Artículos de revistas
A method for choosing the smoothing parameter in a semi-parametric model for detecting change-points in blood flow
Registro en:
Journal Of Applied Statistics. Taylor & Francis Ltd, v. 41, n. 1, n. 26, n. 45, 2014.
0266-4763
1360-0532
WOS:000327237100003
10.1080/02664763.2013.830085
Autor
Han, SW
Mesquita, RC
Busch, TM
Putt, ME
Institución
Resumen
In a smoothing spline model with unknown change-points, the choice of the smoothing parameter strongly influences the estimation of the change-point locations and the function at the change-points. In a tumor biology example, where change-points in blood flow in response to treatment were of interest, choosing the smoothing parameter based on minimizing generalized cross-validation (GCV) gave unsatisfactory estimates of the change-points. We propose a new method, aGCV, that re-weights the residual sum of squares and generalized degrees of freedom terms from GCV. The weight is chosen to maximize the decrease in the generalized degrees of freedom as a function of the weight value, while simultaneously minimizing aGCV as a function of the smoothing parameter and the change-points. Compared with GCV, simulation studies suggest that the aGCV method yields improved estimates of the change-point and the value of the function at the change-point. 41 1 26 45 NIH-NCI [5-P01-CA-087971, R01 CA85831, CA087971-S1, CA129554] NIH-NCI [5-P01-CA-087971, R01 CA85831, CA087971-S1, CA129554]