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Статті в журналах з теми "Coefficient of confidence"

Автор: Grafiati

Опубліковано: 30 травня 2022

Оновлено: 31 травня 2022

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1

Padilla, Miguel A., and Jasmin Divers. "Coefficient Omega Bootstrap Confidence Intervals." Educational and Psychological Measurement 73, no. 6 (July 2, 2013): 956–72. http://dx.doi.org/10.1177/0013164413492765.

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2

Jeyaratnam, S. "Confidence intervals for the correlation coefficient." Statistics & Probability Letters 15, no. 5 (December 1992): 389–93. http://dx.doi.org/10.1016/0167-7152(92)90172-2.

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3

Pitts, Susan M., Rudolf Grübel, and Paul Embrechts. "Confidence bounds for the adjustment coefficient." Advances in Applied Probability 28, no. 03 (September 1996): 802–27. http://dx.doi.org/10.1017/s0001867800046504.

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Анотація:

Let ?(u) be the probability of eventual ruin in the classical Sparre Andersen model of risk theory if the initial risk reserve is u. For a large class of such models ?(u) behaves asymptotically like a multiple of exp (–Ru) where R is the adjustment coefficient; R depends on the premium income rate, the claim size distribution and the distribution of the time between claim arrivals. Estimation of R has been considered by many authors. In the present paper we deal with confidence bounds for R. A variety of methods is used, including jackknife estimation of asymptotic variances and the bootstrap. We show that, under certain assumptions, these procedures result in interval estimates that have asymptotically the correct coverage probabilities. We also give the results of a simulation study that compares the different techniques in some particular cases.

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4

Pitts, Susan M., Rudolf Grübel, and Paul Embrechts. "Confidence bounds for the adjustment coefficient." Advances in Applied Probability 28, no. 3 (September 1996): 802–27. http://dx.doi.org/10.2307/1428182.

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Анотація:

Let ?(u) be the probability of eventual ruin in the classical Sparre Andersen model of risk theory if the initial risk reserve is u. For a large class of such models ?(u) behaves asymptotically like a multiple of exp (–Ru) where R is the adjustment coefficient; R depends on the premium income rate, the claim size distribution and the distribution of the time between claim arrivals. Estimation of R has been considered by many authors. In the present paper we deal with confidence bounds for R. A variety of methods is used, including jackknife estimation of asymptotic variances and the bootstrap. We show that, under certain assumptions, these procedures result in interval estimates that have asymptotically the correct coverage probabilities. We also give the results of a simulation study that compares the different techniques in some particular cases.

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5

YE, Baojuan, and Zhonglin WEN. "Estimating Homogeneity Coefficient and Its Confidence Interval." Acta Psychologica Sinica 44, no. 12 (April 17, 2013): 1687–94. http://dx.doi.org/10.3724/sp.j.1041.2012.01687.

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6

Padilla, Miguel A., Jasmin Divers, and Matthew Newton. "Coefficient Alpha Bootstrap Confidence Interval Under Nonnormality." Applied Psychological Measurement 36, no. 5 (June 18, 2012): 331–48. http://dx.doi.org/10.1177/0146621612445470.

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7

Angus, John E. "Confidence coefficient of approximate two-sided confidence intervals for the binomial probability." Naval Research Logistics 34, no. 6 (December 1987): 845–51. http://dx.doi.org/10.1002/1520-6750(198712)34:6<845::aid-nav3220340609>3.0.co;2-d.

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8

Griffin, Norma S., and Michael E. Crawford. "Measurement of Movement Confidence with a Stunt Movement Confidence Inventory." Journal of Sport and Exercise Psychology 11, no. 1 (March 1989): 26–40. http://dx.doi.org/10.1123/jsep.11.1.26.

