Name
Abstract | ||
|---|---|---|
Isotonic regression is a nonparametric approach for fitting monotonic models
to data that has been widely studied from both theoretical and practical
perspectives. However, this approach encounters computational and statistical
overfitting issues in higher dimensions. To address both concerns we present an
algorithm, which we term Isotonic Recursive Partitioning (IRP), for isotonic
regression based on recursively partitioning the covariate space through
solution of progressively smaller "best cut" subproblems. This creates a
regularized sequence of isotonic models of increasing model complexity that
converges to the global isotonic regression solution. The models along the
sequence are often more accurate than the unregularized isotonic regression
model because of the complexity control they offer. We quantify this complexity
control through estimation of degrees of freedom along the path. Success of the
regularized models in prediction and IRP's favorable computational properties
are demonstrated through a series of simulated and real data experiments. We
discuss application of IRP to the genetic problem of modeling gene interactions
and epistasis, where it appears especially promising. |
Year | Venue | Keywords |
|---|---|---|
2011 | Clinical Orthopaedics and Related Research | genetics,recursive partitioning,degree of freedom,isotonic regression |
Field | DocType | Volume |
Applied mathematics,Combinatorics,Isotonic regression,Isotonic,Recursive partitioning,Mathematics | Journal | abs/1102.5 |
Citations | PageRank | References |
1 | 0.36 | 4 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Ronny Luss | 1 | 102 | 10.30 |
| Saharon Rosset | 2 | 1087 | 105.33 |
| Moni Shahar | 3 | 12 | 2.62 |