Full-text resources of CEJSH and other databases are now available in the new Library of Science.
Visit https://bibliotekanauki.pl

Results found: 5

first rewind previous Page / 1 next fast forward last

Search results

Search:
in the keywords:  exponential distribution
help Sort By:

help Limit search:
first rewind previous Page / 1 next fast forward last
1
Publication available in full text mode
Content available

Wykorzystanie uogólnionego rozkładu gamma do generowania tablicy dwudzielczej

100%
Sulewski P.
Śląski Przegląd Statystyczny
|
2014
|
issue 12(18)
339-348
EN
The article is devoted to the generation of two-way table contents using the generalized gamma distribution (GG). It describes the generalized gamma random number generator and how to create a two-way table by means of the empirical distribution function and theoretical exponential distribution, which is a special case of GG.
2
Publication available in full text mode
Content available

Statistical inference of exponential record data under Kullback-Leiber divergence measure

88%
Awwad R. R. A., Abufoudeh G. K., Bdair O. M.
Statistics in Transition new series
|
2019
|
vol. 20
|
issue 2
1-14
EN
Based on one parameter exponential record data, we conduct statistical inferences (maximum likelihood estimator and Bayesian estimator) for the suggested model parameter. Our second aim is to predict the future (unobserved) records and to construct their corresponding prediction intervals based on observed set of records. In the estimation and prediction processes, we consider the square error loss and the Kullback-Leibler loss functions. Numerical simulations were conducted to evaluate the Bayesian point predictor for the future records. Finally, data analyses involving the times (in minutes) to breakdown of an insulating fluid between electrodes at voltage 34 kv have been performed to show the performance of the methods thus developed on estimation and prediction.
3
Publication available in full text mode
Content available

An EWMA control chart for the exponential distribution using repetitive sampling plan

88%
AZAM M., ASLAM M., JUN C.-H.
Operations Research and Decisions
|
2017
|
vol. 27
|
issue 2
5-19
EN
A new EWMA control chart has been proposed under repetitive sampling when a quantitative characteristic follows the exponential distribution. The properties of the proposed chart, including the average run lengths has been is compared with two existing control charts with the help of simulated data. An application of the proposed chart hs been illustrated using a healthcare data set.
4
Publication available in full text mode
Content available

Interval shrinkage estimation of the parameter of exponential distribution in the presence of outliers under loss functions

75%
Nasiri P.
Statistics in Transition new series
|
2022
|
vol. 23
|
issue 3
65-78
EN
In this paper, we studied estimators based on an interval shrinkage with equal weights point shrinkage estimators for all individual target points θ¯ ∈ (θ0, θ1) for exponentially distributed observations in the presence of outliers drawn from a uniform distribution. Estimators obtained from both shrinkage and interval shrinkage were compared, showing that the estimators obtained via the interval shrinkage method perform better. Symmetric and asymmetric loss functions were also used to calculate the estimators. Finally, a numerical study and illustrative examples were provided to describe the results.
5
Publication available in full text mode
Content available

New polynomial exponential distribution: properties and applications

75%
Beghriche A., Zeghdoudi H., Raman V., Chouia S.
Statistics in Transition new series
|
2022
|
vol. 23
|
issue 3
95-112
EN
The study describes the general concept of the XLindley distribution. Forms of density and hazard rate functions are investigated. Moreover, precise formulations for several numerical properties of distributions are derived. Extreme order statistics are established using stochastic ordering, the moment method, the maximum likelihood estimation, entropies and the limiting distribution. We demonstrate the new family's adaptability by applying it to a variety of real-world datasets.
first rewind previous Page / 1 next fast forward last
JavaScript is turned off in your web browser. Turn it on to take full advantage of this site, then refresh the page.