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Анотація:

The purposes of this study were (a) to construct and validate a Stunt Movement Confidence Inventory (SMCI) that would reliably discriminate between high- and low-confidence children and (b) to examine perceived confidence in light of assumptions from the movement confidence model. Interaction of three components postulated in the model (competence, potentials for enjoyment, and harm) was studied by analyzing the response patterns of 356 children. Reliability coefficients for item, subscale, total scale, and subject stability ranged from r=.79 to .93. SMCI subscales successfully classified 88% of all subjects with a 52.3% improvement over chance and a validity coefficient of .98. The factor matrix accounted for 49% of the total variance and verified the dominance of the competence subscale and the multivariate nature of the harm variable (subscale). Response profiles of low- and high-confidence groups validated the identity and separability of the model's theoretical components—competence, enjoyment, and harm. The SMCI was reliable and valid in discriminating between high- and low-confidence children.

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9

Andersson, Björn, and Tao Xin. "Large Sample Confidence Intervals for Item Response Theory Reliability Coefficients." Educational and Psychological Measurement 78, no. 1 (June 22, 2017): 32–45. http://dx.doi.org/10.1177/0013164417713570.

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In applications of item response theory (IRT), an estimate of the reliability of the ability estimates or sum scores is often reported. However, analytical expressions for the standard errors of the estimators of the reliability coefficients are not available in the literature and therefore the variability associated with the estimated reliability is typically not reported. In this study, the asymptotic variances of the IRT marginal and test reliability coefficient estimators are derived for dichotomous and polytomous IRT models assuming an underlying asymptotically normally distributed item parameter estimator. The results are used to construct confidence intervals for the reliability coefficients. Simulations are presented which show that the confidence intervals for the test reliability coefficient have good coverage properties in finite samples under a variety of settings with the generalized partial credit model and the three-parameter logistic model. Meanwhile, it is shown that the estimator of the marginal reliability coefficient has finite sample bias resulting in confidence intervals that do not attain the nominal level for small sample sizes but that the bias tends to zero as the sample size increases.

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10

Tonda, Tetsuji, and Kenichi Satoh. "Improvement of Confidence Interval for Linear Varying Coefficient." Japanese Journal of Applied Statistics 42, no. 1 (2013): 11–21. http://dx.doi.org/10.5023/jappstat.42.11.

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11

Banik, Shipra, та B. M. Kibria. "Confidence Intervals for the Population Correlation Coefficient ρ". International Journal of Statistics in Medical Research 5, № 2 (3 червня 2016): 99–111. http://dx.doi.org/10.6000/1929-6029.2016.05.02.4.

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12

Vangel, Mark G. "Confidence Intervals for a Normal Coefficient of Variation." American Statistician 50, no. 1 (February 1996): 21. http://dx.doi.org/10.2307/2685039.

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13

Vangel, Mark G. "Confidence Intervals for a Normal Coefficient of Variation." American Statistician 50, no. 1 (February 1996): 21–26. http://dx.doi.org/10.1080/00031305.1996.10473537.

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14

Bonett, Douglas G. "Confidence interval for a coefficient of quartile variation." Computational Statistics & Data Analysis 50, no. 11 (July 2006): 2953–57. http://dx.doi.org/10.1016/j.csda.2005.05.007.

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15

Tian, Lili, and Gregory E. Wilding. "Confidence interval estimation of a common correlation coefficient." Computational Statistics & Data Analysis 52, no. 10 (June 2008): 4872–77. http://dx.doi.org/10.1016/j.csda.2008.04.002.

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16

Tan, Dandan, Yiming Zhang, and Bingxu Han. "Multi-scales Image Denoising Method Based on Joint Confidence Probability of Coefficients." Recent Patents on Engineering 13, no. 4 (December 27, 2019): 395–402. http://dx.doi.org/10.2174/1872212112666180925151744.

