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Statistical Analysis of Upper Bound using Data with Uncertainties
Let $F$ be the unknown distribution of a non-negative continuous random variable. We would like to determine if $supp(F) \subseteq [0,c]$ where $c$ is a constant (a proposed upper bound). Instead of directly observing $X_1,...,X_n i.i.d. \sim F$, we only get to observe as data $Y_1,...,Y_n$ where $Y...
Autor principal: | |
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Lenguaje: | eng |
Publicado: |
2014
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Materias: | |
Acceso en línea: | http://cds.cern.ch/record/1748419 |
Sumario: | Let $F$ be the unknown distribution of a non-negative continuous random variable. We would like to determine if $supp(F) \subseteq [0,c]$ where $c$ is a constant (a proposed upper bound). Instead of directly observing $X_1,...,X_n i.i.d. \sim F$, we only get to observe as data $Y_1,...,Y_n$ where $Y_i = X_i + \epsilon_i$, with $\epsilon_i$ being random variables representing errors. In this paper, we will explore methods to handle this statistical problem for two primary cases - parametric and nonparametric. The data from deep inelastic scattering experiments on measurements of $R=\sigma_L / \sigma_T$ would be used to test code which has been written to implement the discussed methods. |
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