Work

Statistical Estimation of Common Percentile in Birnbaum–Saunders Distributions: Insights from PM2.5 Data in Thailand

Year: 2026

Type: article

Abstract: This study develops approaches for estimating the common percentile of Birnbaum–Saunders (BS) distributions and applies them to daily PM2.5 concentration data from six monitoring stations in Chiang Mai Province, Thailand. Percentiles provide a robust representation of typical pollutant exposure, being less sensitive to outliers and suitable for skewed environmental data. Estimating the same percentile across multiple monitoring sites offers a standardized metric for regional air quality assessment, enabling meaningful comparisons and informing evidence-based environmental policy. Four statistical approaches—Generalized Confidence Interval (GCI), bootstrap, Bayesian, and Highest Posterior Density (HPD)—were employed to construct confidence intervals (CIs) for the common percentile. Simulation studies evaluated the methods in terms of average length (AL) and coverage probability (CP), showing that the GCI approach offers the best balance between precision and reliability. Application to real PM2.5 data confirmed that the BS distribution appropriately models pollutant concentrations and that the common percentile provides a meaningful measure for environmental assessment. These findings highlight the GCI method as a robust tool for constructing CIs in environmental data analysis.

Source: Symmetry

DOI: 10.3390/sym18010100

Authors: Warisa Thangjai, Sa-Aat Niwitpong, Sa-Aat Niwitpong, Suparat Niwitpong, Suparat Niwitpong, Rattana Prommai

Institutions: Ramkhamhaeng University, King Mongkut's University of Technology North Bangkok, University of Phayao

Language: English

Topic: Statistical Distribution Estimation and Applications

Subfield: Statistics and Probability

Field: Mathematics

Domain: Physical Sciences

Open Access status: gold

Estimated APC paid: $2,896

Funder: King Mongkut's University of Technology North Bangkok