A simulation-based approach to the study of coefficient of variation of dividend yields

Pang, Wei; Yu, Bosco Wing-Tong; Troutt, Marvin D.; Hou, Shui Hung

doi:10.1016/j.ejor.2007.05.032

Cited by 22 publications

(12 citation statements)

References 15 publications

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“…A lower CV indicates a higher expected return with less risk [9]. The CV is also used to measure the stability of dividend yields of the Hang Seng index [10].…”

Section: Icmie 129-2mentioning

confidence: 99%

A Comparison of the Variable Sampling Interval (VSI) and Variable Sample Size (VSS) Coefficient of Variation Charts

Lim¹,

Yeong²,

Khoo³

et al. 2018

World Congress on Mechanical, Chemical, and Material Engineering

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An efficient way to perform quality monitoring is important for a lot of industries, particularly in the manufacturing industry. Control charts are one of the important tools to monitor quality. Recently, control charts monitoring the coefficient of variation (CV) are proposed to monitor processes which do not have a constant mean and a standard deviation which changes with the mean. These processes cannot be monitored by standard control charts which monitor the mean and/or standard deviation. The Variable Sampling Interval (VSI) and Variable Sample Size (VSS) CV charts are two recent charts proposed to improve the detection ability of CV charts. This paper compares the performance between these two charts in terms of the Average Time to Signal (ATS). The comparison shows that the VSS CV chart shows better performance when either a small sample size is taken or when a small shift in the CV needs to be detected. Alternatively, the VSI CV chart outperforms the VSS CV chart. This paper will enable practitioners to know the conditions where the VSS CV chart outperforms the VSI CV chart, and vice versa. Hence, practitioners will be able to decide whether it is better to monitor their processes with a VSI or VSS scheme.

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“…A lower CV indicates a higher expected return with less risk [9]. The CV is also used to measure the stability of dividend yields of the Hang Seng index [10].…”

Section: Icmie 129-2mentioning

confidence: 99%

A Comparison of the Variable Sampling Interval (VSI) and Variable Sample Size (VSS) Coefficient of Variation Charts

Lim¹,

Yeong²,

Khoo³

et al. 2018

World Congress on Mechanical, Chemical, and Material Engineering

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show abstract

“…The CV can be used to compare distributions obtained with different units, such as, for example, the variability of the weights of newborns (measured in grams) with the size of adults (measured in centimeters). This approach has been used by several authors to obtain the CV estimates (for details, see Pang et al [24] and Pang et al [25]). …”

Section: Introductionmentioning

confidence: 99%

A simulation-based approach to the study of coefficient of variation of Gompertz distribution under progressive first-failure censoring

Soliman

Abdellah

Abou-Elheggag

et al. 2011

Indian J Pure Appl Math

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In applied statistics, the coefficient of variation is widely used. However, inference concerning the coefficient of variation of non-normal distributions are rarely reported. In this article, a simulation-based Bayesian approach is adopted to estimate the coefficient of variation (CV ) under progressive firstfailure censored data from Gompertz distribution. The sampling schemes such as, first-failure censoring, progressive type II censoring, type II censoring and complete sample can be obtained as special cases of the progressive first-failure censored scheme. The simulation-based approach will give us a point estimate as well as the empirical sampling distribution of CV . The joint prior density as a product of conditional gamma density and inverted gamma density for the unknown Gompertz parameters are considered. In addition, the results of maximum likelihood and parametric bootstrap techniques are also proposed. An analysis of a real life data set is presented for illustrative purposes. Results from simulation studies assessing the performance of our proposed method are included.

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“…Generally, the ratio of two integrals given by (26) can not be obtained in a closed form. In this case, we use the MCMC method to generate samples from the posterior distributions and then compute the Bayes estimator of g(α, β) under the squared errors loss (SEL) function.…”

Section: Bayes Estimator Of CV Using Mcmcmentioning

confidence: 99%

“…In this case, we use the MCMC method to generate samples from the posterior distributions and then compute the Bayes estimator of g(α, β) under the squared errors loss (SEL) function. Therefore, we propose the approaches of MCMC technique to approximate (26). See, for example, Robert and Casella [31] and Recently, Rezaei et al [30].…”

Section: Bayes Estimator Of CV Using Mcmcmentioning

confidence: 99%

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Markov Chain Monte Carlo to Study the Estimation of the Coefficient of Variation

Mahmoud¹,

Soliman²,

Abdellah³

et al. 2013

IJCA

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The coefficient of variation (CV ) of a population is defined as the ratio of the population standard deviation to the population mean. It is regarded as a measure of stability or uncertainty, and can indicate the relative dispersion of data in the population to the population mean. In this article, based on the upper record values, we study the behavior of the CV of a random variable that follows a Lomax distribution. Specifically, we compute the maximum likelihood estimations (MLEs) and the confidence intervals of CV based on the observed Fisher information matrix using asymptotic distribution of the maximum likelihood estimator and also by using the bootstrapping technique. In addition, we propose to apply Markov Chain Monte Carlo (MCMC) techniques to tackle this problem, which allows us to construct the credible intervals. A numerical example based on a real data is presented to illustrate the implementation of the proposed procedure. Finally, Monte Carlo simulations are performed to observe the behavior of the proposed methods.

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A simulation-based approach to the study of coefficient of variation of dividend yields

Cited by 22 publications

References 15 publications

A Comparison of the Variable Sampling Interval (VSI) and Variable Sample Size (VSS) Coefficient of Variation Charts

A Comparison of the Variable Sampling Interval (VSI) and Variable Sample Size (VSS) Coefficient of Variation Charts

A simulation-based approach to the study of coefficient of variation of Gompertz distribution under progressive first-failure censoring

Markov Chain Monte Carlo to Study the Estimation of the Coefficient of Variation

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