Journal articles: 'Estimation of coefficient of variation'

1

Ahmed, S. E. "Improved estimation of the coefficient of variation." Journal of Applied Statistics 21, no. 6 (January 1994): 565–73. http://dx.doi.org/10.1080/757584217.

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Ditlevsen, Susanne, and Petr Lansky. "Firing Variability Is Higher than Deduced from the Empirical Coefficient of Variation." Neural Computation 23, no. 8 (August 2011): 1944–66. http://dx.doi.org/10.1162/neco_a_00157.

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A convenient and often used summary measure to quantify the firing variability in neurons is the coefficient of variation (CV), defined as the standard deviation divided by the mean. It is therefore important to find an estimator that gives reliable results from experimental data, that is, the estimator should be unbiased and have low estimation variance. When the CV is evaluated in the standard way (empirical standard deviation of interspike intervals divided by their average), then the estimator is biased, underestimating the true CV, especially if the distribution of the interspike intervals is positively skewed. Moreover, the estimator has a large variance for commonly used distributions. The aim of this letter is to quantify the bias and propose alternative estimation methods. If the distribution is assumed known or can be determined from data, parametric estimators are proposed, which not only remove the bias but also decrease the estimation errors. If no distribution is assumed and the data are very positively skewed, we propose to correct the standard estimator. When defining the corrected estimator, we simply use that it is more stable to work on the log scale for positively skewed distributions. The estimators are evaluated through simulations and applied to experimental data from olfactory receptor neurons in rats.

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Guo, Huizhen, and Nabendu Pal. "On a Normal Mean with Known Coefficient of Variation." Calcutta Statistical Association Bulletin 54, no. 1-2 (March 2003): 17–30. http://dx.doi.org/10.1177/0008068320030102.

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This paper deals with estimation of θ when iid (independent and identically distributed) observations are available from a N( θ, cθ2) distribution where c > 0 is assumed to be known. Using the equivariance principle under the group of scale and direction transformations we first characterize the class of equivariant estimators of θ. We then investigate a few equivariant estimators, including the maximum likelihood estimator, in terms of standardized bias and standardized mean squared error.

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Sangngam, Prayad. "Ratio Estimators Using Coefficient of Variation and Coefficient of Correlation." Modern Applied Science 8, no. 5 (August 5, 2014): 70. http://dx.doi.org/10.5539/mas.v8n5p70.

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This paper introduces ratio estimators of the population mean using the coefficient of variation of study variable and auxiliary variables together with the coefficient of correlation between the study and auxiliary variables under simple random sampling and stratified random sampling. These ratio estimators are almost unbiased. The mean square errors of the estimators and their estimators are given. Sample size estimation in both sampling designs are presented. An optimal sample size allocation in stratified random sampling is also suggested. Based on theoretical study, it can be shown that these ratio estimators have smaller MSE than the unbiased estimators. Moreover, the empirical study indicates that these ratio estimators have smallest MSE compared to the existing ones.

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Şen, Zekai. "Instantaneous Runoff Coefficient Variation and Peak Discharge Estimation Model." Journal of Hydrologic Engineering 13, no. 4 (April 2008): 270–77. http://dx.doi.org/10.1061/(asce)1084-0699(2008)13:4(270).

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Viglione, A. "Confidence intervals for the coefficient of L-variation in hydrological applications." Hydrology and Earth System Sciences 14, no. 11 (November 11, 2010): 2229–42. http://dx.doi.org/10.5194/hess-14-2229-2010.

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Abstract. The coefficient of L-variation (L-CV) is commonly used in statistical hydrology, in particular in regional frequency analysis, as a measure of steepness for the frequency curve of the hydrological variable of interest. As opposed to the point estimation of the L-CV, in this work we are interested in the estimation of the interval of values (confidence interval) in which the L-CV is included at a given level of probability (confidence level). Several candidate distributions are compared in terms of their suitability to provide valid estimators of confidence intervals for the population L-CV. Monte-Carlo simulations of synthetic samples from distributions frequently used in hydrology are used as a basis for the comparison. The best estimator proves to be provided by the log-Student t distribution whose parameters are estimated without any assumption on the underlying parent distribution of the hydrological variable of interest. This estimator is shown to also outperform the non parametric bias-corrected and accelerated bootstrap method. An illustrative example of how this result can be used in hydrology is presented, namely in the comparison of methods for regional flood frequency analysis. In particular, it is shown that the confidence intervals for the L-CV can be used to assess the amount of spatial heterogeneity of flood data not explained by regionalization models.

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Thangjai, Warisa, Sa-Aat Niwitpong, and Suparat Niwitpong. "A Bayesian Approach for Estimation of Coefficients of Variation of Normal Distributions." Sains Malaysiana 50, no. 1 (January 31, 2021): 261–78. http://dx.doi.org/10.17576/jsm-2021-5001-25.

