Efficient Modelling of Thermal Sensation in Scientific Research Using Ranked Set Sampling
Keywords:
RSS, Logistic Regression, Thermal Sensation, Physiological Modelling, etc.Abstract
The systematic quantification of physiological thermal stress responses serves as a critical parameter for developing robust models to understand how humans adapt the changes in the environment. However, acquiring high-fidelity physiological data, such as thermal sensation, often involves substantial resource expenditure and raises ethical constraints. In this context, the present study aims to develop and evaluate an efficient sampling-based modelling framework for binary physiological responses using Ranked Set Sampling (RSS) integrated with Binary Logistic Regression. A dataset comprising N=3472 observations was analyzed, where thermal sensation was treated as a binary response variable. The proposed RSS-based approach was implemented using ambient air temperature as an auxiliary ranking variable and compared against the conventional Simple Random Sampling (SRS) method. The comparative analysis demonstrates that the RSS-based logistic model yields improved efficiency in parameter estimation and predictive performance relative to SRS. The findings suggest that RSS provides a statistically efficient and ethically advantageous alternative for data collection in physiological studies. Therefore, the proposed framework offers a transformative approach for cost-effective and robust modelling in life science and environmental research.
How to cite this article:
Priya D. Patil, Nutan V. Khangar, Kalpesh P. Amrutkar, Efficient Modelling of Thermal Sensation in Scientific Research Using Ranked Set Sampling, J Adv Res Appl Math Stat, Vol 11, Issue 3&4 2026: Pg. No. 95-104.
DOI: https://doi.org/10.24321/2455.7021.202612
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