Large deviations analysis for the log-normal distribution

Bachelor thesis (2021)

Authors

J.R. Sijsenaar Electrical Engineering, Mathematics and Computer Science

Contributors

R.C. Kraaij Applied Probability - EEMCS (mentor)
P. Chen Statistics - EEMCS (graduation committee member)
Faculty
Electrical Engineering, Mathematics and Computer Science, Electrical Engineering, Mathematics and Computer Science
Copyright: © 2021 Jochem Sijsenaar
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Published Date

04-06-2021

Language

English

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Faculty

Electrical Engineering, Mathematics and Computer Science

Abstract

We inspect the behavior of the probability that a weighted sum of random variables with log-normal tails is greater than its expected value. Under the right conditions for the weights and the variance being set to 1; we were able to bound a suitable transformation of this probability with the upper bound being a fixed factor of the square root of e above the lower bound. Beyond this, we analyse the conditions on the weights and determine a method for letting the weights be random and give an example.
We end off by extending our result to general variance, where we see that the deviation between the lower and upper bound as well as the domain for the result are dependant on the variance.

Files

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