Search

Your search keyword '"Niwitpong, Suparat"' showing total 216 results
Start Over Author "Niwitpong, Suparat"
216 results on '"Niwitpong, Suparat"'

1. Confidence Intervals for the Median of the Delta-Lognormal Distribution with Application

Author

Janthasuwan, Usanee, Niwitpong, Sa-Aat, Niwitpong, Suparat, Kacprzyk, Janusz, Series Editor, Gomide, Fernando, Advisory Editor, Kaynak, Okyay, Advisory Editor, Liu, Derong, Advisory Editor, Pedrycz, Witold, Advisory Editor, Polycarpou, Marios M., Advisory Editor, Rudas, Imre J., Advisory Editor, Wang, Jun, Advisory Editor, Iglesias, Andres, editor, Shin, Jungpil, editor, Patel, Bharat, editor, and Joshi, Amit, editor

Published
2024
Full Text
View/download PDF

5. The Bayesian Confidence Intervals for the Coefficient of Variation of a Weibull Distribution

Author

La-ongkaew, Manussaya, Niwitpong, Sa-Aat, Niwitpong, Suparat, Ceccarelli, Marco, Series Editor, Agrawal, Sunil K., Advisory Editor, Corves, Burkhard, Advisory Editor, Glazunov, Victor, Advisory Editor, Hernández, Alfonso, Advisory Editor, Huang, Tian, Advisory Editor, Jauregui Correa, Juan Carlos, Advisory Editor, Takeda, Yukio, Advisory Editor, Carbone, Giuseppe, editor, Laribi, Med Amine, editor, and Jiang, Zhiyu, editor

Published
2023
Full Text
View/download PDF

7. Confidence Intervals for Mean of Delta Two-Parameter Exponential Distribution

Author

Khooriphan, Wansiri, Niwitpong, Sa-Aat, Niwitpong, Suparat, Goos, Gerhard, Founding Editor, Hartmanis, Juris, Founding Editor, Bertino, Elisa, Editorial Board Member, Gao, Wen, Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Woeginger, Gerhard, Editorial Board Member, Yung, Moti, Editorial Board Member, Honda, Katsuhiro, editor, Entani, Tomoe, editor, Ubukata, Seiki, editor, Huynh, Van-Nam, editor, and Inuiguchi, Masahiro, editor

Published
2022
Full Text
View/download PDF

8. Analysis of Medical Data Using Interval Estimators for Common Mean of Gaussian Distributions with Unknown Coefficients of Variation

Author

Thangjai, Warisa, Niwitpong, Sa-Aat, Niwitpong, Suparat, Goos, Gerhard, Founding Editor, Hartmanis, Juris, Founding Editor, Bertino, Elisa, Editorial Board Member, Gao, Wen, Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Woeginger, Gerhard, Editorial Board Member, Yung, Moti, Editorial Board Member, Honda, Katsuhiro, editor, Entani, Tomoe, editor, Ubukata, Seiki, editor, Huynh, Van-Nam, editor, and Inuiguchi, Masahiro, editor

Published
2022
Full Text
View/download PDF

9. Estimation methods for the variance of Birnbaum-Saunders distribution containing zero values with application to wind speed data in Thailand.

Author

Ratasukharom, Natchaya, Niwitpong, Sa-Aat, and Niwitpong, Suparat

Subjects
MONTE Carlo method, WIND speed, PARTICULATE matter, STATISTICAL models, AIR pollution
Abstract

