000136002 001__ 136002
000136002 005__ 20240705134228.0
000136002 0247_ $$2doi$$a10.1007/s41060-024-00563-4
000136002 0248_ $$2sideral$$a139015
000136002 037__ $$aART-2024-139015
000136002 041__ $$aeng
000136002 100__ $$aMaya, R.
000136002 245__ $$aThe discrete new XLindley distribution and the associated autoregressive process
000136002 260__ $$c2024
000136002 5060_ $$aAccess copy available to the general public$$fUnrestricted
000136002 5203_ $$aThe continuous new XLindley distribution was introduced by Nawel et al. (IEEE Access 11:67220–67229, 2023) as a special case of the polynomial exponential distribution proposed by Beghriche et al. (Statist Transit New Ser 23:95–112, 2022). The current paper introduces the one-parameter discrete analogue distribution of the new XLindley model and studies its main statistical properties. In particular, closed-form expressions are provided for the moment-generating function, mean, variance, quantile function, hazard rate function and mean residual life. Moreover, the new distribution has discrete increasing failure rate and both overdispersed and underdispersed count data can be handled. The estimation of the unknown parameter can be performed by the maximum likelihood method, and a Monte Carlo simulation study reveals that this method provides satisfactory estimates. Additionally, a first-order integer-valued autoregressive process is constructed from the discrete distribution and, via a simulation study, the conditional maximum likelihood method is recommended for estimation purposes. In order to assess the usefulness in practical applications, the proposed distribution and the associated first-order autoregressive process are compared to other competing distributions and processes, using this end several real data sets. In the context of statistical quality control, finally a cumulative sum control chart is developed for monitoring the process mean. To illustrate its usefulness, both simulation and real data analysis are performed.
000136002 540__ $$9info:eu-repo/semantics/embargoedAccess$$aby-nc-nd$$uhttp://creativecommons.org/licenses/by-nc-nd/3.0/es/
000136002 655_4 $$ainfo:eu-repo/semantics/article$$vinfo:eu-repo/semantics/acceptedVersion
000136002 700__ $$0(orcid)0000-0003-3694-5228$$aJodrá, P.$$uUniversidad de Zaragoza
000136002 700__ $$aAswathy, S.
000136002 700__ $$aIrshad, M. R.
000136002 7102_ $$12007$$2265$$aUniversidad de Zaragoza$$bDpto. Métodos Estadísticos$$cÁrea Estadís. Investig. Opera.
000136002 773__ $$g(2024), [27 pp.]$$tInternational Journal of Data Science and Analytics$$x2364-415X
000136002 8564_ $$s612372$$uhttps://zaguan.unizar.es/record/136002/files/texto_completo.pdf$$yPostprint$$zinfo:eu-repo/date/embargoEnd/2025-05-24
000136002 8564_ $$s2275300$$uhttps://zaguan.unizar.es/record/136002/files/texto_completo.jpg?subformat=icon$$xicon$$yPostprint$$zinfo:eu-repo/date/embargoEnd/2025-05-24
000136002 909CO $$ooai:zaguan.unizar.es:136002$$particulos$$pdriver
000136002 951__ $$a2024-07-05-12:56:05
000136002 980__ $$aARTICLE