Mean Absolute Deviations of Sample Means and Minimally Concentrated Binomials by Lutz Mattner
نویسنده
چکیده
This is a contribution to the theory of sums of independent random variables at the level of optimal explicit inequalities: we compute the optimal constants in Hornich’s lower bounds for the mean absolute deviations of sample means. This is done by reducing the original problem to the elementary one of determining the minimally concentrated binomial distributions Bn,p with fixed sample size parameter n.
منابع مشابه
Lutz Mattner Cumulants are universal homomorphisms into Hausdorff groups
This is a contribution to the theory of sums of independent random variables at an algebraico-analytical level: Let Prob∞(R) denote the convolution semigroup of all probability measures on R with all moments finite, topologized by polynomially weighted total variation. We prove that the cumulant sequence κ = (κ : ∈ N), regarded as a function from Prob∞(R) into the additive topological group R∞ ...
متن کاملA New Model for the Secondary Goal in DEA
The purpose of the current paper is to propose a new model for the secondary goal in DEA by introducing secondary objective function. The proposed new model minimizes the average of the absolute deviations of data points from their median. Similar problem is studied in a related model by Liang et al. (2008), which minimizes the average of the absolute deviations of data points from their mean. ...
متن کاملJAMES-STEIN TYPE ESTIMATORS IN LARGE SAMPLES WITH APPLICATION TO THE LEAST ABSOLUTE DEVIATIONS ESTIMATOR BY TAE-HWAN KIM AND HALBERT WHITE DISCUSSION PAPER 99-04R MAY 2000 James-Stein Type Estimators in Large Samples with Application to the Least Absolute Deviations Estimator
We explore the extension of James-Stein type estimators in a direction that enables them to preserve their superiority when the sample size goes to infinity. Instead of shrinking a base estimator towards a fixed point, we shrink it towards a data-dependent point. We provide an analytic expression for the asymptotic risk and bias of James-Stein type estimators shrunk towards a data-dependent poi...
متن کاملJames-Stein Type Estimators in Large Samples with Application to the Least Absolute Deviations Estimator
We explore the extension of James-Stein type estimators in a direction that enables them to preserve their superiority when the sample size goes to infinity. Instead of shrinking a base estimator towards a fixed point, we shrink it towards a data-dependent point. We provide an analytic expression for the asymptotic risk and bias of James-Stein type estimators shrunk towards a data-dependent poi...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
عنوان ژورنال:
دوره شماره
صفحات -
تاریخ انتشار 2003