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Article

On the Convergence and Law of Large Numbers for the Non-Euclidean Lp -Means

by
George Livadiotis
Space Science and Engineering, Southwest Research Institute, San Antonio, TX 78238, USA
Entropy 2017, 19(5), 217; https://doi.org/10.3390/e19050217
Submission received: 18 January 2017 / Revised: 13 April 2017 / Accepted: 9 May 2017 / Published: 11 May 2017

Abstract

This paper describes and proves two important theorems that compose the Law of Large Numbers for the non-Euclidean L p -means, known to be true for the Euclidean L 2 -means: Let the L p -mean estimator, which constitutes the specific functional that estimates the L p -mean of N independent and identically distributed random variables; then, (i) the expectation value of the L p -mean estimator equals the mean of the distributions of the random variables; and (ii) the limit N of the L p -mean estimator also equals the mean of the distributions.
Keywords: expectation values; variance; optimization; fitting methods; Lp Norms; time series analysis expectation values; variance; optimization; fitting methods; Lp Norms; time series analysis

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MDPI and ACS Style

Livadiotis, G. On the Convergence and Law of Large Numbers for the Non-Euclidean Lp -Means. Entropy 2017, 19, 217. https://doi.org/10.3390/e19050217

AMA Style

Livadiotis G. On the Convergence and Law of Large Numbers for the Non-Euclidean Lp -Means. Entropy. 2017; 19(5):217. https://doi.org/10.3390/e19050217

Chicago/Turabian Style

Livadiotis, George. 2017. "On the Convergence and Law of Large Numbers for the Non-Euclidean Lp -Means" Entropy 19, no. 5: 217. https://doi.org/10.3390/e19050217

APA Style

Livadiotis, G. (2017). On the Convergence and Law of Large Numbers for the Non-Euclidean Lp -Means. Entropy, 19(5), 217. https://doi.org/10.3390/e19050217

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