Kernel regularized least squares: Reducing misspecification bias with a flexible and interpretable machine learning approach

Political Analysis

Volumn 22, Issue 2, 2014, Pages 143-168

  Hainmueller, Jens ^a   Hazlett, Chad ^a  

^a MASSACHUSETTS INSTITUTE OF TECHNOLOGY   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

EID: 84898656994     PISSN: 10471987     EISSN: 14764989     Source Type: Journal    
DOI: 10.1093/pan/mpt019     Document Type: Article

References (17)

1. Beck, N., G. King, and L. Zeng. 2000. Improving quantitative studies of international conflict: A conjecture. American Political Science Review 94:21-36.
2. Brambor, T., W. Clark, and M. Golder. 2006. Understanding interaction models: Improving empirical analyses. Political Analysis 14(1):63-82.
3. De Vito, E., A. Caponnetto, and L. Rosasco. 2005. Model selection for regularized least-squares algorithm in learning theory. Foundations of Computational Mathematics 5(1):59-85.
4. Evgeniou, T., M. Pontil, and T. Poggio. 2000. Regularization networks and support vector machines. Advances in Computational Mathematics 13(1):1-50.
5. Friedrich, R. J. 1982. In defense of multiplicative terms in multiple regression equations. American Journal of Political Science 26(4):797-833.
6. Golub, G. H., M. Heath, and G. Wahba. 1979. Generalized cross-validation as a method for choosing a good ridge parameter. Technometrics 21(2):215-23.
7. Harff, B. 2003. No lessons learned from the Holocaust? Assessing risks of genocide and political mass murder since 1955. American Political Science Review 97(1):57-73.
8. Hastie, T., R. Tibshirani, and J. Friedman. 2009. The elements of statistical learning: Data mining, inference, and prediction. 2nd ed. New York, NY: Springer.
9. Jackson, J. E. 1991. Estimation of models with variable coefficients. Political Analysis 3(1):27-49.
10. Kimeldorf, G., and G. Wahba. 1970. A correspondence between Bayesian estimation on stochastic processes and smoothing by splines. Annals of Mathematical Statistics 41(2):495-502.
11. King, G., and L. Zeng. 2006. The dangers of extreme counterfactuals. Political Analysis 14(2):131-59.
12. Rifkin, R. M., and R. A. Lippert. 2007. Notes on regularized least squares. Technical report, MIT Computer Science and Artificial Intelligence Laboratory.
13. Rifkin, R., G. Yeo, and T. Poggio. 2003. Regularized least-squares classification. Nato Science Series Sub Series III Computer and Systems Sciences 190:131-54.
14. Saunders, C., A. Gammerman, and V. Vovk. 1998. Ridge regression learning algorithm in dual variables. In Proceedings of the 15th International Conference on Machine Learning. Volume 19980, 515-21. San Francisco, CA: Morgan Kaufmann.
15. Schölkopf, B., and A. Smola. 2002. Learning with kernels: Support vector machines, regularization, optimization, and beyond. Cambridge, MA: MIT Press.
16. Tychonoff, A. N. 1963. Solution of incorrectly formulated problems and the regularization method. Doklady Akademii Nauk SSSR 151:501-4. Translated in Soviet Mathematics 4:1035-8.
17. Wood, S. N. 2003. Thin plate regression splines. Journal of the Royal Statistical Society: Series B (Statistical Methodology) 65(1):95-114.