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The coefficient of variation is widely used as a measure of data precision. Confidence intervals for a single coefficient of variation when the data follow a normal distribution that is symmetrical and the difference between the coefficients of variation of two normal populations are considered in this paper. First, the confidence intervals for the coefficient of variation of a normal distribution are obtained with adjusted generalized confidence interval (adjusted GCI), computational, Bayesian, and two adjusted Bayesian approaches. These approaches are compared with existing ones comprising two approximately unbiased estimators, the method of variance estimates recovery (MOVER) and generalized confidence interval (GCI). Second, the confidence intervals for the difference between the coefficients of variation of two normal distributions are proposed using the same approaches, the performances of which are then compared with the existing approaches. The highest posterior density interval was used to estimate the Bayesian confidence interval. Monte Carlo simulation was used to assess the performance of the confidence intervals. The results of the simulation studies demonstrate that the Bayesian and two adjusted Bayesian approaches were more accurate and better than the others in terms of coverage probabilities and average lengths in both scenarios. Finally, the performances of all of the approaches for both scenarios are illustrated via an empirical study with two real-data examples.

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Badr, M., M. El-Shirbeny, M. El-Ansary, and M. Awad. "ESTIMATION OF CROP COEFFICIENT VARIATION THROUGH SATELLITE VNIR SPECTRAL DATA." Misr Journal of Agricultural Engineering 34, no. 2 (April 1, 2017): 829–42. http://dx.doi.org/10.21608/mjae.2017.96741.

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Rizvi, S. A. H., and R. Karan Singh. "On estimation of population mean using known coefficient of variation." Microelectronics Reliability 37, no. 5 (May 1997): 841–43. http://dx.doi.org/10.1016/s0026-2714(96)00113-8.

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Kanefuji, Koji, and Kosei Iwase. "Estimation for a scale parameter with known coefficient of variation." Statistical Papers 39, no. 4 (October 1998): 377–88. http://dx.doi.org/10.1007/bf02927100.

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Et. al., Mahdi Wahhab Neamah ,. "Statistical Properties & Different Methods Of Estimation Of A New Extended Weighted Frechet Distribution." Turkish Journal of Computer and Mathematics Education (TURCOMAT) 12, no. 6 (April 11, 2021): 1011–29. http://dx.doi.org/10.17762/turcomat.v12i6.2414.

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In this paper, we introduce a new distribution is called the extended weighted Frechet distribution, which we obtain by applying the Azzalini method and deduced some statistical properties such as mean, variance, coefficients of variation, coefficient of skewness, and coefficient of kurtosis. The parameters of the new distribution were estimated by the following estimation methods: Maximum Likelihood Method (MLE) and percentile method. We used the Monte Carlo simulation to compare the performances of the proposed estimators obtained through methods of estimation.

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Yousef, Ali, and Hosny Hamdy. "Three-Stage Sequential Estimation of the Inverse Coefficient of Variation of the Normal Distribution." Computation 7, no. 4 (December 4, 2019): 69. http://dx.doi.org/10.3390/computation7040069.

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This paper sequentially estimates the inverse coefficient of variation of the normal distribution using Hall’s three-stage procedure. We find theorems that facilitate finding a confidence interval for the inverse coefficient of variation that has pre-determined width and coverage probability. We also discuss the sensitivity of the constructed confidence interval to detect a possible shift in the inverse coefficient of variation. Finally, we find the asymptotic regret encountered in point estimation of the inverse coefficient of variation under the squared-error loss function with linear sampling cost. The asymptotic regret provides negative values, which indicate that the three-stage sampling does better than the optimal fixed sample size had the population inverse coefficient of variation been known.

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Wang, Xudong, and Jitao Yao. "Linear Regression Estimation Methods for Inferring Standard Values of Snow Load in Small Sample Situations." Mathematical Problems in Engineering 2020 (July 27, 2020): 1–10. http://dx.doi.org/10.1155/2020/3753417.

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The aim of this paper is to establish a new method for inferring standard values of snow load in small sample situations. Due to the incomplete meteorological data in some areas, it is often necessary to infer the standard values of snow load in the conditions of small samples in engineering, but the point estimation methods of classical statistics adopted till now do not take into account the influences of statistical uncertainty, and the inference results are always aggressive. In order to overcome the above shortcomings, according to the basic principle of optimal linear unbiased estimation and invariant estimation of the minimum type I distribution parameters and the tantile, using the least square method, the linear regression estimation methods for inferring standard values of snow load in small sample situations are proposed, which can take into account two cases such as parameter-free and known coefficient of variation, and the predicted formulas of snow load standard values are given, respectively. Through numerical integration and Monte Carlo numerical simulation, the numerical table of correlation coefficients is established, which is more convenient for the direct application of inferential formulas. According to the results of theoretical analysis and examples, when using the indirect point estimation methods to infer the standard values of snow load in the conditions of small samples, the inference results are always small. The linear regression estimation method is suitable for inferring standard values of snow load in the conditions of small samples, which can give more reasonable results. When using the linear regression estimation to infer standard values of snow load in practical application, even if the coefficient of variation is unknown, it can set the upper limit value of the coefficient of variation according to the experience; meanwhile, according to the parameter-free and known coefficient of variation, the estimation is carried out, respectively, and the smaller value of the two is taken as the final estimate. The method can be extended to the statistical inference of variable load standard values such as wind load and floor load.

