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Predictability, entropy and information of infinite transformations

Jon Aaronson, Kyewon Koh Park (2009)

Fundamenta Mathematicae

We show that a certain type of quasifinite, conservative, ergodic, measure preserving transformation always has a maximal zero entropy factor, generated by predictable sets. We also construct a conservative, ergodic, measure preserving transformation which is not quasifinite; and consider distribution asymptotics of information showing that e.g. for Boole's transformation, information is asymptotically mod-normal with normalization ∝ √n. Lastly, we show that certain ergodic, probability preserving...

Prediction of time series by statistical learning: general losses and fast rates

Pierre Alquier, Xiaoyin Li, Olivier Wintenberger (2013)

Dependence Modeling

We establish rates of convergences in statistical learning for time series forecasting. Using the PAC-Bayesian approach, slow rates of convergence √ d/n for the Gibbs estimator under the absolute loss were given in a previous work [7], where n is the sample size and d the dimension of the set of predictors. Under the same weak dependence conditions, we extend this result to any convex Lipschitz loss function. We also identify a condition on the parameter space that ensures similar rates for the...

Preface

Rama Cont, Jean-Pierre Fouque, Bernard Lapeyre (2007)

ESAIM: Probability and Statistics

Preservation of log-concavity on summation

Oliver Johnson, Christina Goldschmidt (2006)

ESAIM: Probability and Statistics

We extend Hoggar's theorem that the sum of two independent discrete-valued log-concave random variables is itself log-concave. We introduce conditions under which the result still holds for dependent variables. We argue that these conditions are natural by giving some applications. Firstly, we use our main theorem to give simple proofs of the log-concavity of the Stirling numbers of the second kind and of the Eulerian numbers. Secondly, we prove results concerning the log-concavity of the sum of...

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