A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2017; you can also visit the original URL.
The file type is application/pdf
.
Aggregation of predictors for nonstationary sub-linear processes and online adaptive forecasting of time varying autoregressive processes
2015
Annals of Statistics
In this work, we study the problem of aggregating a finite number of predictors for nonstationary sub-linear processes. We provide oracle inequalities relying essentially on three ingredients: (1) a uniform bound of the ℓ^1 norm of the time varying sub-linear coefficients, (2) a Lipschitz assumption on the predictors and (3) moment conditions on the noise appearing in the linear representation. Two kinds of aggregations are considered giving rise to different moment conditions on the noise and
doi:10.1214/15-aos1345
fatcat:bm7ym544ozh7jgiwf24xgklkua