| Other Titles |
|
| Creator |
|
|
|
|
|
| Language |
|
| Publisher |
|
| Date |
|
| Source Title |
|
| Vol |
|
| Issue |
|
| First Page |
|
| Last Page |
|
| Publication Type |
|
| Access Rights |
|
| Related DOI |
|
| Related DOI |
|
| Related DOI |
|
|
|
| Related URI |
|
|
|
| Related URI |
|
| Related HDL |
|
| Relation |
|
|
|
|
|
| Abstract |
We propose a nonlinear regression model that uses basis expansion for the case where the underlying regression function has inhomogeneous smoothness. In this case, conventional nonlinear regression mo...dels tend to over- or underfit where the function is smoother or less smooth, respectively. We begin by roughly approximating the underlying regression function with a locally linear function. We then extend the fused lasso signal approximator and thereby develop a fast and efficient algorithm. We next use the residuals between the locally linear functions and the data to adaptively prepare the basis functions. Finally, using a regularization method, we construct a nonlinear regression model with these basis functions. To select the optimal value of the tuning parameter for the regularization method, we provide an explicit form of the generalized information criterion. The validity of our proposed method is then demonstrated through several numerical examples.show more
|
Export Mendeley
MI2012-11