Prediction intervals for an ordered observation from the logistic distribution based on censored samples

Wu, Tsong-Huey; Lu, Hai‐Lin

doi:10.1080/10629360600980161

Cited by 3 publications

(2 citation statements)

References 17 publications

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“…Bhaumik and Gibbons (2006) developed an approximate upper SPB for the gamma distribution. Wu and Lu (2007) developed prediction intervals for ordered observations from the logistic distribution based on censored data. Bhaumik et al (2008) constructed a one-sided SPB for left-censored data from the normal distribution.…”

Section: Literature Review and Contributions Of This Workmentioning

confidence: 99%

A general algorithm for computing simultaneous prediction intervals for the (log)-location-scale family of distributions

Xie

Hong

Escobar

et al. 2017

Journal of Statistical Computation and Simulation

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Making predictions of future realized values of random variables based on currently available data is a frequent task in statistical applications. In some applications, the interest is to obtain a two-sided simultaneous prediction interval (SPI) to contain at least k out of m future observations with a certain confidence level based on n previous observations from the same distribution. A closely related problem is to obtain a onesided upper (or lower) simultaneous prediction bound (SPB) to exceed (or be exceeded) by at least k out of m future observations. In this paper, we provide a general approach for computing SPIs and SPBs based on data from a particular member of the (log)-location-scale family of distributions with complete or right censored data. The proposed simulation-based procedure can provide exact coverage probability for complete and Type II censored data. For Type I censored data, our simulation results show that our procedure provides satisfactory results in small samples. We use three applications to illustrate the proposed simultaneous prediction intervals and bounds.

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Section: Literature Review and Contributions Of This Workmentioning

confidence: 99%

A general algorithm for computing simultaneous prediction intervals for the (log)-location-scale family of distributions

Xie

Hong

Escobar

et al. 2017

Journal of Statistical Computation and Simulation

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“…Abu-Dayyeh et al [5] discussed several estimators for the parameters of logistic distribution estimation using SRS and RSS. Wu and Lu [6] constructed different prediction intervals for an ordered observation from logistic distribution based on censored samples. McIntyre [7] was the first to suggest using RSS.…”

Section: Introductionmentioning

confidence: 99%

Confidence interval estimation of the location and scale parameters of the logistic distribution using pivotal method

Muttlak

Abu–Dayyeh

Al-Sawi

et al. 2011

Journal of Statistical Computation and Simulation

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Different confidence intervals (CI) will be constructed for the location and scale parameters of the logistic distribution by assuming that one of them is known. The maximum likelihood estimator and several different pivots will be used to construct different CI for the logistic parameters, using simple random sampling (SRS) and ranked set sampling (RSS). They will be compared via their expected lengths and the standard errors of their lengths using computer simulation. The CIs based on the RSS are found to be more efficient, i.e. having shorter expected lengths and smaller standard errors than their competitors based on the SRS.

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Prediction of Future Order Statistics from the Uniform Distribution

Alshami

2010

Advances in Intelligent and Soft Computing

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Prediction intervals for an ordered observation from the logistic distribution based on censored samples

Cited by 3 publications

References 17 publications

A general algorithm for computing simultaneous prediction intervals for the (log)-location-scale family of distributions

A general algorithm for computing simultaneous prediction intervals for the (log)-location-scale family of distributions

Confidence interval estimation of the location and scale parameters of the logistic distribution using pivotal method

Prediction of Future Order Statistics from the Uniform Distribution

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