Lorentz JÄNTSCHI (lori) works ?id=172
- [id] => 172
- [recorddate] => 2008:10:21:20:00:15
- [lastupdate] => 2008:10:21:20:00:15
- [type] => article
- [place] => Sofia, Bulgaria
- [subject] => informatics - numerical optimization; mathematics - applied; mathematics - modeling; mathematics - number theory; mathematics - optimization; medicine - epidemiology; medicine - evidence based
- [relatedworks] => N/A
- [file] => ?f=172
- [mime] => application/pdf
- [size] => 101421
- [pubname] => International Journal of Pure and Applied Mathematics
- [pubinfo] => Academic Publications
- [pubkey] => ISSN 1311-8080
- [workinfo] => 47(1): 1-8, Zbl pre05492615
- [year] => 2008
- [title] => Optimized confidence intervals for binomial distributed samples
- [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 stands as 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.
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 toghether with 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.