Abstract
Several modelling techniques are used in Statistics to obtain different models. The method of linear regression analysis is explained in this article. Several steps starting from concepts, calculation, and interpretation, which are involved in the modelling process are stepwise explained. The role of ridge regression for choosing important explanatory variable affecting the outcome is discussed and is used in the development of LASSO (least absolute shrinkage and selection operator) technique. How to find the linear regression model and the subset of important variables using LASSO with an open source R statistical software are illustrated.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Berliner LM (2003) Uncertainty and climate change. Stat Sci 18(4):430–435
Draper NR, Smith H (2014) Applied regression analysis, 3rd edn. Wiley
Hastie T, Tibshirani R, Friedman J (2009) The elements of statistical learning: data mining, inference, and prediction, 2nd edn. Springer
Hastie T, Tibshirani R, Wainwright M (2015) Statistical learning with sparsity: the Lasso and generalization. CRC Press
Heumann C, Schomaker M, Shalabh (2016) Introduction to statistics and data analysis – with exercises, solutions and applications in R. Springer
Hoerl AE, Kennard RW (1970) Ridge regression: biased estimation for nonorthogonal problems. Technometrics 12(1):55–67
Montgomery DC, Peck EA, Vining GG (2012) Introduction to linear regression analysis, 5th edn. Wiley
Rao CR, Toutenburg H, Shalabh, Heumann C (2008) Linear models and generalizations – least squares and alternatives. Springer
Tibshirani R (1996) Regression shrinkage and selection via the LASSO. J R Stat Soc B 58(1):267–288
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2020 Springer Nature Switzerland AG
About this chapter
Cite this chapter
Shalabh, Dhar, S.S. (2020). Statistical Modelling and Variable Selection in Climate Science. In: Roy, N., Roychoudhury, S., Nautiyal, S., Agarwal, S., Baksi, S. (eds) Socio-economic and Eco-biological Dimensions in Resource use and Conservation. Environmental Science and Engineering(). Springer, Cham. https://doi.org/10.1007/978-3-030-32463-6_18
Download citation
DOI: https://doi.org/10.1007/978-3-030-32463-6_18
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-32462-9
Online ISBN: 978-3-030-32463-6
eBook Packages: Earth and Environmental ScienceEarth and Environmental Science (R0)