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Ishola, Taiwo Abass, Olatayo Timothy Olabisi, and Adesanya Kazeem Kehinde. "Parameter Estimation of Fractional Trigonometric Polynomial Regression Model." JOURNAL OF UNIVERSITY OF BABYLON for pure and applied sciences 27, no. 1 (May 13, 2019): 519–26. http://dx.doi.org/10.29196/jubpas.v27i1.2208.

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Trigonometric Polynomial Regression is a form of non-linear regression in which the relationship between the outcome variable and risk variable is Fractional modeled as 1/nth degree polynomial regression by combining the function of cos(nx) and sin(nx) on the value of natural numbers. The model was used to analyze the relationship between three continuous and periodic variables. Coefficients of the model were estimated using the Maximum Likelihood Estimate (MLE) method. From the results, the model obtained indicated that an increased in body mass index will increase the level of blood pressure while age may or may not have an influence on the blood pressure level. The values of the Coefficient of variation showed the variation in the dependent variable was well explained by the independent variables and the value of the adjusted coefficient of determination showed the model had a good fit with a high level of predictive power.

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Liang, TaChen. "Empirical Bayes estimation of coefficient of variation in shifted exponential distributions." Journal of Nonparametric Statistics 21, no. 3 (April 2009): 365–78. http://dx.doi.org/10.1080/10485250802587992.

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Gupta, Ramesh C., and Olcay Akman. "Estimation of coefficient of variation in a weighted inverse Gaussian model." Applied Stochastic Models and Data Analysis 12, no. 4 (December 1996): 255–63. http://dx.doi.org/10.1002/(sici)1099-0747(199612)12:4<255::aid-asm287>3.0.co;2-r.

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Renò, Roberto. "NONPARAMETRIC ESTIMATION OF THE DIFFUSION COEFFICIENT OF STOCHASTIC VOLATILITY MODELS." Econometric Theory 24, no. 5 (May 14, 2008): 1174–206. http://dx.doi.org/10.1017/s026646660808047x.

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In this paper, new fully nonparametric estimators of the diffusion coefficient of continuous time models are introduced. The estimators are based on Fourier analysis of the state variable trajectory observed and on the estimation of quadratic variation between observations by means of realized volatility. The estimators proposed are shown to be consistent and asymptotically normally distributed. Moreover, the Fourier estimator can be iterated to get a fully nonparametric estimate of the diffusion coefficient in a bivariate model in which one state variable is the volatility of the other. The estimators are shown to be unbiased in small samples using Monte Carlo simulations and are used to estimate univariate and bivariate models for interest rates.

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18

Ahmed, S. E. "Simultaneous estimation of coefficients of variation." Journal of Statistical Planning and Inference 104, no. 1 (May 2002): 31–51. http://dx.doi.org/10.1016/s0378-3758(01)00121-5.

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19

Golay, Philippe, Delphine Fagot, and Thierry Lecerf. "Against coefficient of variation for estimation of intraindividual variability with accuracy measures." Tutorials in Quantitative Methods for Psychology 9, no. 1 (February 1, 2013): 6–14. http://dx.doi.org/10.20982/tqmp.09.1.p006.

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Rajyaguru, Arti, and P. C. Gupta. "On the estimation of the coefficient of variation from finite population-II." Model Assisted Statistics and Applications 1, no. 1 (December 15, 2005): 57–66. http://dx.doi.org/10.3233/mas-2006-1110.

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Misra, Sheela, Dharmendra Kumar Yadav, Dip ika, and Ashish Kr.Shukla. "On Estimation of Population Coefficient of Variation in Presence of Measurement Errors." International Journal of Mathematics Trends and Technology 51, no. 4 (November 25, 2017): 307–11. http://dx.doi.org/10.14445/22315373/ijmtt-v51p540.

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Abella, Jaume, Maria Padilla, Joan Del Castillo, and Francisco J. Cazorla. "Measurement-Based Worst-Case Execution Time Estimation Using the Coefficient of Variation." ACM Transactions on Design Automation of Electronic Systems 22, no. 4 (July 22, 2017): 1–29. http://dx.doi.org/10.1145/3065924.

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Connett, John E., and Wondra Wong Lee. "Estimation of the coefficient of variation from laboratory analysis of split specimens." Controlled Clinical Trials 10, no. 3 (September 1989): 348. http://dx.doi.org/10.1016/0197-2456(89)90170-0.

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Novák, Lukáš, and Drahomír Novák. "Estimation of coefficient of variation for structural analysis: The correlation interval approach." Structural Safety 92 (September 2021): 102101. http://dx.doi.org/10.1016/j.strusafe.2021.102101.

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Bretz, F., V. Guiard, L. A. Hothorn, and G. Dilba. "Simultaneous Confidence Intervals for Ratios with Applications to the Comparison of Several Treatments with a Control." Methods of Information in Medicine 43, no. 05 (2004): 465–69. http://dx.doi.org/10.1055/s-0038-1633899.

