Ranked set sampling allocation models for multiple skewed variables: an application to agricultural data

Bocci, Chiara; Petrucci, Alessandra; Rocco, Emilia

doi:10.1007/s10651-009-0110-7

Cited by 10 publications

(3 citation statements)

References 7 publications

(18 reference statements)

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“…If we incorporate these estimators into (7) and solve it with respect to by using the iterative methods, the ML estimate of is obtained. Then, we insert this estimate value of in to the equation (8) and obtain the ML estimates of 1 and 2 . The iterative process, such as Newton-Raphson method, should converge quickly to its maximum in this case, if the initial guess is reasonably close to the actual solution, see Kundu and Gupta (2006).…”

Section: Estimator Ofmentioning

confidence: 99%

“…See Patil et al (1994), Kaur et al (1995) and Chen et al (2004) for complete review of the applications and the theoretical studies about RSS. RSS is very feasible to different areas such as for environmental studies, see Yu and Lam (1997), Barnett (1999) and Bocci et al (2010). There are lots of studies in literature for estimating the system reliability based on RSS data, see Mukhuti (2008a, 2008b), Muttlak et al (2010), Dong et al (2013), Mahdizadeh and Zamanzade (2016, 2018a, 2018b and Şenoğlu (2017, 2018).…”

Section: Introductionmentioning

confidence: 99%

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Estimation of Stress-Strength Reliability For Weibull Distribution Based on Type-II Right Censored Ranked Set Sampling Data

Akgül¹,

Şenoğlu²

2018

Pak.j.stat.oper.res.

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In this paper, we consider the estimation of stress-strength reliability Â under the type-II right censored data when the distributions of both the stress and the strength are Weibull. First, we discuss the estimation of Â based on simple random sampling (SRS). Then, we use the effective and the efficient alternative of SRS which is known to be the ranked set sampling (RSS) to estimate . In the estimation procedure of , we use two different approaches they are i) maximum likelihood (ML) which requires an iterative method and ii) modified maximum likelihood (MML) which has an explicit form. Monte-Carlo simulation study is performed to identify the efficient sampling method (i.e., SRS or RSS) and the efficient estimation method (i.e., ML or MML). Finally, strength and wind speed data sets are analyzed to illustrate the proposed methods in practice.

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Section: Estimator Ofmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Estimation of Stress-Strength Reliability For Weibull Distribution Based on Type-II Right Censored Ranked Set Sampling Data

Akgül¹,

Şenoğlu²

2018

Pak.j.stat.oper.res.

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“…It is appropriate to employ RSS in many fields where sampling schemes cost a lot of human strength and money. It has a widespread application in the domains of the environment, biology, engineering applications, and agriculture (see [17][18][19][20]). Recently, the authors in Ref.…”

Section: Introductionmentioning

confidence: 99%

Bayesian Inference for the Parameters of Kumaraswamy Distribution via Ranked Set Sampling

Jiang

Gui

2021

Symmetry

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In this paper, we address the estimation of the parameters for a two-parameter Kumaraswamy distribution by using the maximum likelihood and Bayesian methods based on simple random sampling, ranked set sampling, and maximum ranked set sampling with unequal samples. The Bayes loss functions used are symmetric and asymmetric. The Metropolis-Hastings-within-Gibbs algorithm was employed to calculate the Bayes point estimates and credible intervals. We illustrate a simulation experiment to compare the implications of the proposed point estimators in sense of bias, estimated risk, and relative efficiency as well as evaluate the interval estimators in terms of average confidence interval length and coverage percentage. Finally, a real-life example and remarks are presented.

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Ranked set sampling

Patil

Sürücü

Egemen

2012

Encyclopedia of Environmetrics

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Environmental monitoring and assessment typically requires observational data as opposed to data obtained from controlled experiments. Obtaining such data requires identification of sample units to represent the population of concern, followed by selection of particular units to quantify the characteristic(s) of interest. Sample units are generally the smallest possible units of measurement, such as plots, soil cores, and individuals, while typical characteristics of interest include biomass, chemical concentrations, or “head counts.” Typically the most expensive part of this process is laboratory analysis, while identification of potential sample units is comparatively simple. One can therefore achieve great observational economy if one can identify a large number of sample units to represent the population of interest, yet only have to quantify a carefully selected subsample. This potential for observational economy was recognized for estimating mean pasture and forage yields in the early 1950s when McIntyre proposed a method, later coined ranked set sampling (RSS) by Halls and Dell, and currently under active investigation in various quarters.

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Ranked set sampling allocation models for multiple skewed variables: an application to agricultural data

Cited by 10 publications

References 7 publications

Estimation of Stress-Strength Reliability For Weibull Distribution Based on Type-II Right Censored Ranked Set Sampling Data

Estimation of Stress-Strength Reliability For Weibull Distribution Based on Type-II Right Censored Ranked Set Sampling Data

Bayesian Inference for the Parameters of Kumaraswamy Distribution via Ranked Set Sampling

Ranked set sampling

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