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Анотація:

Background: It is a classic problem that we estimate the original coefficient from the known coefficient disturbed with noise. Methods: This paper proposes an image denoising method which combines the dual-tree complex wavelet with good direction selection and translation invariance. Firstly, we determine the expression of probability density function through estimating the parameters by the variance and the fourth-order moment. Secondly, we propose two assumptions and calculate the joint confidence probability of original coefficient under the situation that the disturbed parental and present coefficients from neighborhood scale are known. Finally, we set the joint confidence probability as shrinkage function of coefficient for implementing the image denoising. Results: The simulation experiment results show that, compared to these traditional methods, this new method can reserve more detail information. Conclusion: Compared to the current methods, our novel algorithm can remove the most noise and reserve the detail texture in denoising results, which can make better visualization. In addition, our algorithm also shows advantage in PSNR.

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17

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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Анотація:

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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18

Thangjai, Warisa, Sa-Aat Niwitpong, and Suparat Niwitpong. "Bayesian Confidence Intervals for Coefficients of Variation of PM10 Dispersion." Emerging Science Journal 5, no. 2 (April 1, 2021): 139–54. http://dx.doi.org/10.28991/esj-2021-01264.

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Анотація:

Herein, we propose the Bayesian approach for constructing the confidence intervals for both the coefficient of variation of a log-normal distribution and the difference between the coefficients of variation of two log-normal distributions. For the first case, the Bayesian approach was compared with large-sample, Chi-squared, and approximate fiducial approaches via Monte Carlo simulation. For the second case, the Bayesian approach was compared with the method of variance estimates recovery (MOVER), modified MOVER, and approximate fiducial approaches using Monte Carlo simulation. The results show that the Bayesian approach provided the best approach for constructing the confidence intervals for both the coefficient of variation of a log-normal distribution and the difference between the coefficients of variation of two log-normal distributions. To illustrate the performances of the confidence limit construction approaches with real data, they were applied to analyze real PM10 datasets from the Nan and Chiang Mai provinces in Thailand, the results of which are in agreement with the simulation results. Doi: 10.28991/esj-2021-01264 Full Text: PDF

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19

Turner, Heather J., Prathiba Natesan, and Robin K. Henson. "Performance Evaluation of Confidence Intervals for Ordinal Coefficient Alpha." Journal of Modern Applied Statistical Methods 16, no. 2 (December 4, 2017): 157–85. http://dx.doi.org/10.22237/jmasm/1509494940.

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20

Berger, Yves G., and İklim Gedik Balay. "Confidence Intervals of Gini Coefficient Under Unequal Probability Sampling." Journal of Official Statistics 36, no. 2 (June 1, 2020): 237–49. http://dx.doi.org/10.2478/jos-2020-0013.

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AbstractWe propose an estimator for the Gini coefficient, based on a ratio of means. We show how bootstrap and empirical likelihood can be combined to construct confidence intervals. Our simulation study shows the estimator proposed is usually less biased than customary estimators. The observed coverages of the empirical likelihood confidence interval proposed are also closer to the nominal value.

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21

Hall, Peter, Michael A. Martin, and William R. Schucany. "Better nonparametric bootstrap confidence intervals for the correlation coefficient." Journal of Statistical Computation and Simulation 33, no. 3 (November 1989): 161–72. http://dx.doi.org/10.1080/00949658908811194.

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22

Chowdhury, Jahir Uddin, and Jery R. Stedinger. "Confidence Interval for Design Floods with Estimated Skew Coefficient." Journal of Hydraulic Engineering 117, no. 7 (July 1991): 811–31. http://dx.doi.org/10.1061/(asce)0733-9429(1991)117:7(811).

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23

Kojima, Kyoko, Takako Matsui, Takeshi Yoshitomi, and Satoshi Ishikawa. "Examination of the confidence coefficient in Humphry automated perimeter." JAPANESE ORTHOPTIC JOURNAL 26 (1998): 173–77. http://dx.doi.org/10.4263/jorthoptic.26.173.

