Oregon State University

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Department of Statistics Research Seminar

Variable Selection in semi-parametric regression models

Monday, April 22, 2013 3:55 PM - 5:00 PM

Shuping Jiang, Department of Statistics, Oregon State University

We first considered stochastic additive models (SAM) for nonlinear time series data. We propose a penalized polynomial spline method for estimation and lag selection in SAM. It approximates the nonparametric functions by polynomial splines and performs variable/lag selection by imposing a penalty on the empirical L2 norm of the spline functions.

Under geometrically ?-mixing, we establish that the resulting estimator enjoys the optimal rate of convergence for estimating the nonparametric functions. It also selects the correct model with probability approaching to one as the sample size increases.

We extend the local linear approximation (LLA) algorithm of Zou and Li (2008) to solve the penalized polynomial spline problem. A coordinate-wise algorithm is developed for finding the solution.

Extensive Monte Carlo studies have been conducted and show that the proposed procedure works effectively even with moderate sample size. We also illustrate the proposed method by analyzing the US employment time series.

The proposed method is also applied to the additive coefficient models (ACM). This focus is to develop the asymptotic properties of the global solution of the non-convex objective function. Relevant research is in progress.


Milam Hall (campus map)
019
Free
Judith Burks
1 541 737 33611
judy.burks at oregonstate.edu
College of Science, Statistics Department
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