Asymptotic optimality and efficient computation of the leave-subject-out cross-validation

By Ganggang Xu, Jianhua Z. Huang. 2012

55 citations recorded by the source corpus.

Although the leave-subject-out cross-validation (CV) has been widely used in practice for tuning parameter selection for various nonparametric and semiparametric models of longitudinal data, its theoretical property is unknown and solving the associated optimization problem is computationally expensive, especially when there are multiple tuning parameters. In this paper, by focusing on the penalized spline method, we show that the leave-subject-out CV is optimal in the sense that it is asymptotically equivalent to the empirical squared error loss function minimization. An efficient Newton-type algorithm is developed to compute the penalty parameters that optimize the CV criterion. Simulated and real data are used to demonstrate the effectiveness of the leave-subject-out CV in selecting both the penalty parameters and the working correlation matrix.

Subfields: Artificial Intelligence, Statistics and Probability

View Paper: https://www.semanticscholar.org/paper/aee53989b418bae58bde74dbfe799e8dcbe51bad

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Artificial Intelligence

1. Xinyu Zhang
2. Yan Gao
3. Guohua Zou
4. Shouyang Wang

In Statistics and Probability

1. Guohua Zou
2. Xinyu Zhang
3. Yan Gao
4. Shouyang Wang
5. J. R. Magnus
6. Giuseppe De Luca
7. Hua Liang
8. Alan T. K. Wan

Citations in the corpus

• Model averaging based on leave-subject-out cross-validation (2016, methodology): “We estimateβ byminimizing the following penalizedweighted least squares (Xu and Huang, 2012)”