Department of Statistics

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    Uncovering the Statistical Foundations of Bibliometric Techniques and Their Role in Exploring the Data Science Research Evolution
    (Library Information Services, COMSATS University Islamabad, Lahore Campus, 2025) Aleem Ahmad; CIIT/FA23-RST-001/LHR; Dr. Tajammal Hussain; LHR TP 9872
    Sceinometric and bibliometric techniques are widely used in assessing the quality and quantity of research production in all aspects. In sceinometrics techniques, we focus on evaluating the scientific progress in all fields of science, while in bibliometrics, which is basically is a subpart of sceinometrics, we mainly focus on the quality and quantity of the research production. Both techniques are very important for finding the current research trends. These techniques are widely used by many research-funded institutions, decision makers. and the government. These help them with research funding allocations and identify the emerging research areas. These techniques are very useful in evaluating the journal impact factor, ranking universities, countries, and enhancing research efficiency. Because all research production activity creates data so these techniques are very closely related to data science. In Data Science, we collect, analyze, and process the largest sets of research data. We can find its application in every field, such as healthcare, education, and environmental studies, etc. In this study, we are focusing on the statistical foundation of bibliometric techniques and see that what is their role in exploring the data science research field. Many statistical models, such as discriminant analysis, cluster analysis, and VOSviewer software, are used for viewing the scientific landscape of this research. The present research gives us in-depth insight into the progression and evolution of data science as a prominent research field, after using advanced bibliometric techniques on the dataset of all 4424 journal articles till 31 December 2023. This study concludes that data science is evolving and has a potential research domain.
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    Some Memory Type Estimators for Population Variance in Survey Sampling
    (Library Information Services, COMSATS University Islamabad, Lahore Campus, 2025) Umme Habiba; CIIT/FA23/RST/004/LHR; Dr. Riffat Jabeen; LHR TP 9874
    Reducing and estimating population variation is important in survey sampling. These variations can occur in any sampling design, including stratified random sampling. In stratified random sampling the difference in stratum weights increase the variation. This problem can be control with the use of calibration techniques, and auxiliary information. By using this technique, we can increase the accuracy and efficiency of estimator. Improving the efficiency of estimator for population variance estimation is the primary goal of this study. Both exponentially weighted moving average (EWMA) and extended exponentially weighted moving average (EEWMA) memory type statistics are used in this study to estimate population variance. EWMA use only current data whereas EEWMA use both current and past observations. The EWMA and EEWMA statistics are used to build the calibration variance and ratio estimators. The mean square errors are calculated, and the effectiveness of the estimators is illustrated through a discussion of mathematical comparisons. To assess the efficiency of the proposed memory type estimators, a simulation study is conducted and MSE are compare with existing estimators. The results show that proposed estimators are perform efficiently as compared to the existing estimators
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    Data-Driven Sample Size Adaptation in EWMA Control Chart for Improved Monitoring
    (Library Information Services, COMSATS University Islamabad, Lahore Campus, 2025) Gulmakai; CIIT/FA23-RST-002/LHR; Dr. Muhammad Noor-ul-Amin; LHR TP 9873
    Statistical Process Control (SPC) serves as a fundamental methodology for monitoring and maintaining stability in manufacturing processes. In this study, an Adaptive Sample Size-based Exponentially Weighted Moving Average (ASEWMA) control chart is proposed to enhance the detection of process shifts by dynamically adjusting the sample size in response to observed process behavior. The adaptability of the sample size enables the control chart to respond more efficiently to both small and moderate shifts while conserving computational and sampling resources when the process is stable. Further, the proposed control chart is evaluated in the presence of measurement error. The performance of the ASEWMA control chart is rigorously evaluated through extensive Monte Carlo simulations. Key performance indicators such as the Average Run Length (ARL) and Standard Deviation of Run Length (SDRL) are employed to assess its effectiveness in various shift scenarios. Comparative analyses demonstrate that the ASEWMA chart consistently outperforms traditional control charts, including the Fixed Sample Size EWMA (FEWMA) and Variable Sample Size EWMA (VEWMA), particularly in detecting small shifts in the process mean. The findings of this research highlight the ASEWMA chart’s potential to provide a practical and efficient tool for quality monitoring in industrial settings. By achieving a desirable balance between sensitivity to shifts and computational efficiency, the proposed method facilitates the timely detection of process changes while maintaining robustness under in-control conditions