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Summary Objectives: In this article, we illustrate and compare exact simultaneous confidence sets with various approximate simultaneous confidence intervals for multiple ratios as applied to many-to-one comparisons. Quite different datasets are analyzed to clarify the points. Methods: The methods are based on existing probability inequalities (e.g., Bonferroni, Slepian and Šidàk), estimation of nuisance parameters and re-sampling techniques. Exact simultaneous confidence sets based on the multivariate t-distribution are constructed and compared with approximate simultaneous confidence intervals. Results: It is found that the coverage probabilities associated with the various methods of constructing simultaneous confidence intervals (for ratios) in many-to-one comparisons depend on the ratios of the coefficient of variation for the mean of the control group to the coefficient of variation for the mean of the treatments. If the ratios of the coefficients of variations are less than one, the Bonferroni corrected Fieller confidence intervals have almost the same coverage probability as the exact simultaneous confidence sets. Otherwise, the use of Bonferroni intervals leads to conservative results. Conclusions: When the ratio of the coefficient of variation for the mean of the control group to the coefficient of variation for the mean of the treatments are greater than one (e.g., in balanced designs with increasing effects), the Bonferroni simultaneous confidence intervals are too conservative. Therefore, we recommend not using Bonferroni for this kind of data. On the other hand, the plug-in method maintains the intended confidence coefficient quite satisfactorily; therefore, it can serve as the best alternative in any case.

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Ai, Zhipin, and Yonghui Yang. "Modification and Validation of Priestley–Taylor Model for Estimating Cotton Evapotranspiration under Plastic Mulch Condition." Journal of Hydrometeorology 17, no. 4 (April 1, 2016): 1281–93. http://dx.doi.org/10.1175/jhm-d-15-0151.1.

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Abstract Compared with more comprehensive physical algorithms such as the Penman–Monteith model, the Priestley–Taylor model is widely used in estimating evapotranspiration for its robust ability to capture evapotranspiration and simplicity of use. The key point in successfully using the Priestley–Taylor model is to find a proper Priestley–Taylor coefficient, which is variable under different environmental conditions. Based on evapotranspiration partition and plant physiological limitation, this study developed a new model for estimating the Priestley–Taylor coefficient incorporating the effects of three easily obtainable parameters such as leaf area index (LAI), air temperature, and mulch fraction. Meanwhile, the effects of plastic film on the estimation of net radiation and soil heat flux were fully considered. The reliability of the modified Priestley–Taylor model was testified using observed cotton evapotranspiration from eddy covariance in two growing seasons, with high coefficients of determination of 0.86 and 0.81 in 2013 and 2014, respectively. Then, the modified model was further validated by estimating cotton evapotranspiration under three fractions of mulch cover: 0%, 60%, and 100%. The estimated values agreed well with the measured values via water balance analysis. It can be found that seasonal variation of the modified Priestley–Taylor coefficient showed a more reasonable pattern compared with the original coefficient of 1.26. Sensitivity analysis showed that the modified Priestley–Taylor coefficient was more sensitive to LAI than to air temperature. Overall, the modified model has much higher accuracy and could be used for evapotranspiration estimation under plastic mulch condition.

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Thangjai, Warisa, Sa-Aat Niwitpong, and Suparat Niwitpong. "Confidence intervals for the common coefficient of variation of rainfall in Thailand." PeerJ 8 (September 21, 2020): e10004. http://dx.doi.org/10.7717/peerj.10004.

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The log-normal distribution is often used to analyze environmental data like daily rainfall amounts. The rainfall is of interest in Thailand because high variable climates can lead to periodic water stress and scarcity. The mean, standard deviation or coefficient of variation of the rainfall in the area is usually estimated. The climate moisture index is the ratio of plant water demand to precipitation. The climate moisture index should use the coefficient of variation instead of the standard deviation for comparison between areas with widely different means. The larger coefficient of variation indicates greater dispersion, whereas the lower coefficient of variation indicates the lower risk. The common coefficient of variation, is the weighted coefficients of variation based on k areas, presents the average daily rainfall. Therefore, the common coefficient of variation is used to describe overall water problems of k areas. In this paper, we propose four novel approaches for the confidence interval estimation of the common coefficient of variation of log-normal distributions based on the fiducial generalized confidence interval (FGCI), method of variance estimates recovery (MOVER), computational, and Bayesian approaches. A Monte Carlo simulation was used to evaluate the coverage probabilities and average lengths of the confidence intervals. In terms of coverage probability, the results show that the FGCI approach provided the best confidence interval estimates for most cases except for when the sample case was equal to six populations (k = 6) and the sample sizes were small (nI < 50), for which the MOVER confidence interval estimates were the best. The efficacies of the proposed approaches are illustrated with example using real-life daily rainfall datasets from regions of Thailand.

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Doh, Jaehyeok, and Jongsoo Lee. "Bayesian estimation of the lethargy coefficient for probabilistic fatigue life model." Journal of Computational Design and Engineering 5, no. 2 (October 21, 2017): 191–97. http://dx.doi.org/10.1016/j.jcde.2017.10.002.

