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A Decision-Theoretic Approach to Forecasting

In: Robustness in Statistical Forecasting

Author

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  • Yuriy Kharin

    (Belarusian State University, Department of Mathematical Modeling and Data Analysis)

Abstract

Statistical forecasting is prediction of future states of a certain process based on the available stochastic observations as well as the available prior model assumptions made about this process. This chapter describes a general (universal) approach to statistical forecasting based on mathematical decision theory, including a brief discussion of discriminant analysis. The following fundamental notions are introduced: optimal and suboptimal forecasts, loss function, risk functional, minimax, admissible, and Bayesian decision rules (BDRs), Bayesian forecast density, decision rule randomization, plug-in principle.

Suggested Citation

  • Yuriy Kharin, 2013. "A Decision-Theoretic Approach to Forecasting," Springer Books, in: Robustness in Statistical Forecasting, edition 127, chapter 0, pages 7-29, Springer.
    • Handle: RePEc:spr:sprchp:978-3-319-00840-0_2
    DOI: 10.1007/978-3-319-00840-0_2
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