Name
Abstract | ||
|---|---|---|
We study the problem of sequential prediction of real-valued sequences under the squared error loss function. While refraining from any statistical and structural assumptions on the underlying sequence, we introduce a competitive approach to this problem and compare the performance of a sequential algorithm with respect to the large and continuous class of parametric predictors. We define the performance difference between a sequential algorithm and the best parametric predictor as “regret”, and introduce a guaranteed worst-case lower bounds to this relative performance measure. In particular, we prove that for any sequential algorithm, there always exists a sequence for which this regret is lower bounded by zero. We then extend this result by showing that the prediction problem can be transformed into a parameter estimation problem if the class of parametric predictors satisfy a certain property, and provide a comprehensive lower bound to this case. |
Year | Venue | Keywords |
|---|---|---|
2014 | Signal Processing Conference | functional analysis,prediction theory,sequences,comprehensive lower bound,guaranteed worst case lower bound,parametric prediction,real valued sequence,relative performance measure,sequential algorithm,sequential prediction,squared error loss function,Sequential prediction,lower bound,worst-case performance |
Field | DocType | ISSN |
Signal processing,Mathematical optimization,Regret,Upper and lower bounds,Mean squared error,Parametric statistics,Estimation theory,Sequential algorithm,Mathematics,Bounded function | Conference | 2076-1465 |
Citations | PageRank | References |
0 | 0.34 | 3 |
Authors | ||
4 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Nuri Denizcan Vanli | 1 | 77 | 6.77 |
| Muhammed O. Sayin | 2 | 77 | 5.77 |
| Salih Ergüt | 3 | 365 | 19.17 |
| S. S. Kozat | 4 | 194 | 16.72 |