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Abstract In this study, a model for probabilistic fatigue life that is based on the Zhurkov model is suggested using stochastically and statistically estimated lethargy coefficients. The fatigue life model was derived using the Zhurkov life model, and it was deterministically validated using real fatigue life data as a reference. For this process, firstly, a lethargy coefficient that is related to the failure of materials must be obtained with rupture time and stress from a quasi-static tensile test. These experiments are performed using HS40R steel. However, the lethargy coefficient has discrepancies due to the inherent uncertainty and the variation of material properties in the experiments. The Bayesian approach was employed for estimating the lethargy coefficient of the fatigue life model using the Markov Chain Monte Carlo (MCMC) sampling method and considering its uncertainties. Once the samples are obtained, one can proceed to the posterior predictive inference of the fatigue life. This life model was shown to be reasonable when compared with experimental fatigue life data. As a result, predicted fatigue life was observed to significantly decrease in accordance with increasing relative stress conditions. Highlights Zhurkov fatigue life model is deterministically validated with experiments. Prediction of the S-N curve using Zhurkov fatigue model and lethargy coefficients. Lethargy coefficients of Zhurkov fatigue model are estimated by Bayesian updating. Bayesian updating is useful for quantifying the uncertainty of unknown parameters.

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Yousef and Hamdy. "Three-Stage Estimation of the Mean and Variance of the Normal Distribution with Application to an Inverse Coefficient of Variation with Computer Simulation." Mathematics 7, no. 9 (September 8, 2019): 831. http://dx.doi.org/10.3390/math7090831.

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This paper considers sequentially two main problems. First, we estimate both the mean and the variance of the normal distribution under a unified one decision framework using Hall’s three-stage procedure. We consider a minimum risk point estimation problem for the variance considering a squared-error loss function with linear sampling cost. Then we construct a confidence interval for the mean with a preassigned width and coverage probability. Second, as an application, we develop Fortran codes that tackle both the point estimation and confidence interval problems for the inverse coefficient of variation using a Monte Carlo simulation. The simulation results show negative regret in the estimation of the inverse coefficient of variation, which indicates that the three-stage procedure provides better estimation than the optimal.

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Mahul, Antoine, and Alex Aussem. "Distributed Neural Networks for Quality of Service Estimation in Communication Networks." International Journal of Computational Intelligence and Applications 03, no. 03 (September 2003): 297–308. http://dx.doi.org/10.1142/s1469026803000999.

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We study an original scheme based on distributed feedforward neural networks, aimed at modelling several queueing systems in cascade fed with bursty traffic. For each queueing system, a neural network is trained to anticipate the average number of waiting packets, the packet loss rate and the coefficient of variation of the packet inter-departure time, given the mean rate, the peak rate and the coefficient of variation of the packet inter-arrival time. The latter serves for the calculation of the coefficient of variation of the cell inter-arrival time of the aggregated traffic which is fed as input to the next neural network along the path. The potential of this method is successfully illustrated on several single server FIFO (First In, First Out) queues and on small queueing networks made up from a combination of queues in tandem and in parallel fed by a superposition of ideal sources. Our long-term goal is the design of preventive control strategy in a multiservice communication network.

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Fedukhin, A. V., and N. V. Cespedes Garcia. "On the matter of evaluation of the variation coefficient of the time to failure based on low-level quantiles." Dependability 18, no. 4 (December 5, 2018): 10–15. http://dx.doi.org/10.21683/1729-2646-2018-18-4-10-15.

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In the context of various tasks related to dependability estimation of systems by probabilistic physical methods the most important a priori information that ensures effective solutions is the information on the variation coefficient of the time to failure. Given the low failure statistics, the estimation of the variation coefficient of the time to failure is complicated due to significant sample censoring. In these cases, methods of variation coefficient evaluation with additional a priori information and the method of quantiles are used. The solution of a number of dependability-related tasks that require taking into consideration various failure distributions is significantly simplified if the functions of such distributions are tabulated in the relative operation time and variation coefficient parameters. An effective solution of dependability-related tasks with the use of tables of DN distribution function was first proposed for the parametrization of distribution in parameters x and v, where x is the scale parameter, relative operation time x = at; v is the shape parameter, variation coefficient v = V; a is the average degradation rate. That allowed performing tabulation out of real time, simplifying function tabulation and its use in a number of dependability-related tasks by method of quantiles. The paper analyzed the effectiveness of the method of quantiles in the estimation of the variation coefficient of the time to failure, that is at the same time the shape parameter of the DN distribution, under scarce failure statistics and based on it proposes a new, more effective, method. The method of estimation of the variation coefficient using low and ultralow-level quantiles is based on the behaviour analysis of function ai = f(t) obtained using the method of quantiles. It is considered that the best choice of the a priori value of v is a choice under which the dependence graph ai = f(t) is most accurately described by a straight horizontal line, which is in complete compliance with the hypothesis of constant degradation rate accepted in the context of DN distribution formalization. In cases when the dependence graph ai = f(t) does not easily allow concluding on the best choice of the a priori value v (it is especially difficult to make a choice based on the statistics of first failures), the following formal criterion can be used: the most acceptable a priori value of the shape parameter v lies within the range of values, where the sign of the trend of the average degradation rate (h) in graph ai = f(t) changes. Studies have established that the most significant errors in the estimation of the variation coefficient are associated with first failures. When processing the results of dependability tests it is assumed the first failures in a sample have the lowest information weight, as their occurrence is due to serious defects not detected by final quality inspection of products. The first failures normally “fall out” of the overall statistical pattern, and it is recommended to omit them from further analysis. The proposed method of estimation of the variation coefficient of the time to failure based on ultralow-level quantiles enables – in the context of limited failure statistics, when other methods are inefficient – for sufficiently accurate identification of not only the variation coefficient of the time to failure and DN distribution parameters, but also make conclusions regarding the feasibility and legitimacy of equalization (description) of the considered sample using this diffusion distribution, i.e. it can be used as a kind of criterion of compliance of the empirical failure distribution under consideration with the chosen theoretical dependability model. The described process of finding the truest values of the variation coefficient of the time to failure using the formal criterion can be computerized.

