An algorithm to construct Monte Carlo confidence intervals for an arbitrary function of probability distribution parameters

Tyralis, H; Koutsoyiannis, D; Kozanis, S

dc.contributor.author	Tyralis, H	en
dc.contributor.author	Koutsoyiannis, D	en
dc.contributor.author	Kozanis, S	en
dc.date.accessioned	2014-03-01T11:46:39Z
dc.date.available	2014-03-01T11:46:39Z
dc.date.issued	2012	en
dc.identifier.issn	09434062	en
dc.identifier.uri	https://dspace.lib.ntua.gr/xmlui/handle/123456789/37997
dc.subject	Confidence interval	en
dc.subject	Hydrological statistics	en
dc.subject	Location-scale family	en
dc.subject	Maximum likelihood	en
dc.subject	Monte Carlo	en
dc.title	An algorithm to construct Monte Carlo confidence intervals for an arbitrary function of probability distribution parameters	en
heal.type	other	en
heal.identifier.primary	10.1007/s00180-012-0364-7	en
heal.identifier.secondary	http://dx.doi.org/10.1007/s00180-012-0364-7	en
heal.publicationDate	2012	en
heal.abstract	We derive a new algorithm for calculating an exact confidence interval for a parameter of location or scale family, based on a two-sided hypothesis test on the parameter of interest, using some pivotal quantities. We use this algorithm to calculate approximate confidence intervals for the parameter or a function of the parameter of one-parameter continuous distributions. After appropriate heuristic modifications of the algorithm we use it to obtain approximate confidence intervals for a parameter or a function of parameters for multi-parameter continuous distributions. The advantage of the algorithm is that it is general and gives a fast approximation of an exact confidence interval. Some asymptotic (analytical) results are shown which validate the use of the method under certain regularity conditions. In addition, numerical results of the method compare well with those obtained by other known methods of the literature on the exponential, the normal, the gamma and the Weibull distribution. © 2012 Springer-Verlag.	en
heal.journalName	Computational Statistics	en
dc.identifier.doi	10.1007/s00180-012-0364-7	en
dc.identifier.spage	1	en
dc.identifier.epage	27	en