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Journal articles on the topic 'Coefficient of confidence'

Author: Grafiati

Published: 30 May 2022

Last updated: 31 July 2025

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1

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

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2

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

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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.

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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 (1996): 802–27. http://dx.doi.org/10.1017/s0001867800046504.

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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.

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4

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

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5

YE, Baojuan, and Zhonglin WEN. "Estimating Homogeneity Coefficient and Its Confidence Interval." Acta Psychologica Sinica 44, no. 12 (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 (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 (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 (1989): 26–40. http://dx.doi.org/10.1123/jsep.11.1.26.

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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%

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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 (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 p

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10

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

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11

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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12

Vangel, Mark G. "Confidence Intervals for a Normal Coefficient of Variation." American Statistician 50, no. 1 (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 (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 (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 (2008): 4872–77. http://dx.doi.org/10.1016/j.csda.2008.04.002.

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16

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 variatio

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17

Muzakar, Abdullah, Sitti Rohmi Djalilah, and Muhamad Suhardi. "Collaborative Confidence: Transforming Teacher Performance through Teamwork and Self-Efficacy." Nidhomul Haq : Jurnal Manajemen Pendidikan Islam 9, no. 3 (2024): 744–55. https://doi.org/10.31538/ndhq.v9i3.71.

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This study aims to comprehensively analyze the influence of teamwork and self-efficacy on teacher performance at a public junior high school in Jonggat District, Central Lombok Regency. Data were collected through participant observation and questionnaires using a Likert scale of 1-5. The method used was a survey with a correlational approach, while data analysis was carried out using descriptive and inferential statistics, including path analysis. The results of the study showed that (1) teamwork has a direct positive effect on teacher performance, with a correlation coefficient of 0.582 and

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18

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 (2019): 395–402. http://dx.doi.org/10.2174/1872212112666180925151744.

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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

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19

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 interval

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20

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

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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 sho

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21

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 (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 t

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22

Berger, Yves G., and İklim Gedik Balay. "Confidence Intervals of Gini Coefficient Under Unequal Probability Sampling." Journal of Official Statistics 36, no. 2 (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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23

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 (2014): 2491–505. http://dx.doi.org/10.1080/00949655.2014.932791.

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24

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

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25

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 (2017): 157–85. http://dx.doi.org/10.22237/jmasm/1509494940.

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26

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 (2008): 2–10. http://dx.doi.org/10.22237/jmasm/1209614460.

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27

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

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28

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

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29

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

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30

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 (1989): 161–72. http://dx.doi.org/10.1080/00949658908811194.

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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 (2013): 244–53. http://dx.doi.org/10.1016/j.cct.2013.07.003.

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32

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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33

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 (2011): 1236–61. http://dx.doi.org/10.1080/03610918.2011.568151.

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34

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

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35

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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36

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

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38

Bonett, Douglas G., and Robert M. Price. "Inferential Methods for the Tetrachoric Correlation Coefficient." Journal of Educational and Behavioral Statistics 30, no. 2 (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 confid

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39

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

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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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40

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

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41

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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42

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

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43

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

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44

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

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45

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

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46

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 (2009): 111–18. http://dx.doi.org/10.1080/03610910903324834.

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47

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

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48

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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49

Yan, Yuxin, Shuanghua Luo, and Cheng-yi Zhang. "Statistical Inference for Partially Linear Varying Coefficient Quantile Models with Missing Responses." Symmetry 14, no. 11 (2022): 2258. http://dx.doi.org/10.3390/sym14112258.

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The construction of confidence intervals is investigated for the partially linear varying coefficient quantile model with missing random responses. Combined with quantile regression, an imputation-based empirical likelihood method is proposed to construct confidence intervals for parametric and varying coefficient components. Then, it is proved that the proposed empirical log-likelihood ratios are asymptotically Chi-square in theory. Finally, the symmetry confidence intervals of the parametric components and the point-by-point confidence intervals of the varying coefficient components are cons

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50

Alhihi, Suad, and Maalee Almheidat. "Estimation of Pianka Overlapping Coefficient for Two Exponential Distributions." Mathematics 11, no. 19 (2023): 4152. http://dx.doi.org/10.3390/math11194152.

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Overlapping coefficients (OVL) are commonly used to estimate the similarity between populations in terms of their density functions. In this paper, we consider Pianka’s overlap coefficient for two exponential populations. The methods for statistical inference of Pianka’s coefficient are presented. The bias and mean square error (MSE) of the maximum likelihood estimator (MLE) and the Bayes estimator of Pianka’s overlap coefficient are investigated by simulation. Confidence intervals for Pianka’s overlap measure are constructed.

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