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Tomović, Slavko. "Estimation of the two-parameter Weibull distribution parameters using the coefficient of variation." Vojnotehnicki glasnik 44, no. 6 (1996): 577–82. http://dx.doi.org/10.5937/vojtehg9605577t.

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A.W.Mahmoud, M., A. A. Soliman, A. H. Abd Ellah, and R. M. El-Sagheer. "Markov Chain Monte Carlo to Study the Estimation of the Coefficient of Variation." International Journal of Computer Applications 77, no. 4 (September 18, 2013): 31–37. http://dx.doi.org/10.5120/13384-1000.

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Aja-Fernandez, S., and C. Alberola-Lopez. "On the estimation of the coefficient of variation for anisotropic diffusion speckle filtering." IEEE Transactions on Image Processing 15, no. 9 (September 2006): 2694–701. http://dx.doi.org/10.1109/tip.2006.877360.

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Wearden, J. H. "Mission: Impossible? Modelling the Verbal Estimation of Duration." Timing & Time Perception 3, no. 3-4 (December 10, 2015): 223–45. http://dx.doi.org/10.1163/22134468-03002051.

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Three participants produced a large number of verbal estimates of tone durations in the range of 77–1183 ms. Data from this task were simulated by an ‘attractor model’, which used the idea of competition between ‘attractors’ (‘quantized’ values output as verbal estimates) which differed in weight, and distance from the stimulus duration to be estimated. To produce an estimate, all attractors competed for priority as output values, with the final value being decided probabilistically. The model embodied underlying scalar representations of time, in the form of mean accuracy and constant coefficient of variation. The model was able to reconcile such scalar properties of time with deviations from scalar properties often found in verbal estimation data, such as declining coefficients of variation with increasing duration value. The model furthermore showed that multiplicative and additive changes in underlying time representations should be translated veridically into behaviour, although the attractor competition process could distort patterns and absolute values of underlying variance.

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Agarwal, Rajiv. "Rapid microplate method for PAH estimation." American Journal of Physiology-Renal Physiology 283, no. 2 (August 1, 2002): F236—F241. http://dx.doi.org/10.1152/ajprenal.00336.2001.

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Evaluation of renal hemodynamics requires estimation of effective renal plasma flow, which is commonly measured by the renal clearance of p-aminohippuric acid (PAH). There are many existing methods for PAH assay that are complicated, expensive, or time consuming. We describe a rapid, precise, and accurate microplate-based assay of PAH using p-dimethylaminocinnamaldehyde, which produces a red color on reaction with PAH, and compare it with a reference HPLC method. Renal PAH clearances were measured in 10 volunteers, and clearances were calculated by using the new and HPLC methods. There was excellent agreement between the HPLC and the microplate method of PAH assay. The average ratio of microplate to HPLC method was nearly 1.0, and the limits of agreement (2 SD) for plasma, urine, and clearances were 17.2, 19.3, and 25.5%, respectively. Intraday coefficient of variation for urine and plasma was <7%; interday coefficient of variation was <6% for urine and plasma samples. The microplate method is a reliable alternative to a reference HPLC method and can be performed for a fraction of the cost, time, and reagents.

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Corscadden, Kenneth W., Allan Thomson, Behrang Yoonesi, and Josiah McNutt. "The Impact of Variable Wind Shear Coefficients on Risk Reduction of Wind Energy Projects." International Scholarly Research Notices 2016 (October 30, 2016): 1–12. http://dx.doi.org/10.1155/2016/5790464.

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Estimation of wind speed at proposed hub heights is typically achieved using a wind shear exponent or wind shear coefficient (WSC), variation in wind speed as a function of height. The WSC is subject to temporal variation at low and high frequencies, ranging from diurnal and seasonal variations to disturbance caused by weather patterns; however, in many cases, it is assumed that the WSC remains constant. This assumption creates significant error in resource assessment, increasing uncertainty in projects and potentially significantly impacting the ability to control gird connected wind generators. This paper contributes to the body of knowledge relating to the evaluation and assessment of wind speed, with particular emphasis on the development of techniques to improve the accuracy of estimated wind speed above measurement height. It presents an evaluation of the use of a variable wind shear coefficient methodology based on a distribution of wind shear coefficients which have been implemented in real time. The results indicate that a VWSC provides a more accurate estimate of wind at hub height, ranging from 41% to 4% reduction in root mean squared error (RMSE) between predicted and actual wind speeds when using a variable wind shear coefficient at heights ranging from 33% to 100% above the highest actual wind measurement.

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HILL, WILLIAM G., and HAN A. MULDER. "Genetic analysis of environmental variation." Genetics Research 92, no. 5-6 (December 2010): 381–95. http://dx.doi.org/10.1017/s0016672310000546.

