Piecewise linear approximation of empirical distributions under a Wasserstein distance constraint
| Autor: | Philipp Arbenz, William Guevara-Alarcón |
|---|---|
| Rok vydání: | 2018 |
| Předmět: |
Statistics and Probability
Applied Mathematics Monte Carlo method Univariate Sample (statistics) 0102 computer and information sciences Monte Carlo simulation empirical distribution piecewise linear approximation Wasserstein distance compression 01 natural sciences Empirical distribution function Set (abstract data type) Constraint (information theory) Piecewise linear function 010104 statistics & probability Distribution (mathematics) 010201 computation theory & mathematics Modeling and Simulation Applied mathematics 0101 mathematics Statistics Probability and Uncertainty Mathematics |
| Zdroj: | Journal of Statistical Computation and Simulation, vol. 88, no. 16, pp. 3193-3216 |
| ISSN: | 1563-5163 0094-9655 |
| DOI: | 10.1080/00949655.2018.1506454 |
| Popis: | Big data applications and Monte Carlo simulation results can nowadays easily contain data sets in the size of millions of entries. We consider the situation when the information on a large univariate data set or sample needs to be preserved, stored or transferred. We suggest an algorithm to approximate a univariate empirical distribution through a piecewise linear distribution which requires significantly less memory to store. The approximation is chosen in a computationally efficient manner, such that it preserves the mean, and its Wasserstein distance to the empirical distribution is sufficiently small. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
|