Density function smoothing using discrete F-transform

Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F61989100%3A27510%2F09%3A00020604" target="_blank" >RIV/61989100:27510/09:00020604 - isvavai.cz</a>
Result on the web
—
DOI - Digital Object Identifier
—

Result language
angličtina
Original language name
Density function smoothing using discrete F-transform
Original language description
There exist many approaches to the estimation of probability distribution function. A general principal is to reduce a noise (more precisely, a white noise) in data by means of e.g. stochastic processes, kernel regressions, integral transforms, wavelettransforms, or even fuzzy filters. Without applying such a method the data set would be deformed by a noise so that the resulting interpretation might be problematic or, what would be worse, misleading. Moreover, a construction of valuable models from such data is also complicated. In this paper we propose and apply a relatively new and more or less simple approach to filter a noise from data -- the fuzzy transform (F-transform) originally introduced in Perfilieva (2006). More particularly, we will introduce a filter using the direct and inverse F-transform and show that the filtered data have a smaller noise, i.e., the variance of the random variable describing a filtered data noise is smaller than the variance of the random variable e
Czech name
—
Czech description
—

Project
<a href="/en/project/GA402%2F08%2F1237" target="_blank" >GA402/08/1237: Application of complex Lévy processes in modeling of financial assets prices</a><br>
Continuities
S - Specificky vyzkum na vysokych skolach

Publication year
2009
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Similar results(10)