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SummaryEnvironmental variation (VE) in a quantitative trait – variation in phenotype that cannot be explained by genetic variation or identifiable genetic differences – can be regarded as being under some degree of genetic control. Such variation may be either between repeated expressions of the same trait within individuals (e.g. for bilateral traits), in the phenotype of different individuals, where variation within families may differ, or in both components. We consider alternative models for defining the distribution of phenotypes to include a component due to heterogeneity ofVE. We review evidence for the presence of genetic variation inVEand estimates of its magnitude. Typically the heritability ofVEis under 10%, but its genetic coefficient of variation is typically 20% or more. We consider experimental designs appropriate for estimating genetic variance inVEand review alternative methods of estimation. We consider the effects of stabilizing and directional selection onVEand review both the forces that might be maintaining levels ofVEand heritability found in populations. We also evaluate the opportunities for reducingVEin breeding programmes. Although empirical and theoretical studies have increased our understanding of genetic control of environmental variance, many issues remain unresolved.

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Vostrý, L., B. Hofmanová, H. Vostrá Vydrová, J. Přibyl, and I. Majzlík. "Estimation of genetic parameters for melanoma in the Old Kladruber horse." Czech Journal of Animal Science 57, No. 2 (February 21, 2012): 75–82. http://dx.doi.org/10.17221/5134-cjas.

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The aim of this study was to assess the prevalence of melanoma to investigate a possible genetic variation of this trait in the Old Kladruber horse. A total of 564 grey varieties of the Old Kladruber horse, 238 males and 326 females, with five generations of ancestors (n = 1245 animals) were analysed. Melanoma status was recorded for different stages. Three different analyses were conducted: a linear animal model (LM) with melanoma classified into five categories, threshold animal model (TM) with melanoma classified into five categories and threshold animal model (TMb) with melanoma classified into two categories (0 = absence, 1 = presence). All models included the fixed effects of year of evaluation, age, line, sex, greying level, random direct genetic effect, and the effect of animal’s permanent environment. Heritability for melanoma occurrence was estimated for LM – 0.09, for TM – 0.27, and for TMb – 0.11. The coefficient of repeatability was estimated for LM – 0.77, for TM – 0.90, and for TMb – 0.99. The values of the Pearson’s correlation coefficient and Spearmen’s rank correlation coefficient among breeding values estimated by LM, TM, and TMb models were from 0.82 to 0.88 and from 0.83 to 0.90, respectively, for data with pedigree information and from 0.77 to 0.84 and 0.77 to 0.88, respectively, for a subset of animals with measurements. Results suggest that additive genetic variation of melanoma occurrence in the Old Kladruber horse seems large enough to be exploited in a specific breeding programme.  

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Pawlas, Zbyněk, Lev B. Klebanov, Martin Prokop, and Petr Lansky. "Parameters of Spike Trains Observed in a Short Time Window." Neural Computation 20, no. 5 (May 2008): 1325–43. http://dx.doi.org/10.1162/neco.2007.01-07-442.

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We study the estimation of statistical moments of interspike intervals based on observation of spike counts in many independent short time windows. This scenario corresponds to the situation in which a target neuron occurs. It receives information from many neurons and has to respond within a short time interval. The precision of the estimation procedures is examined. As the model for neuronal activity, two examples of stationary point processes are considered: renewal process and doubly stochastic Poisson process. Both moment and maximum likelihood estimators are investigated. Not only the mean but also the coefficient of variation is estimated. In accordance with our expectations, numerical studies confirm that the estimation of mean interspike interval is more reliable than the estimation of coefficient of variation. The error of estimation increases with increasing mean interspike interval, which is equivalent to decreasing the size of window (less events are observed in a window) and with decreasing the number of neurons (lower number of windows).

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Arnholt, Alan T., and Jaimie L. Hebert. "Estimating the Mean with Known Coefficient of Variation." American Statistician 49, no. 4 (November 1995): 367. http://dx.doi.org/10.2307/2684576.

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Arnholt, Alan T., and Jaimie L. Hebert. "Estimating the Mean with Known Coefficient of Variation." American Statistician 49, no. 4 (November 1995): 367–69. http://dx.doi.org/10.1080/00031305.1995.10476185.

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YAMAJI, NORITSUGU, CHIKANORI HASHIMOTO, TAKESHI WATANABE, and KEISUKE ISHIMARU. "ESTIMATION OF DEFORMABILITY OF FRESH CONCRETE MIXED WITH FLY ASH FLOWING THROUGH TAPERED PIPE USING PUMPING TESTER." International Journal of Modern Physics B 25, no. 31 (December 20, 2011): 4299–302. http://dx.doi.org/10.1142/s0217979211066817.

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In study, we quantitatively evaluate and compare high-viscosity concrete with type fourth fly ash and normal concrete. We estimate the deformability of fresh concrete using a pumping tester and investigate the pumpability of concrete. In this study, the range of stable pumping is evaluated by having the average pump oil pressure and the coefficient of variation of the oil pressure. At an average pump oil pressure of less than 0.50 MPa and coefficient of variation of less than 15 %, the range of steady pumping of fly ash concrete and normal concrete are different. Concrete with type fourth fly ash significantly decreases the coefficient of variation of the oil pressure and prevents the stoppage of fresh concrete flowing in pipe.

