Discussion of A Brief Survey of Modern Optimization for Statisticians

نویسندگان

  • Yves Atchade
  • George Michailidis
چکیده

Optimization methods have always played a key role in the development of statistical methodology, but have become of critical importance for modern methods to analyze large size high-dimensional data. Lange, Choi and Zhua (henceforth LCZ) are to be commended for providing a comprehensive overview of optimization methods widely used in statistics. The paper discusses classical unconstrained optimization algorithms (steepest descent and variants), the majorization-maximization framework that has proved very useful in devising novel algorithms for a variety of statistical problems, and also provides a flavor of constrained optimization problems arising in high-dimensional statistics (e.g. regularization, matrix completion, etc.). The topics discussed and their accompanying examples focus on important classes of algorithms that have helped statisticians develop and solve complex models.

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تاریخ انتشار 2013