A Sequential Framework for Composite Hypothesis Testing

Bar, Shahar; Tabrikian, Joseph

doi:10.1109/tsp.2018.2866813

Cited by 3 publications

(1 citation statement)

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“…Most SHT extend Wald's SPRT to settings with composite or multiple hypotheses, adaptive tests with LRs impacted by other processes, and sequential testing uncertainty. Bar and Tabrikian (2018) generalize the SPRT for composite nested and non-nested hypotheses.…”

Section: Introductionmentioning

confidence: 99%

Robust Bellman State Prediction with Learning and Model Preferences

Estey

2024

Preprint

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I contribute to stochastic modeling methodology in a theoretical framework spanning core decisions in the model's lifetime. These are predicting an out-of-sample unit's latent state even from non-series data, deciding when to start and stop learning about the state variable, and choosing models from important trade-offs. States evolve from linear dynamics with time-varying predictors and coefficients (drift) and generalized continuous noise (diffusion). Coefficients must address misprediction costs, data complexity, and distributional uncertainty (ambiguity) about the state's diffusion and stopping time. I exactly solve a stochastic dynamic program robust to worst-case costs from both uncertainties. The Bellman optimal coefficients extend generalized ridge regression by out-of-sample components impacting value changes given state changes. Performance issues trigger sequential analysis whether learning alternative models, given the effort, is better than keeping baseline. Learning is method-general and stops in fewest average attempts within decision errors. I derive preference functions for comparing models with state and cost-change constraints to decide a model, joint-time state and value distributions, and other properties beneficial to modelers.

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Section: Introductionmentioning

confidence: 99%