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.
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Kharin, Y. (2013). A Decision-Theoretic Approach to Forecasting. In: Robustness in Statistical Forecasting. Springer, Cham. https://doi.org/10.1007/978-3-319-00840-0_2
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DOI: https://doi.org/10.1007/978-3-319-00840-0_2
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