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In statistical analysis, measuring a score of predictive performance is an important task. In many scientific fields, appropriate scores were tailored to tackle the problems at hand. A proper score is...
We describe the underlying probabilistic interpretation of alpha and beta divergences. We first show that beta divergences are inherently tied to Tweedie distributions, a particular type of exponentia...
This paper is devoted to robust estimation based on dual divergences estimators for parametric models in the framework of right censored data. We give limit laws of the proposed estimators and examine...
A general notion of bootstrapped $\phi$-divergences estimates constructed by exchangeably weighting sample is introduced.
We consider a Kullback-Leibler-based algorithmfor the stochastic multi-armed bandit prob- lem in the case of distributions with finite supports (not necessarily known beforehand), whose asymptotic r...
A new lower bound involving f-divergences between the underlying probability measures is proved for the minimax risk in estimation problems. The proof just uses the convexity of the function f and ...
An important tool to quantify the likeness of two probability measures are f–divergences, which have seen widespread application in statistics and information theory. An example is the total variati...
In this paper we propose the use of -divergences as test statistics to verify simple hypotheses about a one-dimensional parametric diffusion process dXt = b(Xt, )dt + (Xt, )dWt, from discrete ob...

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