expression arrays, social network modeling, clinical, genetics, and phenotypic data. The rapid growth in the size and scope of data sets in a host of disciplines has created a need for innovative statistical strategies for understanding and making sense of such data. A variety of statistical and computational tools are needed to reveal the story that is contained in the data. Although the buzzword "Big Data" is nebulously defined, its problems are real, and statisticians must play a vital role in this data-centric world. Undoubtedly, overcoming the challenges of HDD is key to successful research in a host of fields. Clearly, there is an increasing demand for efficient prediction strategies for analyzing HDD. Shrinkage analysis has been one of my main research fields for many years. . Most of the existing methods for dealing with HDD begin with selection of a submodel for further investigation. Penalized methods are unstable and biased unless very stringent conditions are imposed. This research proposal in HDD focusses on post selection strategies to combat some of the issues inherited in penalized methods. The overarching objective is to provide answers to the question "what are the tools and tricks, pitfalls, applications, challenges and opportunities in HDD analysis". This research proposal provides a framework for HDD shrinkage analysis when both strong signals and weak signals co-exist and emphasizes that statisticians can play a dominant role in solving HDD problems and will move statisticians from the cellar of the scientific discovery to the penthouse. The proposed research will provide opportunities for training highly qualified personnel at all levels. The training will be three-fold, methodological, coding/computational, and analysis of data from real-life problems. More public and private sectors are now acknowledging the importance of statistical tools and its critical role in analyzing Big Data. There are millions of jobs available globally for Big Data analysts. This proposal will train individuals for these lucrative positions.