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The relationship between wind speed and PM is inversely proportional, indicating that higher wind speeds enhance the dispersion of PM . In other words, when the wind speed increases, especially during strong winds, it leads to improved removal of PM concentrations from the environment. The mean of the delta-Birnbaum–Saunders distribution can be used to analyze wind speed data for predicting future wind speeds. In this study, we construct generalized confidence interval (GCI), bootstrap confidence interval (BCI) and generalized fiducial confidence interval (GFCI) based on the variance estabilized transformation (VST), Wlison, and Hannig methods. The results of a Monte Carlo simulation study indicate that the coverage probabilities of the generalized confidence interval and the generalized fiducial confidence interval based on VST method are greater than or close to the nominal confidence level of 0.95 and provide the shortest averaged length. The average lengths of the proposed confidence intervals tended to decrease when the sample size increased. Wind speed data from Chiang Rai province, Thailand, was used to illustrate the efficacy of the proposed methods, the results of which were in good agreement with the simulation study findings. [ABSTRACT FROM AUTHOR]

The inverse of the coefficient of variation (ICV), otherwise known as the signal to-noise ratio, is the ratio of the population standard deviation to the population mean. It has often been used in the fields of finance and image processing, among others. In this study, various methods were applied to estimate the confidence intervals (CIs) for the difference between and the ratio of the ICVs of two delta-gamma distributions. The fiducial quantity method, Bayesian CI estimates based on the Jeffreys, uniform, or normal-gamma-beta (NGB) prior, and highest posterior density (HPD) intervals based on the Jeffreys, uniform, or NGB priors were used in this endeavor. A Monte Carlo simulation study was conducted to assess the performances of the proposed CI estimation methods in terms of their coverage probabilities and average lengths. The results indicate that the HPD interval based on the NGB prior or the Jeffreys prior performed well for a small probability of the samples containing zero observations () whereas the fiducial quantity method performed well for large values of . Furthermore, we demonstrate the practicability of the proposed methods using rainfall data from Lampang province, Thailand. [ABSTRACT FROM AUTHOR]

The primary objective of this research is to investigate the construction of confidence intervals (CIs) for the difference and ratio of medians, specifically focusing on the delta-lognormal distribution. The study presents five different CI methods: the generalized confidence interval (GCI), the fiducial generalized confidence interval (FGCI), the method of variance estimates recovery (MOVER), the highest posterior density based on Jeffreys prior (H–Jef), and the Jeffreys rule prior (H–JRul). To assess their performance, we compare coverage probabilities and expected lengths obtained through Monte Carlo simulations. The simulation results demonstrate that the H–Jef interval is effective for estimating the median difference, while the H–JRul interval yields the best results for estimating the median ratio. Finally, all the proposed methods are applied to analyze a dataset of daily rainfall data from Mae Hong Son and Nan Meteorological Stations in Thailand. [ABSTRACT FROM AUTHOR]

Statistical estimation of the difference between normal means with known coefficients of variation has been investigated for the first time. This phenomenon occurs normally in environment and agriculture experiments when the scientist knows the coefficients of variation of their experiments. In this paper, we constructed new confidence intervals for the difference between normal means with known coefficients of variation. We also derived analytic expressions for the coverage probability and the expected length of each confidence interval. To confirm our theoretical results, Monte Carlo simulation will be used to assess the performance of these intervals based on their coverage probabilities and their expected lengths and we also show our result to the real problem. [ABSTRACT FROM AUTHOR]