Modified balanced circular systematic sampling

Leu, Ching-Ho; Kao, Fei-Fei

doi:10.1016/j.spl.2005.08.005

Cited by 7 publications

(3 citation statements)

References 6 publications

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“…From a predetermined splitting ratio, the algorithm selects the pace between ks (the selected samples) and distributes the data into training/validation/test subsets (Fleck et al, 2012;Schultz, 2015). The work of Leu and Kao (2006) presents a review on many modifications of the systematic methodology, such as multi-start sampling (Gautschi, 1957), Markov systematic sampling (Sampath and Uthayakumaran, 1999) and circular systematic sampling. All the modification try to solve problems in the split, especially in populations where the simple y-rank cannot capture linear or parabolic trends efficiently.…”

Section: Backgrounds: Y-rank and K-meansmentioning

confidence: 99%

K-Rank: An Evolution of Y-Rank for Multiple Solutions Problem

Santos

Ranzan

Farenzena

et al. 2019

Braz. J. Chem. Eng.

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Y-rank can present faults when dealing with non-linear problems. A methodology is proposed to improve the selection of data in situations where y-rank is fragile. The proposed alternative, called k-rank, consists of splitting the data set into clusters using the k-means algorithm, and then apply y-rank to the generated clusters. Models were calibrated and tested with subsets split by y-rank and k-rank. For the Heating Tank case study, in 59% of the simulations, models calibrated with k-rank subsets achieved better results. For the Propylene / Propane Separation Unit case, when dealing with a small number of sample points, the y-rank models had errors almost three times higher than the k-rank models for the test subset, meaning that the fitted model could not deal properly with new unseen data. The proposed methodology was successful in splitting the data, especially in cases with a limited amount of samples.

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Section: Backgrounds: Y-rank and K-meansmentioning

confidence: 99%

K-Rank: An Evolution of Y-Rank for Multiple Solutions Problem

Santos

Ranzan

Farenzena

et al. 2019

Braz. J. Chem. Eng.

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“…Linear systematic sampling in [73], circular systematic sampling in [74] are also considered in the literature. In [75], a modified balanced circular systematic sampling when N = nk is suggested. Another modification of systematic sampling, called FCFS-SS (First Come First Serve following Systematic Sampling) [76], is proposed.…”

Section: B) Systematic Samplingmentioning

confidence: 99%

Data Summarization Techniques for Big Data—A Survey

Hesabi

Tari

Gościński

et al. 2015

Handbook on Data Centers

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“…[26] proposed the additional circular systematic sampling when the populations reveal linear and parabolic trends. Later, [11] have proposed estimators under balanced circular systematic sampling and centered circular systematic sampling when population trend is linear or parabolic. Circular systematic sampling can be used in both cases, whether k is an integer or not.…”

Section: Introductionmentioning

confidence: 99%

Modified classes of estimators in circular systematic sampling

Riaz¹,

Diana²,

Shabbir³

2015

HJMS

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In this paper we consider the problem of estimation of population mean in circular systematic sampling design along with the non-response problem. For the population mean using auxiliary information three generalized classes of estimators are suggested. The biases and the mean square errors of the suggested classes of estimators are obtained and compared with sample mean, linear regression estimators, [23] estimator and [21] estimators. A numerical study is provided to show that the proposed classes of estimators based on circular systematic design can be more efficient than the estimators based on simple random sampling. Moreover, a simulation study is accomplished when some population parameters are assumed to be unknown.

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Modified balanced circular systematic sampling

Cited by 7 publications

References 6 publications

K-Rank: An Evolution of Y-Rank for Multiple Solutions Problem

K-Rank: An Evolution of Y-Rank for Multiple Solutions Problem

Data Summarization Techniques for Big Data—A Survey

Modified classes of estimators in circular systematic sampling

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