Arbeitspapier
Non-Bayesian testing of a stochastic prediction
We propose a method to test a prediction of the distribution of a stochastic process. In a non-Bayesian non-parametric setting, a predicted distribution is tested using a realization of the stochastic process. A test associates a set of realizations for each predicted distribution, on which the prediction passes. So that there are no type I errors, a prediction assigns probability 1 to its test set. Nevertheless, these sets are .small., in the sense that .most.distributions assign it probability 0, and hence there are .few. type II errors. It is also shown that there exists such a test that cannot be manipulated, in the sense that an uninformed predictor who is pretending to know the true distribution is guaranteed to fail on an uncountable number of realizations, no matter what randomized prediction he employs. The notion of a small set we use is category I, described in more detail in the paper.
- Sprache
-
Englisch
- Erschienen in
-
Series: Discussion Paper ; No. 1418
- Klassifikation
-
Wirtschaft
- Thema
-
Stochastischer Prozess
Statistischer Test
Wahrscheinlichkeitsrechnung
Theorie
- Ereignis
-
Geistige Schöpfung
- (wer)
-
Dekel, Eddie
Feinberg, Yossi
- Ereignis
-
Veröffentlichung
- (wer)
-
Northwestern University, Kellogg School of Management, Center for Mathematical Studies in Economics and Management Science
- (wo)
-
Evanston, IL
- (wann)
-
2005
- Handle
- Letzte Aktualisierung
-
10.03.2025, 11:45 MEZ
Datenpartner
ZBW - Deutsche Zentralbibliothek für Wirtschaftswissenschaften - Leibniz-Informationszentrum Wirtschaft. Bei Fragen zum Objekt wenden Sie sich bitte an den Datenpartner.
Objekttyp
- Arbeitspapier
Beteiligte
- Dekel, Eddie
- Feinberg, Yossi
- Northwestern University, Kellogg School of Management, Center for Mathematical Studies in Economics and Management Science
Entstanden
- 2005
Ähnliche Objekte (12)
Bayesian prediction for stochastic processes
Bayesian prediction for stochastic process models in reliability
Bayesian Testing for Exogenous Partition Structures in Stochastic Block Models
Uncertainty quantification of elastic material responses: testing, stochastic calibration and Bayesian model selection
Bayesian semiparametric stochastic volatility modeling
BaPreStoPro: an R package for Bayesian prediction of stochastic processes
Stochastic Analytic Continuation: A Bayesian Approach
Testing for stochastic monotonicity
A Bayesian semiparametric realized stochastic volatility model
Stochastic spectral methods for linear Bayesian inference
Robust Bayesian inference in stochastic frontier models
Stochastic Spectral Methods for Linear Bayesian Inference
Bayesian prediction for stochastic processes
Bayesian prediction for stochastic process models in reliability
Bayesian Testing for Exogenous Partition Structures in Stochastic Block Models
Uncertainty quantification of elastic material responses: testing, stochastic calibration and Bayesian model selection
Bayesian semiparametric stochastic volatility modeling
BaPreStoPro: an R package for Bayesian prediction of stochastic processes
Stochastic Analytic Continuation: A Bayesian Approach
Testing for stochastic monotonicity
A Bayesian semiparametric realized stochastic volatility model
Stochastic spectral methods for linear Bayesian inference
Robust Bayesian inference in stochastic frontier models
Stochastic Spectral Methods for Linear Bayesian Inference
Bayesian prediction for stochastic processes
Bayesian prediction for stochastic process models in reliability
Bayesian Testing for Exogenous Partition Structures in Stochastic Block Models
Uncertainty quantification of elastic material responses: testing, stochastic calibration and Bayesian model selection
Bayesian semiparametric stochastic volatility modeling
BaPreStoPro: an R package for Bayesian prediction of stochastic processes
Stochastic Analytic Continuation: A Bayesian Approach
Testing for stochastic monotonicity
A Bayesian semiparametric realized stochastic volatility model
Stochastic spectral methods for linear Bayesian inference
Robust Bayesian inference in stochastic frontier models