Thailand is currently grappling with a severe problem of air pollution, especially from small particulate matter (PM), which poses considerable threats to public health. The speed of the wind is pivotal in spreading these harmful particles across the atmosphere. Given the inherently unpredictable wind speed behavior, our focus lies in establishing the confidence interval (CI) for the variance of wind speed data. To achieve this, we will employ the delta-Birnbaum-Saunders (delta-BirSau) distribution. This statistical model allows for analyzing wind speed data and offers valuable insights into its variability and potential implications for air quality. The intervals are derived from ten different methods: generalized confidence interval (GCI), bootstrap confidence interval (BCI), generalized fiducial confidence interval (GFCI), and normal approximation (NA). Specifically, we apply GCI, BCI, and GFCI while considering the estimation of the proportion of zeros using the variance stabilized transformation (VST), Wilson, and Hannig methods. To evaluate the performance of these methods, we conduct a simulation study using Monte Carlo simulations in the R statistical software. The study assesses the coverage probabilities and average widths of the proposed confidence intervals. The simulation results reveal that GFCI based on the Wilson method is optimal for small sample sizes, GFCI based on the Hannig method excels for medium sample sizes, and GFCI based on the VST method stands out for large sample sizes. To further validate the practical application of these methods, we employ daily wind speed data from an industrial area in Prachin Buri and Rayong provinces, Thailand. [ABSTRACT FROM AUTHOR]

Published
2024
Full Text
View/download PDF

10. Confidence Interval for Coefficient of Variation of Inverse Gamma Distributions

Author

Kaewprasert, Theerapong, Niwitpong, Sa-Aat, Niwitpong, Suparat, Goos, Gerhard, Founding Editor, Hartmanis, Juris, Founding Editor, Bertino, Elisa, Editorial Board Member, Gao, Wen, Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Woeginger, Gerhard, Editorial Board Member, Yung, Moti, Editorial Board Member, Huynh, Van-Nam, editor, Entani, Tomoe, editor, Jeenanunta, Chawalit, editor, Inuiguchi, Masahiro, editor, and Yenradee, Pisal, editor

Published
2020
Full Text
View/download PDF

11. Bayesian Confidence Intervals for Means of Normal Distributions with Unknown Coefficients of Variation

Author

Thangjai, Warisa, Niwitpong, Sa-Aat, Niwitpong, Suparat, Goos, Gerhard, Founding Editor, Hartmanis, Juris, Founding Editor, Bertino, Elisa, Editorial Board Member, Gao, Wen, Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Woeginger, Gerhard, Editorial Board Member, Yung, Moti, Editorial Board Member, Huynh, Van-Nam, editor, Entani, Tomoe, editor, Jeenanunta, Chawalit, editor, Inuiguchi, Masahiro, editor, and Yenradee, Pisal, editor

Published
2020
Full Text
View/download PDF

12. The Bayesian Confidence Interval for the Mean of the Zero-Inflated Poisson Distribution

Author

Junnumtuam, Sunisa, Niwitpong, Sa-Aat, Niwitpong, Suparat, Goos, Gerhard, Founding Editor, Hartmanis, Juris, Founding Editor, Bertino, Elisa, Editorial Board Member, Gao, Wen, Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Woeginger, Gerhard, Editorial Board Member, Yung, Moti, Editorial Board Member, Huynh, Van-Nam, editor, Entani, Tomoe, editor, Jeenanunta, Chawalit, editor, Inuiguchi, Masahiro, editor, and Yenradee, Pisal, editor

Published
2020
Full Text
View/download PDF

13. Generalized Confidence Interval of the Ratio of Coefficients of Variation of Birnbaum-Saunders Distribution

Author

Puggard, Wisunee, Niwitpong, Sa-Aat, Niwitpong, Suparat, Goos, Gerhard, Founding Editor, Hartmanis, Juris, Founding Editor, Bertino, Elisa, Editorial Board Member, Gao, Wen, Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Woeginger, Gerhard, Editorial Board Member, Yung, Moti, Editorial Board Member, Huynh, Van-Nam, editor, Entani, Tomoe, editor, Jeenanunta, Chawalit, editor, Inuiguchi, Masahiro, editor, and Yenradee, Pisal, editor

