Residual Ratio Thresholding for Linear Model Order Selection

Thresholding-based Iterative Selection Procedures for Model Selection and Shrinkage

This paper discusses a class of thresholding-based iterative selection procedures (TISP) for model selection and shrinkage. People have long before noticed the weakness of the convex l1-constraint (or the softthresholding) in wavelets and have designed many different forms of nonconvex penalties to increase model sparsity and accuracy. But for a nonorthogonal regression matrix, there is great d...

Thresholding Estimators for Linear

2 Denoising via Thresholding and Model Selection

In the context of wavelet denoising and compression, we study minimum description length (MDL) criteria for model selection criteria as exible forms of thresholding. Mixture MDL methods based on a single Laplacian, a two-piece Laplacian, and a generalized Gaussian prior are shown to be adaptive thresholding rules. While achieving mean squared error performance comparable with other popular thre...

Projection Method with Residual Selection for Linear Feasibility Problems

We propose a new projection method for linear feasibility problems. The method is based on the so called residual selection model. We present numerical results for some test problems.

Portmanteau Likelihood Ratio Tests for Model Selection

This paper provides an extension of Vuong’s (1989, Econometrica, 57, pp.307–333) model selection test to the multivariate case. We use the Kullback–Leibler Information Criterion (KLIC) to measure the closeness of a model to the truth to provide a diagnosis of many competing models where the models are not correctly specified. After investigating the asymptotic joint distribution of the likeliho...