Statistical Methods for Modeling High-Dimensional and Complex Data: Second Edition

Dear Colleagues,

Statistical models help us to understand the structure of systems or processes in various fields of engineering, natural sciences, and social sciences. One of the most important tasks in statistics is the development of methods and theories for building statistical models for datasets, which are approximations of the reality embodied in the observed data. In general, such models are not unique. For a given set of competing models, it is important to choose the best approximation model among them before performing statistical analysis.

Since data often exhibit complex structures, statistical models are expected to capture this complexity, which can further deepen our understanding of the underlying data-generating mechanisms and advance related fields in science and engineering. This Special Issue calls for newly developed statistical methods to model high-dimensional, complex data, especially methods based on entropy or information theory.

Prof. Dr. Yuehua Wu
Guest Editor

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