Published
2020
Full Text
View/download PDF

14. Confidence Intervals for the Difference Between the Coefficients of Variation of Inverse Gaussian Distributions

Author

Chankham, Wasana, Niwitpong, Sa-Aat, Niwitpong, Suparat, Goos, Gerhard, Founding Editor, Hartmanis, Juris, Founding Editor, Bertino, Elisa, Editorial Board Member, Gao, Wen, Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Woeginger, Gerhard, Editorial Board Member, Yung, Moti, Editorial Board Member, Huynh, Van-Nam, editor, Entani, Tomoe, editor, Jeenanunta, Chawalit, editor, Inuiguchi, Masahiro, editor, and Yenradee, Pisal, editor

Published
2020
Full Text
View/download PDF

15. Generalized Confidence Interval for the Difference Between Percentiles of Birnbaum–Saunders Distributions and Its Application to PM2.5 in Thailand.

Author

Thangjai, Warisa, Niwitpong, Sa-Aat, Niwitpong, Suparat, and Kumar, Rajesh

Subjects
PERCENTILES, CONFIDENCE intervals, INFERENTIAL statistics, DATA analysis
Abstract

The Birnbaum–Saunders distribution is of particular interest for statistical inference. This distribution represents the failure time distribution in engineering. In addition, the Birnbaum–Saunders distribution is commonly used in different areas of science and engineering. Percentiles are a frequently employed statistical concept. Percentiles help ascertain the position of an observation concerning the percentage of data points below it. These percentiles serve as indicators of both the central tendency and the dispersion of data. While comparing two data distributions, the mean is typically the most dependable parameter for describing the population. However, in situations where the distribution exhibits significant skewness, percentiles may sometimes offer a more reliable representation. Herein, the confidence intervals for the difference between percentiles of Birnbaum–Saunders distributions were constructed by the generalized confidence interval (GCI) approach, the bootstrap approach, the Bayesian approach, and the highest posterior density (HPD) approach. A Monte Carlo simulation was conducted to evaluate the performance of the confidence intervals. The performance was considered via coverage probability and average width. The findings suggest that utilizing the GCI approach is advisable for estimating confidence intervals for the disparity between two percentiles. Ultimately, the outcomes of the simulation investigation, coupled with an application in the field of environmental sciences, were outlined. [ABSTRACT FROM AUTHOR]

Published
2024
Full Text
View/download PDF

16. Estimating the Confidence Interval for the Common Coefficient of Variation for Multiple Inverse Gaussian Distributions.

Author

Chankham, Wasana, Niwitpong, Sa-Aat, and Niwitpong, Suparat

Subjects
INVERSE Gaussian distribution, MONTE Carlo method, DISTRIBUTION (Probability theory), CONTINUOUS distributions, CONFIDENCE intervals, GAUSSIAN distribution
Abstract

The inverse Gaussian distribution is a two-parameter continuous probability distribution with positive support, which is used to account for the asymmetry of the positively skewed data that are often seen when modeling environmental phenomena, such as P M 2.5 levels. The coefficient of variation is often used to assess variability within datasets, and the common coefficient of variation of several independent samples can be used to draw inferences between them. Herein, we provide estimation methods for the confidence interval for the common coefficient of variation of multiple inverse Gaussian distributions by using the generalized confidence interval (GCI), the fiducial confidence interval (FCI), the adjusted method of variance estimates recovery (MOVER), and the Bayesian credible interval (BCI) and highest posterior density (HPD) methods using the Jeffreys prior rule. The estimation methods were evaluated based on their coverage probabilities and average lengths, using a Monte Carlo simulation study. The findings indicate the superiority of the GCI over the other methods for nearly all of the scenarios considered. This was confirmed for a real-world scenario involving P M 2.5 data from three provinces in northeastern Thailand that followed inverse Gaussian distributions. [ABSTRACT FROM AUTHOR]

Published
2024
Full Text
View/download PDF

31. The Simultaneous Confidence Interval for the Ratios of the Coefficients of Variation of Multiple Inverse Gaussian Distributions and Its Application to PM 2.5 Data.

