dc.contributor.author | Pant, Mohan | |
dc.contributor.author | Headrick, Todd C. | |
dc.date.accessioned | 2017-02-08T21:56:03Z | |
dc.date.available | 2017-02-08T21:56:03Z | |
dc.date.issued | 2017 | |
dc.identifier.citation | Published in The Journal of Statistical and Econometric Methods 6(1):1-44, 2017 | en_US |
dc.identifier.issn | 1792-6602 | |
dc.identifier.issn | 1792-6939 | |
dc.identifier.uri | http://hdl.handle.net/10106/26353 | |
dc.description.abstract | Power method (PM) polynomials have been used for simulating non-normal
distributions in a variety of settings such as toxicology research, price risk,
business-cycle features, microarray analysis, computer adaptive testing, and
structural equation modeling. A majority of these applications are based on the
method of matching product moments (e.g., skew and kurtosis). However,
estimators of skew and kurtosis can be (a) substantially biased, (b) highly
dispersed, or (c) influenced by outliers. To address this limitation, two families of
double-uniform-PM and double-triangular-PM distributions are characterized
through the method of 𝐿-moments using a doubling technique. The 𝐿-moment
based procedure is contrasted with the method of product moments in the contexts
of fitting real data and estimation of parameters. A methodology for simulating
correlated double-uniform-PM and double-triangular-PM distributions with
specified values of 𝐿-skew, 𝐿-kurtosis, and 𝐿-correlation is also demonstrated.
Monte Carlo simulation results indicate that the L-moment-based estimators of
𝐿-skew, 𝐿-kurtosis, and 𝐿-correlation are superior to their product moment-based
counterparts. | |
dc.language.iso | en_US | en_US |
dc.publisher | Scienpress Ltd | en_US |
dc.subject | Skew matching | en_US |
dc.subject | Kurtosis matching | en_US |
dc.subject | Product moments | en_US |
dc.subject | L-moments | en_US |
dc.subject | L-correlations | en_US |
dc.title | Simulating Uniform- and Triangular- Based Double Power Method Distributions | en_US |
dc.type | Article | en_US |
dc.publisher.department | Department of Curriculum & Instruction, The University of Texas at Arlington | en_US |
dc.identifier.externalLink | https://www.scienpress.com/journal_focus.asp?main_id=68&Sub_id=IV&Issue=159407 | en_US |
dc.identifier.externalLinkDescription | The original publication is available at the journal homepage. | en_US |
dc.rights.license | Published open access through Scientific Press International Limited | |