Deadline Date: 31 December 2025
Sampling is advantageous not only in the fields of Arts, Science, and Technology but is also useful in our daily lives. Due to sample variability, researchers apply scientific probability-based designs to select the sample. This reduces the risk of a distorted view of the population and allows statistically valid inferences to be made from the sample. The basic purpose of sampling is to obtain consistent, efficient, and unbiased estimates of the desired population parameters while considering cost, time, and effort savings. It is emphasized that a sample survey is usually less expensive than a census survey and the desired information may be obtained with a greater degree of accuracy. In sampling theory, emphasis is placed on the efficient use of suitable auxiliary information to improve the precision of estimates and reduce sampling errors.
In recent years, we have been moving toward comprehensive data analytics, a deeper understanding of data, and decision-making based on data. We have lots of data, but not always complete information. To understand the broader population and data behavior, researchers rely on estimation of population parameters using sample statistics and appropriate sampling strategies. Estimation of parameters like mean, median, mode, variance, skewness, and kurtosis is crucial in engineering for understanding system behaviour, performance evaluation, optimization, and predictive modelling.
The efficiency of estimators improves when they are closely related to the variable under study. For instance, historical system performance data can serve as auxiliary information to estimate future outcomes in engineering systems. Estimation is a fundamental problem in nearly every technical field, and simulation studies can enhance the robustness and applicability of these estimation methods.
This special issue aims to bring together researchers and practitioners working on estimation procedures using sampling and simulation theory to share novel ideas and findings, particularly focusing on engineering domains such as reliability analysis, structural modelling, system optimization, and industrial applications.
The Topics of interest for this special issue include, but are not limited to:
Theory of Estimation of Population Parameters:
• Using auxiliary information
• Using Auxiliary Attributes
• Propagation Measurement Errors
• Application of the above in various engineering fields.
Estimation Methods :
• Using machine learning algorithm
• Using linear and logistic regression
• Big Data models
• Application of the above in various engineering fields.
Sampling is advantageous not only in the fields of Arts, Science, and Technology but is also useful in our daily lives. Due to sample variability, researchers apply scientific probability-based designs to select the sample. This reduces the risk of a distorted view of the population and allows statistically valid inferences to be made from the sample. The basic purpose of sampling is to obtain consistent, efficient, and unbiased ... show more
Editorial board
