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.
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References
Antonakis, J., Bendahan, S., Jacquart, P., & Lalive, R. (2010). On making causal claims: A review and recommendations. The Leadership Quarterly, 21(6), 1086–1120.
Barrett, J. P. (1974). The coefficient of determination—some limitations. The American Statistician, 28(1), 19–20.
Bazaraa, M. S., Sherali, H. D., & Shetty, C. (2006). Nonlinear programming: Theory and applications (3rd ed.). Wiley.
Böhning, D. (1992). Multinomial logistic regression algorithm. Annals of the Institute of Statistical Mathematics, 44(1), 197–200.
Box, G. E., & Pierce, D. A. (1970). Distribution of residual autocorrelations in autoregressive-integrated moving average time series models. Journal of the American Statistical Association, 65(332), 1509–1526.
Chatfield, C. (2004). The analysis of time series: An introduction (6th ed.). Chapman & Hall/CRC.
De Myttenaere, A., Golden, B., Le Grand, B., & Rossi, F. (2016). Mean absolute percentage error for regression models. Neurocomputing, 192, 38–48.
Deng, X., Liu, Q., Deng, Y., & Mahadevan, S. (2016). An improved method to construct basic probability assignment based on the confusion matrix for classification problem. Information Sciences, 340, 250–261.
Efron, B., Hastie, T., Johnstone, I., & Tibshirani, R. (2004). Least angle regression. The Annals of Statistics, 32(2), 407–499.
Escudero, L. F., Galindo, E., Garcıa, G., Gomez, E., & Sabau, V. (1999). Schumann, a modeling framework for supply chain management under uncertainty. European Journal of Operational Research, 119(1), 14–34.
Greene, W. H. (2017). Econometric analysis (8th ed.). Pearson.
Hamilton, J. D. (1994). Time series analysis. Princeton University Press.
Hansen, L. P. (1982). Large sample properties of generalized method of moments estimators. Econometrica, 50(4), 1029–1054.
Hastie, T., Tibshirani, R., & Friedman, J. (2001). The elements of statistical learning. Springer Series in statistics (2nd ed.).
Judd, K. L. (1998). Numerical methods in economics. The MIT Press.
Kollerstrom, N. (1992). Thomas simpson and ‘newton’s method of approximation’: An enduring myth. The British Journal for the History of Science, 25(3), 347–354.
Pardoe, I. (2021). Applied regression modeling (3rd ed.). Wiley.
Pregibon, D. (1981). Logistic regression diagnostics. The Annals of Statistics, 9(4), 705–724.
Ramsey, F. L. (1974). Characterization of the partial autocorrelation function. The Annals of Statistics, 2(6), 1296–1301.
Shumway, R. H., & Stoffer, D. S. (2011). Time series analysis and its applications (3rd ed.). Springer.
Silvey, S. D. (1975). Statistical inference. Chapman & Hall/CRC.
Sodhi, M. S., Son, B.-G., & Tang, C. S. (2008). Asp, the art and science of practice: What employers demand from applicants for mba-level supply chain jobs and the coverage of supply chain topics in mba courses. Interfaces, 38(6), 469–484.
Strang, G. (2019). Linear algebra and learning from data. Wellesley-Cambridge Press.
Train, K. E. (2009). Discrete choice methods with simulation (2nd ed.). Cambridge University Press.
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Biçer, I. (2023). Analytical Foundations: Predictive and Prescriptive Analytics. In: Supply Chain Analytics. Springer Texts in Business and Economics. Springer, Cham. https://doi.org/10.1007/978-3-031-30347-0_2
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DOI: https://doi.org/10.1007/978-3-031-30347-0_2
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