Robust estimation with exponentially tilted Hellinger distance
نویسندگان
چکیده
منابع مشابه
Minimum Hellinger Distance Estimation with Inlier Modification
Inference procedures based on the Hellinger distance provide attractive alternatives to likelihood based methods for the statistician. The minimum Hellinger distance estimator has full asymptotic efficiency under the model together with strong robustness properties under model misspecification. However, the Hellinger distance puts too large a weight on the inliers which appears to be the main r...
متن کاملOptimal robust estimates using the Hellinger distance
Optimal robust M-estimates of a multidimensional parameter are described using Hampel’s infinitesimal approach. The optimal estimates are derived by minimizing a measure of efficiency under the model, subject to a bounded measure of infinitesimal robustness. To this purpose we define measures of efficiency and infinitesimal sensitivity based on the Hellinger distance. We show that these two mea...
متن کاملHellinger distance
In this lecture, we will introduce a new notion of distance between probability distributions called Hellinger distance. Using some of the nice properties of this distance, we will generalize the fooling set argument for deterministic protocols to the randomized setting. We will then use this to prove a Ω(n) lower bound for the communication complexity of Disjointness. We will also see how this...
متن کاملExponentially Tilted Empirical Likelihood
Newey and Smith (2001) have recently shown that Empirical Likelihood (EL) exhibits desirable higher-order asymptotic properties, namely, that its O ¡ n−1 ¢ bias is particularly small and that biascorrected EL is higher-order efficient. Although EL possesses these properties when the model is correctly specified, this paper shows that the asymptotic variance of EL in the presence of model misspe...
متن کاملClass distribution estimation based on the Hellinger distance
Class distribution estimation (quantification) plays an important role in many practical classification problems. Firstly, it is important in order to adapt the classifier to the operational conditions when they differ from those assumed in learning. Additionally, there are some real domains where the quantification task is itself valuable due to the high variability of the class prior probabil...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
ژورنال
عنوان ژورنال: Journal of Econometrics
سال: 2020
ISSN: 0304-4076
DOI: 10.1016/j.jeconom.2020.03.027