A simple approximation for semivariance

Choobineh, F.; Branting, D.

doi:10.1016/0377-2217(86)90332-2

Cited by 65 publications

(36 citation statements)

References 8 publications

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“…Yourstone and Zimmer [6] approximated the underlying distribution using the generalized Burr distribution with the estimated skewness and kurtosis of the sample average for the determination of control limits. Choobineh and Ballard [7] proposed a weighted variance (WV) method based on the semivariance approximation of Choobineh and Branting [8]. Bai and Choi [9] considered a WV method which modifies the method of Choobineh and Ballard [7].…”

Section: Introductionmentioning

confidence: 99%

Control charts for positively‐skewed populations with weighted standard deviations

Chang

Bai

2001

Quality & Reliability Eng

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SUMMARYThis paper proposes a heuristic method of constructingX, cumulative sum and exponentially weighted moving average control charts for skewed populations with weighted standard deviations obtained by decomposing the standard deviation into upper and lower deviations adjusted in accordance with the direction and degree of skewness. These control charts, however, reduce to standard control charts when the underlying distribution is symmetric. Simple formulae are derived to estimate unknown process parameters from means and ranges of subgroups. The false alarm rates of these control charts are compared with those of existing control charts when the underlying distribution is Weibull, gamma, or lognormal. Simulation results show that considerable improvement over the existing methods can be achieved when the underlying distribution is skewed.

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Section: Introductionmentioning

confidence: 99%

Control charts for positively‐skewed populations with weighted standard deviations

Chang

Bai

2001

Quality & Reliability Eng

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“…Because it is direct, clear and comparatively simple in reflecting investors' intuition about risk, it receives much attention. Many scholars such as Mao [36], Choobineh and Branting [7], Markowitz [39], Kaplan and Alldredge [26], Grootveld and Hallerbach [16], and Huang [24] researched the properties and computation problem of semivariance. Their researches show that semivariance has merits in measuring risk.…”

Section: Introductionmentioning

confidence: 99%

Mean-semivariance models for fuzzy portfolio selection

Huang

2008

Journal of Computational and Applied Mathematics

214

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This paper discusses portfolio selection problem in fuzzy environment. In the paper, semivariance is originally presented for fuzzy variable, and three properties of the semivariance are proven. Based on the concept of semivariance of fuzzy variable, two fuzzy mean-semivariance models are proposed. To solve the new models in general cases, a fuzzy simulation based genetic algorithm is presented in the paper. In addition, two numerical examples are also presented to illustrate the modelling idea and the effectiveness of the designed algorithm.

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“…A number of methods are available. The weighted variance (WV) method was developed by Choobineh and Ballard (1987) based on semivariance approximation (Choobineh & Branting, 1986). Heuristic variants of the WV method that requires no assumptions concerning the functional form of the distribution were later proposed by, e.g., Bai and Choi (1995), Castagliola (2000), and Castagliola and Tsung (2005).…”

Section: Introductionmentioning

confidence: 99%