A Selective Overview of Statistical Methods for High Dimensional Data
Abstract
High dimensional data arise new opportunities for diverse fields of scientific research in modern statistical application and challenges to data scientists. Modern technological development of statistical theory in massive data plays a pivotal role in contemporary scientific research and knowledge discovery. Furthermore, the massive sample size and high dimensionality have reshaped classical statistical thinking and modeling for the communities in scientific research. Scientists have to tackle the unique computational and statistical challenges in which the number of variables p is much larger than the number of observations n. In order to fulfill this objective, this paper emerges a comprehensive overview of statistical
analysis methods for high dimensional settings in various disciplines that are certainly important for enhancing the performance of the machine learning tasks and improving the decision making on big data.
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