7 papers
Optimal probabilistic forecasts: When do they work?
Gael M. Martin, Rubén Loaiza-Maya, David T. Frazier +2
Proper scoring rules are used to assess the out-of-sample accuracy of probabilistic forecasts, with different scoring rules rewarding distinct aspects of forecast performance. Here…
Computing Bayes: Bayesian Computation from 1763 to the 21st Century
Gael M. Martin, David T. Frazier, Christian P. Robert
The Bayesian statistical paradigm uses the language of probability to express uncertainty about the phenomena that generate observed data. Probability distributions thus characteri…
Focused Bayesian Prediction
Ruben Loaiza-Maya, Gael M. Martin, David T. Frazier
We propose a new method for conducting Bayesian prediction that delivers accurate predictions without correctly specifying the unknown true data generating process. A prior is defi…
Forecasting observables with particle filters: Any filter will do!
Patrick Leung, Catherine S. Forbes, Gael M. Martin +1
We investigate the impact of filter choice on forecast accuracy in state space models. The filters are used both to estimate the posterior distribution of the parameters, via a par…
Optimal Bias Correction of the Log-periodogram Estimator of the Fractional Parameter: A Jackknife Approach
Kanchana Nadarajah, Gael M Martin, Donald S Poskitt
We use the jackknife to bias correct the log-periodogram regression(LPR) estimator of the fractional parameter in a stationary fractionally integrated model. The weights for the ja…
Construction and Visualization of Optimal Confidence Sets for Frequentist Distributional Forecasts
David Harris, Gael M. Martin, Indeewara Perera +1
The focus of this paper is on the quantification of sampling variation in frequentist probabilistic forecasts. We propose a method of constructing confidence sets that respects the…