Data Adaptive Ridging in Local Polynomial Regression

By B. Seifert, Theo Gasser. 2000

71 citations recorded by the source corpus.

Subfields: Management Science and Operations Research, Statistics and Probability

View Paper: https://www.semanticscholar.org/paper/15ff33e9c4746b4514a9a346f53ab730f5f31a65

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In Management Science and Operations Research

In Statistics and Probability

Citations in the corpus

Propensity Score Matching without Conditional Independence Assumption - With an Application to the Gender Wage Gap in the United Kingdom (2007, unknown): “The ridge parameter r is set to 5/16 for the Epanechnikov and 0.3535 for the Gaussian kernel, according to the rule-of-thumb of Seifert and Gasser (2000).”
The performance of estimators based on the propensity score (2013, background): “2 General structure of the estimators considered As discussed by Smith and Todd (2005), Busso, DiNardo and McCrary (2009a) and Angrist and Pischke (2009) among many others, all estimators adjusting for covariates can be”
Selecting the amount of smoothing in nonparametric regression estimation for complex surveys (2005, methodology): “[9], but we do not explore this topic further here.”
Programme Evaluation with Multiple Treatments (2004, methodology): “Ridge regression was proposed by Seifert and Gasser (1996, 2000), among others, to overcome the unbounded variance problem of local linear regression.27 The local polynomial matching estimators require the choice of a bandwidth parameter h, which governs the size of the local smoothing…”
Diagnostic Tools and a Remedial Method for Collinearity in Geographically Weighted Regression (2007, unknown): “Another noticeable difference in the two tables is the large increase in VIF values, from 1.33 to a mean of 3.04 with GWR. Figure 2 displays the distribution of VIFs for the GWR model and shows that there are three local models with large VIFs over the conservative threshold value of 10, where VIFs ”
Using Matching to Estimate Treatment Effects: Data Requirements, Matching Metrics, and Monte Carlo Evidence (2004, background): “He finds that the local linear estimator suggested by Seifert and Gasser (1996, 2000) is better than matching estimators.”
Matching and semi-parametric IV estimation, a distance-based measure of migration, and the wages of young men (2011, methodology): “Again, K is a kernel weighting function, h is a bandwidth and r is the ridge parameter.21 We follow the rule of thumb from Seifert and Gasser (2000) and use a Gaussian kernel with r set equal to 1 24 2 0.35u du .”