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Statistical Decision Theory: Methods and Applications

In: Statistics for Business and Financial Economics

Author

Listed:
  • Cheng-Few Lee

    (Rutgers University Business School, Department of Finance and Economics)

  • John C. Lee

    (Center for PBBEF Research)

  • Alice C. Lee

Abstract

In business, decision making is at the heart of management. Using statistics as a guide, this chapter introduces and examines decision making in business and economics in terms of statistical decision theory. The branch of statistics called statistical decision theory is sometimes termed Bayesian decision statistics, in honor of research presented over 200 years ago by the English philosopher the Reverend Thomas Bayes (1702–1761). Nevertheless, statistical decision theory is a new branch of statistics. Propelled by research by Howard Raiffa, John Pratt, and Leonard Savage (among others), it developed rapidly in the 1950s, and it now occupies an important place in statistical literature. In contrast to classical statistics, where the focus is on estimation, constructing intervals, and hypothesis testing, statistical decision theory focuses on the process of making a decision. In other words, it is concerned with the situation in which an individual, group, or corporation has several feasible alternative courses of action in an uncertain environment.

Suggested Citation

  • Cheng-Few Lee & John C. Lee & Alice C. Lee, 2013. "Statistical Decision Theory: Methods and Applications," Springer Books, in: Statistics for Business and Financial Economics, edition 3, chapter 0, pages 1065-1124, Springer.
    • Handle: RePEc:spr:sprchp:978-1-4614-5897-5_21
    DOI: 10.1007/978-1-4614-5897-5_21
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