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Naive Penalized Spline Derivative Estimators Achieve Optimal Rates of Convergence

Naive Penalized Spline Derivative Estimators Achieve Optimal Rates of Convergence

Event Category:

Statistics and Data Science Seminar Series

Speaker:
Bright Antwi Boasiako

Institution:
UMass Amherst

While it is common to study mean regression functions in statistical learning, there are many cases in economics, ecology, and nonparametric regression where it is of interest to estimate derivatives of the mean regression function with minimal assumptions on the functional form of the mean function. In this talk, I will discuss some theoretical findings for derivative estimation using nonparametric methods. In particular, we show that simply differentiating a penalized spline estimator of the mean regression function to estimate the corresponding derivative, achieves the optimal L2 rate of convergence.

Friday, October 7, 2022 - 11:00am

LGRT 171

## Department of Mathematics and Statistics