Lorentz JÄNTSCHI (lori) works ?id=140
- [id] => 140
- [recorddate] => 2007:08:19:19:20:08
- [lastupdate] => 2007:08:19:19:20:08
- [type] => conference
- [place] => Plovdiv, Bulgaria
- [subject] => informatics - fundamentals; informatics - models implementation; informatics - numerical optimization; mathematics - applied; mathematics - modeling; mathematics - statistics; medicine - informatics
- [relatedworks] => N/A
- [file] => ?f=140
- [mime] => application/pdf
- [size] => 219476
- [pubname] => Fourth International Conference of Applied Mathematics and Computing, August 12-18, 2007
- [pubinfo] => University of Chemical Technology and Metallurgy Sofia & Technical University of Plovdiv
- [pubkey] => Invited lecture, presented on August 13, from 18.00 to 18.30
- [workinfo] => p. 47
- [year] => 2007
- [title] => Are confidence intervals for binomial distributed samples an optimization meters?
- [authors] => Sorana D. BOLBOACĂ, Lorentz JÄNTSCHI
- [abstract] =>
The aim of the research was to develop an optimization procedure of computing confidence intervals for binomial distributed samples based.
An inductive algorithm is proposed method used to solve the problem of confidence intervals estimation for binomial proportions. The implemented optimization procedure uses two triangulations (varying simultaneously two pairs of three variables).
The optimization method was assessed in a simulation study for a significance level of 5%, and sample sizes that vary from six to one thousand and associated possible proportions. The obtained results are available online at the following address:
Overall, the optimization method performed better, the values of cumulative error function decreasing in average with 10%, depending on the sample sizes and the confidence intervals method with which it is compared.
The performances of the optimization method increase with increasing of the sample size, surprisingly because it is well known that the confidence interval methods that use the normal approximation hypothesis for a binomial distribution obtain good results with increasing of sample sizes.
- [keywords] => Optimization; Confidence interval; Binomial distribution; Contingency table
- [acknowledgment] => This research was partly supported by UEFISCSU Romania through project ET46/2006 and CNCSIS Romania through project AT93/2007.