Analytical Foundations: Predictive and Prescriptive Analytics

Işık Biçer
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Işık Biçer: York University

Chapter Chapter 2 in Supply Chain Analytics, 2023, pp 27-74 from Springer

Abstract: Abstract In this chapter, we review important predictive and prescriptive models that can be applied to supply chain problems. We begin with linear models and outline basic assumptions of the ordinary least squares (OLS) approach. Then, we discuss how to extend the OLS method when some of the assumptions are violated. We focus on the generalized least squares (GLS), two-stage least squares (2SLS), generalized method of moments (GMM) and time series analysis as the methods of remediation of restrictive assumptions. We also review the machine learning regularization approaches and classification methods that are proven to be effective in dealing with high dimensionality and categorical variable issues. We later introduce some fundamental theories of predictive analytics that are used in the following chapters of this book.

Keywords: Linear regression; Logistic regression; Endogeneity; Time series analysis; Numerical optimization (search for similar items in EconPapers)
Date: 2023
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Persistent link: https://EconPapers.repec.org/RePEc:spr:sptchp:978-3-031-30347-0_2

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DOI: 10.1007/978-3-031-30347-0_2

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