Statistical Modeling: New Trends and Applications

Tuba Iram

Abstract

Statistical modeling plays a crucial role in various fields, aiding in decision-making, prediction, and inference. This article presents a comprehensive review of recent literature to identify and discuss the emerging trends in statisticaL modeling. The methodology involved a systematic search of relevant databases using specific keywords and selection criteria. The review highlights the limitations of traditional statistical modeling techniques and emphasizes the need for advancements in the field. The findings reveal several emerging trends, including the integration of machine learning algorithms, Bayesian approaches, and non-parametric methods. Each trend is discussed in detail, providing an overview of the concept, its applications, and supporting examples from the literature. Furthermore, the implications and potential applications of these emerging trends are explored, shedding light on how they can advance various domains, such as healthcare, finance, and social sciences. The article concludes by outlining future research directions and opportunities, underscoring the importance of interdisciplinary collaboration to harness the full potential of these emerging statistical modeling techniques. By staying abreast of these trends, researchers and practitioners can enhance their understanding and application of statistical modeling in the ever-evolving landscape of data analysisand decision-making.

Abstract

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