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Lánský, Petr, and Priscilla E. Greenwood. "Optimal Signal Estimation in Neuronal Models." Neural Computation 17, no. 10 (October 1, 2005): 2240–57. http://dx.doi.org/10.1162/0899766054615653.

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We study optimal estimation of a signal in parametric neuronal models on the basis of interspike interval data. Fisher information is the inverse asymptotic variance of the best estimator. Its dependence on the parameter value indicates accuracy of estimation. Our models assume that the input signal is estimated from neuronal output interspike interval data where the frequency transfer function is sigmoidal. If the coefficient of variation of the interspike interval is constant with respect to the signal, the Fisher information is unimodal, and its maximum for the most estimable signal can be found. We obtain a general result and compare the signal producing maximal Fisher information with the inflection point of the sigmoidal transfer function in several basic neuronal models.

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Shaha, D. C., Y. K. Cho, M. T. Kwak, S. R. Kundu, and K. T. Jung. "Spatial variation of the longitudinal dispersion coefficient in an estuary." Hydrology and Earth System Sciences 15, no. 12 (December 7, 2011): 3679–88. http://dx.doi.org/10.5194/hess-15-3679-2011.

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Abstract. The effective longitudinal dispersion is a primary tool for determining property distributions in estuaries. Most previous studies have examined the longitudinal dispersion coefficient for the average tidal condition. However, information on spatial and temporal variations of this coefficient at low and high tides is scarce. Three years of hydrographic data taken at low and high tide along the main axis of the Sumjin River Estuary (SRE), Korea are used to estimate the spatial and temporal variation of the effective longitudinal dispersion coefficient. The range of the dispersion coefficient is rather broad at high water slack (HWS) and narrower at low water slack (LWS) because of the different tidal amplitudes. The spatially varying dispersion coefficient has maximal values (>300 m2 s−1) near the mouth at high water and decreases gradually upstream, with fluctuations. The temporally varying dispersion coefficient appears to be positively correlated with river discharges at both low and high tide. The dispersion varies with the square root of river discharges at HWS and LWS. The dispersive salt fluxes increases with increasing river discharges and decreases with decreasing river discharges at HWS and LWS. Estimation of the numerical values of the effective longitudinal dispersion coefficient in the SRE can be useful for better understanding of the distributions of other tracers in the SRE as well as for developing and testing hypotheses about various mixing mechanisms.

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46

Shaha, D. C., Y. K. Cho, M. T. Kwak, S. R. Kundu, and K. T. Jung. "Spatial variation of the longitudinal dispersion coefficient in an estuary." Hydrology and Earth System Sciences Discussions 8, no. 4 (August 12, 2011): 7757–80. http://dx.doi.org/10.5194/hessd-8-7757-2011.

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Abstract. The effective longitudinal dispersion is a primary tool for determining property distributions in estuaries. Most previous studies have examined the longitudinal dispersion coefficient for the average tidal condition. However, information on spatial and temporal variations of this coefficient at low and high tide is scarce. Three years of hydrographic data taken at low and high tide along the main axis of the Sumjin River Estuary (SRE), Korea are used to estimate the spatial and temporal variation of the effective longitudinal dispersion coefficient. The range of the dispersion coefficient is rather broad at high water slack (HWS) and narrower at low water slack (LWS) because of the different tidal amplitudes. The spatially varying dispersion coefficient has maximal values (>300 m2 s−1) near the mouth at high water and decreases gradually upstream, with fluctuations. The temporally varying dispersion coefficient appears to be positively correlated with river discharges at both low and high tide. The dispersion varies with the square root of river discharges at HWS and LWS. The dispersive salt fluxes increases with increasing river discharges and decreases with decreasing river discharges at HWS and LWS. Estimation of the numerical values of the effective longitudinal dispersion coefficient in the SRE can be useful for better understanding of the distributions of other tracers in the SRE as well as for developing and testing hypotheses about various mixing mechanisms.

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47

Singh, Housila P., and Nidhi Mathur. "Estimation of population mean when coefficient of variation is known using scrambled response technique." Journal of Statistical Planning and Inference 131, no. 1 (April 2005): 135–44. http://dx.doi.org/10.1016/j.jspi.2004.01.002.

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Subramani, J., and G. Kumarapandiyan. "Estimation of Variance Using Known Coefficient of Variation and Median of an Auxiliary Variable." Journal of Modern Applied Statistical Methods 12, no. 1 (May 1, 2013): 58–64. http://dx.doi.org/10.22237/jmasm/1367381400.

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Garg, Neha, and Menakshi Pachori. "Use of coefficient of variation in calibration estimation of population mean in stratified sampling." Communications in Statistics - Theory and Methods 49, no. 23 (June 10, 2019): 5842–52. http://dx.doi.org/10.1080/03610926.2019.1622729.

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Gabal, A., M. Aly, S. Abdallah, H․ Rady, and H. Roshdy. "Estimation of the Variation Coefficient and Some Local Genetical Resources of Some Common Bean." Egyptian Academic Journal of Biological Sciences, H. Botany 12, no. 2 (August 18, 2021): 41–57. http://dx.doi.org/10.21608/eajbsh.2021.191436.

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Journal articles on the topic 'Estimation of coefficient of variation'

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