We apply a general algorithm for merging prediction strategies (the. Aggregating Algorithm) to the problem of linear regression with the.
Abstract. We apply a general algorithm for merging prediction strategies (the Aggregating Algorithm) to the problem of linear regression with the square loss; ...
Abstract. A radically new approach to statistical modelling, which combines mathematical techniques of Bayesian statistics with the philosophy of the.
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We build an online algorithm competitive with all the experts of relevant models of this type and derive an upper bound on the cumulative loss of the algorithm.
We apply the Aggregating Algorithm to the problem of online regression under the square loss function. We develop an algorithm competitive with the ...
Dec 1, 1997 · We apply a general algorithm for merging prediction strategies (the Aggregating Algorithm) to the problem of linear regression with the ...
We build an online algorithm competitive with all the experts of relevant models of this type and derive an upper bound on the cumulative loss of the algorithm.
[PDF] Synthetic Control as Online Linear Regression - Jiafeng Chen
jiafengkevinchen.github.io › files
We show that synthetic control algorithms themselves are no-regret online algorithms and are in fact competitive against a wide class of matching or DID ...
Jul 31, 1998 · Competitive on-line linear regression · Linear Regression Analysis: Theory and Computing · The OWA operator in multiple linear regression.
PDF | We consider the on-line predictive version of the standard problem of linear regression; the goal is to predict each consecutive response given.