Author

Chankham, Wasana, Niwitpong, Sa-Aat, and Niwitpong, Suparat

Subjects
INVERSE Gaussian distribution, CONFIDENCE intervals, FOREST fires, AIR pollution
Abstract

Due to slash/burn agricultural activity and frequent forest fires, P M 2.5 has become a significant air pollution problem in Thailand, especially in the north and north east regions. Since its dispersion differs both spatially and temporally, estimating P M 2.5 concentrations discretely by area, for which the inverse Gaussian distribution is suitable, can provide valuable information. Herein, we provide derivations of the simultaneous confidence interval for the ratios of the coefficients of variation of multiple inverse Gaussian distributions using the generalized confidence interval, the Bayesian interval based on the Jeffreys' rule prior, the fiducial interval, and the method of variance estimates recovery. The efficacies of these methods were compared by considering the coverage probability and average length obtained from simulation results of daily P M 2.5 datasets. The findings indicate that in most instances, the fiducial method with the highest posterior density demonstrated a superior performance. However, in certain scenarios, the Bayesian approach using the Jeffreys' rule prior for the highest posterior density yielded favorable results. [ABSTRACT FROM AUTHOR]

Published
2024
Full Text
View/download PDF

34. Confidence Intervals for the Ratio of Coefficients of Variation in the Two-Parameter Exponential Distributions

Author

Sangnawakij, Patarawan, Niwitpong, Sa-Aat, Niwitpong, Suparat, Hutchison, David, Series editor, Kanade, Takeo, Series editor, Kittler, Josef, Series editor, Kleinberg, Jon M., Series editor, Mattern, Friedemann, Series editor, Mitchell, John C., Series editor, Naor, Moni, Series editor, Pandu Rangan, C., Series editor, Steffen, Bernhard, Series editor, Terzopoulos, Demetri, Series editor, Tygar, Doug, Series editor, Weikum, Gerhard, Series editor, Huynh, Van-Nam, editor, Inuiguchi, Masahiro, editor, Le, Bac, editor, Le, Bao Nguyen, editor, and Denoeux, Thierry, editor

Published
2016
Full Text
View/download PDF

35. Confidence Intervals for Common Mean of Normal Distributions with Known Coefficient of Variation

Author

Sodanin, Sukritta, Niwitpong, Sa-Aat, Niwitpong, Suparat, Hutchison, David, Series editor, Kanade, Takeo, Series editor, Kittler, Josef, Series editor, Kleinberg, Jon M., Series editor, Mattern, Friedemann, Series editor, Mitchell, John C., Series editor, Naor, Moni, Series editor, Pandu Rangan, C., Series editor, Steffen, Bernhard, Series editor, Terzopoulos, Demetri, Series editor, Tygar, Doug, Series editor, Weikum, Gerhard, Series editor, Huynh, Van-Nam, editor, Inuiguchi, Masahiro, editor, Le, Bac, editor, Le, Bao Nguyen, editor, and Denoeux, Thierry, editor

Published
2016
Full Text
View/download PDF

36. Confidence Intervals for Common Variance of Normal Distributions

Author

Smithpreecha, Narudee, Niwitpong, Sa-Aat, Niwitpong, Suparat, Hutchison, David, Series editor, Kanade, Takeo, Series editor, Kittler, Josef, Series editor, Kleinberg, Jon M., Series editor, Mattern, Friedemann, Series editor, Mitchell, John C., Series editor, Naor, Moni, Series editor, Pandu Rangan, C., Series editor, Steffen, Bernhard, Series editor, Terzopoulos, Demetri, Series editor, Tygar, Doug, Series editor, Weikum, Gerhard, Series editor, Huynh, Van-Nam, editor, Inuiguchi, Masahiro, editor, Le, Bac, editor, Le, Bao Nguyen, editor, and Denoeux, Thierry, editor

