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Machine learning textbooks such as Bishop (2006), among others, introduce different regression models. For a more statistical introduction including an extensive overview of the many different semi-parametric methods and non-parametric methods such as kernel methods, see Hastie et al. (2003). For a coverage of key statistical issues including nonlinear regression, identifiability, measures of curvature, autocorrelation, and such, see Seber and Wild (1989). For a large variety of built-in regression techniques, refer to R (http://www.r-project.org/).
Recommended Reading
Machine learning textbooks such as Bishop (2006), among others, introduce different regression models. For a more statistical introduction including an extensive overview of the many different semi-parametric methods and non-parametric methods such as kernel methods, see Hastie et al. (2003). For a coverage of key statistical issues including nonlinear regression, identifiability, measures of curvature, autocorrelation, and such, see Seber and Wild (1989). For a large variety of built-in regression techniques, refer to R (http://www.r-project.org/).
Bishop C (2006) Pattern recognition and machine learning. Springer, New York
Gaffney S, Smyth P (1999) Trajectory clustering with mixtures of regression models. In: ACM SIGKDD, vol 62. ACM, New York, pp 63–72
Geman S, Bienenstock E, Doursat R (1992) Neural networks and the bias/variance dilemma. Neural Comput 4:1–58
Goldberg P, Williams C, Bishop C (1998) Regression with input-dependent noise: a Gaussian process treatment. In: Neural information processing systems, vol 10. MIT
Hastie T, Tibshirani R, Friedman J (Corrected ed) (2003) The elements of statistical learning: data mining, inference, and prediction. Springer, New York
Koenker R (2005) Quantile regression. Cambridge University Press, Cambridge
Nelder JA, Wedderburn RWM (1972) Generalized linear models. J R Stat Soc: Ser A 135: 370–384
Seber G, Wild C (1989) Nonlinear regression. Wiley, New York
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Quadrianto, N., Buntine, W.L. (2017). Regression. In: Sammut, C., Webb, G.I. (eds) Encyclopedia of Machine Learning and Data Mining. Springer, Boston, MA. https://doi.org/10.1007/978-1-4899-7687-1_716
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