Open Access Open Access  Restricted Access Subscription or Fee Access

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 analysis
and decision-making.


Keywords


Statistical modeling, Bayesian, data analysis, nonparametric methods, algorithms

Full Text:

PDF

References


Neale MC, Boker SM, Xie G, Maes HM. Statistical modeling. Richmond, VA: Department of Psychiatry, Virginia Commonwealth University. 1999: 31.

Chambers John M, Trevor J. Hastie. Statistical models. Statistical models in S. Routledge, 2017. 13–44.

Neale MC, Boker SM, Xie G, Maes HH. Mx: statistical modeling. Richmond, VA: Department of Psychiatry, Medical College of Virginia; 1997.

Kroese Dirk P, Joshua CC Chan. Statistical modeling and computation. New York: Springer, 2014, p. 400.

Rissanen Jorma. Information and complexity in statistical modeling.. New York: Springer, 2007, Vol. 152.

McCullagh P. What is a statistical model? Ann Stat. 2002 Oct; 30(5): 1225–310.

Neter John, et al. Applied linear statistical models. Irwin, 4th ed. (1996): 318.

Roos T, Myllymaki P, Tirri H. A statistical modeling approach to location estimation. IEEE Trans Mob Comput. 2002 Jan; 1(1): 59–69.

Kirzhnits DA, Lozovik YE, Shpatakovskaya GV. Statistical model of matter. Sovi Phys Usp. 1975 Sep 30; 18(9): 649.

Gao G. Statistical modeling of SAR images: A survey. Sensors. 2010 Jan 21; 10(1): 775–95.

Ohno-Machado L. Modeling medical prognosis: survival analysis techniques. J Biomed Inform. 2001 Dec 1; 34(6): 428–39.

Villarroya S, Baumann P. On the integration of machine learning and array databases. In 2020 IEEE 36th International conference on data engineering (ICDE), IEEE. 2020 Apr 20; 1786–1789.

Spiegelhalter DJ, Freedman LS, Parmar MK. Bayesian approaches to randomized trials. J R Stat Soc: Series A (Stat Soc). 1994 May; 157(3): 357–87.

Härdle W, Linton O. Applied nonparametric methods. Handbook of Econometrics. 1994 Jan 1; 4: 2295–339.

Pearl J. Causal inference. Causality: objectives and assessment. 2010 Feb; 18: 39–58.

Anagnostopoulos I, Zeadally S, Exposito E. Handling big data: research challenges and future directions. J Supercomput. 2016 Apr; 72: 1494–516.

Marcinkevičs R, Vogt JE. Interpretability and explainability: A machine learning zoo mini-tour. arXiv preprint arXiv:2012.01805. 2020 Dec 3.


Refbacks

  • There are currently no refbacks.