Abstract
The probability plot correlation coefficient (PPCC) test has been known as a powerful but easy-to-use goodness-of-fit test. However, the application of PPCC test statistics is sometimes difficult since the test statistics are generally derived in tabulated form and the number of test statistics is significant. In this study, the PPCC test statistics for the normal, Gumbel, gamma, GEV, and Weibull distributions are derived, and regression equations of the PPCC test statistics for those models are formulated as a function of the significance levels, sample sizes, and skewness coefficients depending on the models. Monte Carlo simulation for power tests were performed to compare the rejection capability of the PPCC test with those of the χ2, Cramer von Mises, and Kolmogorov-Smirnov tests for several probability distributions. The power test results indicated that the PPCC and χ2-tests had better rejection performances than the CVM and K-S tests did when the parent and applied models were identical. Moreover, the PPCC test showed the most powerful rejection rate, followed by the χ2-test, while the CVM was the worst when the parent and applied models were different. In addition, the power of rejection increased with sample size when the parent and applied models were different. However, the rejection power did not vary appreciably with sample size when the parent and applied models were identical.
| Original language | English |
|---|---|
| Pages (from-to) | 1-15 |
| Number of pages | 15 |
| Journal | Journal of Hydrology |
| Volume | 355 |
| Issue number | 1-4 |
| DOIs | |
| Publication status | Published - 2008 Jun 20 |
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All Science Journal Classification (ASJC) codes
- Water Science and Technology
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Regression equations of probability plot correlation coefficient test statistics from several probability distributions. / Heo, Jun-Haeng; Kho, Youn Woo; Shin, Hongjoon; Kim, Sooyoung; Kim, Taesoon.
In: Journal of Hydrology, Vol. 355, No. 1-4, 20.06.2008, p. 1-15.Research output: Contribution to journal › Article
TY - JOUR
T1 - Regression equations of probability plot correlation coefficient test statistics from several probability distributions
AU - Heo, Jun-Haeng
AU - Kho, Youn Woo
AU - Shin, Hongjoon
AU - Kim, Sooyoung
AU - Kim, Taesoon
PY - 2008/6/20
Y1 - 2008/6/20
N2 - The probability plot correlation coefficient (PPCC) test has been known as a powerful but easy-to-use goodness-of-fit test. However, the application of PPCC test statistics is sometimes difficult since the test statistics are generally derived in tabulated form and the number of test statistics is significant. In this study, the PPCC test statistics for the normal, Gumbel, gamma, GEV, and Weibull distributions are derived, and regression equations of the PPCC test statistics for those models are formulated as a function of the significance levels, sample sizes, and skewness coefficients depending on the models. Monte Carlo simulation for power tests were performed to compare the rejection capability of the PPCC test with those of the χ2, Cramer von Mises, and Kolmogorov-Smirnov tests for several probability distributions. The power test results indicated that the PPCC and χ2-tests had better rejection performances than the CVM and K-S tests did when the parent and applied models were identical. Moreover, the PPCC test showed the most powerful rejection rate, followed by the χ2-test, while the CVM was the worst when the parent and applied models were different. In addition, the power of rejection increased with sample size when the parent and applied models were different. However, the rejection power did not vary appreciably with sample size when the parent and applied models were identical.
AB - The probability plot correlation coefficient (PPCC) test has been known as a powerful but easy-to-use goodness-of-fit test. However, the application of PPCC test statistics is sometimes difficult since the test statistics are generally derived in tabulated form and the number of test statistics is significant. In this study, the PPCC test statistics for the normal, Gumbel, gamma, GEV, and Weibull distributions are derived, and regression equations of the PPCC test statistics for those models are formulated as a function of the significance levels, sample sizes, and skewness coefficients depending on the models. Monte Carlo simulation for power tests were performed to compare the rejection capability of the PPCC test with those of the χ2, Cramer von Mises, and Kolmogorov-Smirnov tests for several probability distributions. The power test results indicated that the PPCC and χ2-tests had better rejection performances than the CVM and K-S tests did when the parent and applied models were identical. Moreover, the PPCC test showed the most powerful rejection rate, followed by the χ2-test, while the CVM was the worst when the parent and applied models were different. In addition, the power of rejection increased with sample size when the parent and applied models were different. However, the rejection power did not vary appreciably with sample size when the parent and applied models were identical.
UR - http://www.scopus.com/inward/record.url?scp=43949146600&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=43949146600&partnerID=8YFLogxK
U2 - 10.1016/j.jhydrol.2008.01.027
DO - 10.1016/j.jhydrol.2008.01.027
M3 - Article
AN - SCOPUS:43949146600
VL - 355
SP - 1
EP - 15
JO - Journal of Hydrology
JF - Journal of Hydrology
SN - 0022-1694
IS - 1-4
ER -