| || |
Submitted by pscully on Thu, 10/16/2008 - 16:50.
10/23/2008 - 16:10
10/23/2008 - 17:30
STA/BST 290: Chin-Shang Li
Testing for Linearity of the Nonparametric Component of a Partially Linear Model
THURSDAY, October 23rd, 2008 at 4.10pm, MSB 1147 (Colloquium Room)
Refreshments: 3.30pm, MSB 4110 (Statistics Lounge)
Speaker: Chin-Shang Li (Dept of Public Health Sciences, UC Davis)
Title: Testing for Linearity of the Nonparametric Component of a Partially Linear Model
Abstract: The nonparametric component in a partially linear model is estimated by a linear combination of fixed-knot cubic B-splines with a second-order difference penalty on the adjacent B-spline coefficients. The resulting penalized least-squares estimator is used to construct two Wald-type spline-based test statistics for the null hypothesis of the linearity of the nonparametric function. When the number of knots is fixed, the limiting distribution of the first test statistic is the distribution of a linear combination of independent chi-squared random variables, each with one degree of freedom, under the null hypothesis. The smoothing parameter is determined by specifying a value for the asymptotically expected value of the test statistic under the null hypothesis. When the number of knots is fixed and under the null hypothesis, the second test statistic asymptotically has a chi-squared distribution with K = q + 2 degrees of freedom, where q is the number of knots used for estimation. The power performances of the two proposed tests are investigated via simulation experiments, and the practicality of the proposed methodology is illustrated using a real-life data set.