The views expressed here are those of the individual authors and not necessarily those of the JSM sponsors, their officers, or their staff.
Abstract Details
|
Activity Number:
|
410
|
|
Type:
|
Contributed
|
|
Date/Time:
|
Tuesday, August 2, 2011 : 2:00 PM to 3:50 PM
|
|
Sponsor:
|
Section on Nonparametric Statistics
|
| Abstract - #302119 |
|
Title:
|
Smoothing Mechanism of Cyclic Cubic Regression Splines Smoothing
|
|
Author(s):
|
Mihoko Minami*+
|
|
Companies:
|
Keio University
|
|
Address:
|
3-14-1 Hiyoshi Kohoku-ku, Yokohama , International, 223-8522, JAPAN
|
|
Keywords:
|
Hat matrix ;
eigen decomposition ;
penalized likelihood ;
periodic variation ;
harmonic functions
|
|
Abstract:
|
Cyclic cubic regression spline smoothing is a method to estimate s periodic smooth regression function such as daily or annual pattern of temperature. For a given set of knots, a cyclic cubic spline function is a piecewise cubic function continuous up to second derivatives at the knots, and the function values and derivatives up to the second order at the both endpoints are equal. When the knots are equally spaced, the vector of function values holds an eqation with the vector f second derivatives and cyclic band matrices. From this equation, it is shown that the eigen values and vectors of the hat matrix for cyclic cubic regression spline smoothing have an interesting structure and it characterizes smoothing mechanism of cyclic cubic regression spline smoothing.
|
The address information is for the authors that have a + after their name.
Authors who are presenting talks have a * after their name.
Back to the full JSM 2011 program
|
2011 JSM Online Program Home
For information, contact jsm@amstat.org or phone (888) 231-3473.
If you have questions about the Continuing Education program, please contact the Education Department.