Published
2016
Full Text
View/download PDF

37. Simultaneous Fiducial Generalized Confidence Intervals for All Differences of Coefficients of Variation of Log-Normal Distributions

Author

Thangjai, Warisa, Niwitpong, Sa-Aat, Niwitpong, Suparat, Hutchison, David, Series editor, Kanade, Takeo, Series editor, Kittler, Josef, Series editor, Kleinberg, Jon M., Series editor, Mattern, Friedemann, Series editor, Mitchell, John C., Series editor, Naor, Moni, Series editor, Pandu Rangan, C., Series editor, Steffen, Bernhard, Series editor, Terzopoulos, Demetri, Series editor, Tygar, Doug, Series editor, Weikum, Gerhard, Series editor, Huynh, Van-Nam, editor, Inuiguchi, Masahiro, editor, Le, Bac, editor, Le, Bao Nguyen, editor, and Denoeux, Thierry, editor

Published
2016
Full Text
View/download PDF

38. Simultaneous Confidence Intervals for All Pairwise Differences between Means of Weibull Distributions.

Author

La-ongkaew, Manussaya, Niwitpong, Sa-Aat, and Niwitpong, Suparat

Subjects
WEIBULL distribution, DISTRIBUTION (Probability theory), GAMMA distributions, WIND speed, CONTINUOUS distributions
Abstract

The Weibull distribution is a continuous probability distribution that finds wide application in various fields for analyzing real-world data. Specifically, wind speed data often adhere to the Weibull distribution. In our study, our aim is to compare the mean wind speed datasets from different areas in Thailand. To achieve this, we proposed simultaneous confidence intervals for all pairwise differences between the means of Weibull distributions. The generalized confidence interval (GCI), method of variance estimates recovery (MOVER), and a Bayesian approach, utilizing both gamma and uniform prior distributions, are proposed to construct simultaneous confidence intervals. Through simulations, we find that the Bayesian highest posterior density (HPD) interval using a gamma prior distribution demonstrates satisfactory performance, while the GCI proves to be a viable alternative. Finally, we applied these proposed approaches to real wind speed data in northeastern and southern Thailand to illustrate their effectiveness and practicality. [ABSTRACT FROM AUTHOR]

Published
2023
Full Text
View/download PDF

39. Confidence Intervals for the Ratio of Coefficients of Variation of the Gamma Distributions

Author

Sangnawakij, Patarawan, Niwitpong, Sa-Aat, Niwitpong, Suparat, Hutchison, David, Series editor, Kanade, Takeo, Series editor, Kittler, Josef, Series editor, Kleinberg, Jon M., Series editor, Mattern, Friedemann, Series editor, Mitchell, John C., Series editor, Naor, Moni, Series editor, Pandu Rangan, C., Series editor, Steffen, Bernhard, Series editor, Terzopoulos, Demetri, Series editor, Tygar, Doug, Series editor, Weikum, Gerhard, Series editor, Huynh, Van-Nam, editor, Inuiguchi, Masahiro, editor, and Demoeux, Thierry, editor

Published
2015
Full Text
View/download PDF

40. Confidence Intervals for the Difference Between Normal Means with Known Coefficients

Author

Niwitpong, Suparat, Niwitpong, Sa-Aat, Hutchison, David, Series editor, Kanade, Takeo, Series editor, Kittler, Josef, Series editor, Kleinberg, Jon M., Series editor, Mattern, Friedemann, Series editor, Mitchell, John C., Series editor, Naor, Moni, Series editor, Pandu Rangan, C., Series editor, Steffen, Bernhard, Series editor, Terzopoulos, Demetri, Series editor, Tygar, Doug, Series editor, Weikum, Gerhard, Series editor, Huynh, Van-Nam, editor, Inuiguchi, Masahiro, editor, and Demoeux, Thierry, editor

Published
2015
Full Text
View/download PDF

Catalog

Books, media, physical & digital resources
See catalog results