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24

Algina, James, H. J. Keselman, and Randall D. Penfield. "Confidence Intervals for the Squared Multiple Semipartial Correlation Coefficient." Journal of Modern Applied Statistical Methods 7, no. 1 (May 1, 2008): 2–10. http://dx.doi.org/10.22237/jmasm/1209614460.

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25

Tian, Lili. "On confidence intervals of a common intraclass correlation coefficient." Statistics in Medicine 24, no. 21 (2005): 3311–18. http://dx.doi.org/10.1002/sim.2145.

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26

Sievers, Walter. "Standard and bootstrap confidence intervals for the correlation coefficient." British Journal of Mathematical and Statistical Psychology 49, no. 2 (November 1996): 381–96. http://dx.doi.org/10.1111/j.2044-8317.1996.tb01095.x.

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27

Ghosh, J. K., and Rahul Mukerjee. "Improvement in Stein's Procedure using a Random Confidence Coefficient." Calcutta Statistical Association Bulletin 40, no. 1-4 (January 1990): 145–52. http://dx.doi.org/10.1177/0008068319900512.

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28

van der Ark, L. Andries, and Robbie C. M. van Aert. "Comparing confidence intervals for Goodman and Kruskal's gamma coefficient." Journal of Statistical Computation and Simulation 85, no. 12 (June 30, 2014): 2491–505. http://dx.doi.org/10.1080/00949655.2014.932791.

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29

Banik, Shipra, and B. M. Golam Kibria. "Estimating the Population Coefficient of Variation by Confidence Intervals." Communications in Statistics - Simulation and Computation 40, no. 8 (April 19, 2011): 1236–61. http://dx.doi.org/10.1080/03610918.2011.568151.

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30

Altunkaynak, Bulent, and Hamza Gamgam. "Bootstrap confidence intervals for the coefficient of quartile variation." Communications in Statistics - Simulation and Computation 48, no. 7 (February 18, 2018): 2138–46. http://dx.doi.org/10.1080/03610918.2018.1435800.

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31

Shoukri, Mohamed M., Allan Donner, and Abdelmoneim El-Dali. "Covariate-adjusted confidence interval for the intraclass correlation coefficient." Contemporary Clinical Trials 36, no. 1 (September 2013): 244–53. http://dx.doi.org/10.1016/j.cct.2013.07.003.

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32

Lee, James, and K. P. Fung. "Confidence interval of the kappa coefficient by bootstrap resampling." Psychiatry Research 49, no. 1 (October 1993): 97–98. http://dx.doi.org/10.1016/0165-1781(93)90033-d.

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33

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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Анотація:

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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34

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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Анотація:

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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35

Bonett, Douglas G., and Robert M. Price. "Inferential Methods for the Tetrachoric Correlation Coefficient." Journal of Educational and Behavioral Statistics 30, no. 2 (June 2005): 213–25. http://dx.doi.org/10.3102/10769986030002213.

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The tetrachoric correlation describes the linear relation between two continuous variables that have each been measured on a dichotomous scale. The treatment of the point estimate, standard error, interval estimate, and sample size requirement for the tetrachoric correlation is cursory and incomplete in modern psychometric and behavioral statistics texts. A new and simple method of accurately approximating the tetrachoric correlation is introduced. The tetrachoric approximation is then used to derive a simple standard error, confidence interval, and sample size planning formula. The new confidence interval is shown to perform far better than the confidence interval computed by SAS. A method to improve the SAS confidence interval is proposed. All of the new results are computationally simple and are ideally suited for textbook and classroom presentations.

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36

Charter, Richard A. "Confidence Interval Formulas for Split-Half Reliability Coefficients." Psychological Reports 86, no. 3_suppl (June 2000): 1168–70. http://dx.doi.org/10.2466/pr0.2000.86.3c.1168.

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37

Charter, Richard A. "Confidence Interval Formulas for Split-Half Reliability Coefficients." Psychological Reports 86, no. 3_part_2 (June 2000): 1168–70. http://dx.doi.org/10.1177/003329410008600317.2.

