2002•Unpublished venueRequires access

A geometric approach to statistical estimation

Rudolf Kulhavý

Open publisher page 8 citations

Abstract

The role of Kerridge inaccuracy, Shannon entropy and Kullback-Leibler distance in statistical estimation is shown for both discrete and continuous observations. The cases of data independence and regression-type dependence are considered in parallel. Pythagorean-like relations valid for probability distributions are presented and their importance for estimation under compressed data is indicated.

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What this paper is about

The role of Kerridge inaccuracy, Shannon entropy and Kullback-Leibler distance in statistical estimation is shown for both discrete and continuous observations. The cases of data independence and regression-type dependence are considered in parallel. Pythagorean-like relations valid for probability distributions are presented and their importance for estimation under compressed data is indicated.

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Available abstract

The role of Kerridge inaccuracy, Shannon entropy and Kullback-Leibler distance in statistical estimation is shown for both discrete and continuous observations. The cases of data independence and regression-type dependence are considered in parallel. Pythagorean-like relations valid for probability distributions are presented and their importance for estimation under compressed data is indicated.

Key concepts: Entropy (arrow of time), Independence (probability theory), Pythagorean theorem, Kullback–Leibler divergence, Mathematics, Estimation, Computer science, Probability distribution

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