This study delves into the evaluation of the effectiveness of exponential product-type estimators, incorporating embedded imputation techniques across multiple successive occasions. We conduct a comprehensive analysis, exploring the performance, advantages, and limitations of these estimators in the context of successive data points. Our research contributes to the understanding of statistical estimation methods and provides insights into the application of embedded imputation techniques for enhancing the accuracy and reliability of exponential product-type estimators. The findings aim to inform researchers, practitioners, and policymakers involved in data analysis and statistical modeling.

This study presents a statistical analysis of organizational performance using factor analysis, with a focus on a bank in Ghana. Organizational performance is a critical aspect of business success, and understanding its underlying factors is essential for strategic decision-making and performance improvement. Factor analysis is utilized to identify and analyze the key dimensions influencing organizational performance within the banking sector in Ghana. The study employs data collected from the bank's various performance indicators and organizational metrics to construct a statistical model of organizational performance. The findings shed light on the significant factors contributing to organizational effectiveness and provide insights into areas for targeted intervention and improvement.

Probability theory plays a fundamental role in modeling uncertainty and reasoning under uncertainty in various domains. Integrating probability concepts within type theory offers a formal framework to reason about probabilistic phenomena rigorously. This paper explores the formalization of probability concepts within type theory, aiming to bridge theoretical foundations with practical applications. We review foundational aspects of probability theory, discuss their formal representation in type theory, and highlight applications across computer science, artificial intelligence, and mathematical logic. Through illustrative examples and theoretical insights, we demonstrate how this integration enhances precision in probabilistic reasoning and supports the development of verifiable and reliable systems. This research contributes to advancing the theoretical underpinnings of probabilistic type theory and its practical implications for complex reasoning tasks in computational sciences.

Non-inferiority testing in longitudinal studies involving functional data presents unique challenges due to the dynamic nature of the measurements over time. Traditional methods often fail to adequately address the complex interdependencies and temporal patterns inherent in longitudinal functional datasets. In this paper, we propose a novel approach for assessing non-inferiority in longitudinal functional data. Our method leverages advancements in functional data analysis and incorporates tailored statistical techniques to account for temporal dependencies and variability across subjects. We illustrate the application of our approach through simulated and real-world longitudinal datasets, demonstrating its efficacy in accurately determining non-inferiority while preserving statistical rigor. By addressing these challenges, our approach provides a robust framework for researchers and practitioners aiming to evaluate treatment effects or interventions in longitudinal studies with functional outcomes.