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38

Henson, Robin K. "Expanding Reliability Generalization: Confidence Intervals and Charter's Combined Reliability Coefficient." Perceptual and Motor Skills 99, no. 3 (December 2004): 818–20. http://dx.doi.org/10.2466/pms.99.3.818-820.

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39

HENSON, ROBIN K. "EXPANDING RELIABILITY GENERALIZATION: CONFIDENCE INTERVALS AND CHARTER'S COMBINED RELIABILITY COEFFICIENT." Perceptual and Motor Skills 99, no. 7 (2004): 818. http://dx.doi.org/10.2466/pms.99.7.818-820.

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40

Taye, Girma, and Peter Njuho. "Monitoring Field Variability Using Confidence Interval for Coefficient of Variation." Communications in Statistics - Theory and Methods 37, no. 6 (February 11, 2008): 831–46. http://dx.doi.org/10.1080/03610920701762804.

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41

Fan, Jianqing, and Wenyang Zhang. "Simultaneous Confidence Bands and Hypothesis Testing in Varying-coefficient Models." Scandinavian Journal of Statistics 27, no. 4 (December 2000): 715–31. http://dx.doi.org/10.1111/1467-9469.00218.

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42

Kazemi, Mohammad Reaz. "Inference of Common Correlation Coefficient Based on Confidence Distribution Concept." Journal of Statistical Sciences 14, no. 2 (February 1, 2021): 0. http://dx.doi.org/10.29252/jss.14.2.12.

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43

Ukoumunne, Obioha C., Anthony C. Davison, Martin C. Gulliford, and Susan Chinn. "Non-parametric bootstrap confidence intervals for the intraclass correlation coefficient." Statistics in Medicine 22, no. 24 (2003): 3805–21. http://dx.doi.org/10.1002/sim.1643.

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44

Bonett, Douglas G., and Edith Seier. "Confidence Interval for a Coefficient of Dispersion in Nonnormal Distributions." Biometrical Journal 48, no. 1 (February 2006): 144–48. http://dx.doi.org/10.1002/bimj.200410148.

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45

Xiao, Yuanhui, Jiawei Liu, and Madhusudan Bhandary. "Profile Likelihood Based Confidence Intervals for Common Intraclass Correlation Coefficient." Communications in Statistics - Simulation and Computation 39, no. 1 (December 8, 2009): 111–18. http://dx.doi.org/10.1080/03610910903324834.

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46

Abdel-Karim, Amany Hassan. "CONFIDENCE INTERVALS FOR POPULATION COEFFICIENT OF VARIATION OF WEIBULL DISTRIBUTION." Advances and Applications in Statistics 69, no. 2 (July 20, 2021): 145–68. http://dx.doi.org/10.17654/as069020145.

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47

Bonett, Douglas G. "Sample Size Requirements for Testing and Estimating Coefficient Alpha." Journal of Educational and Behavioral Statistics 27, no. 4 (December 2002): 335–40. http://dx.doi.org/10.3102/10769986027004335.

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Анотація:

An approximate test and confidence interval for coefficient alpha are derived. The approximate test and confidence interval are then used to derive closed-form sample size formulas. The sample size formulas can be used to determine the sample size needed to test coefficient alpha with desired power or to estimate coefficient alpha with desired precision. The sample size formulas closely approximate the sample size requirements for an exact confidence interval or an exact test.

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48

Donner, Allan, and George Wells. "A Comparison of Confidence Interval Methods for the Intraclass Correlation Coefficient." Biometrics 42, no. 2 (June 1986): 401. http://dx.doi.org/10.2307/2531060.

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49

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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Анотація:

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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50

Zhang, Wenyang, and Heng Peng. "Simultaneous confidence band and hypothesis test in generalised varying-coefficient models." Journal of Multivariate Analysis 101, no. 7 (August 2010): 1656–80. http://dx.doi.org/10.1016/j.jmva.2010.03.003.

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