Sparse vector heterogeneous autoregressive model with nonconvex penalties
نویسندگان
چکیده
منابع مشابه
SparseNet: Coordinate Descent With Nonconvex Penalties.
We address the problem of sparse selection in linear models. A number of nonconvex penalties have been proposed in the literature for this purpose, along with a variety of convex-relaxation algorithms for finding good solutions. In this article we pursue a coordinate-descent approach for optimization, and study its convergence properties. We characterize the properties of penalties suitable for...
متن کاملSparse seasonal and periodic vector autoregressive modeling
Seasonal and periodic vector autoregressions are two common approaches to modeling vector time series exhibiting cyclical variations. The total number of parameters in these models increases rapidly with the dimension and order of the model, making it difficult to interpret the model and questioning the stability of the parameter estimates. To address these and other issues, two methodologies f...
متن کاملCompressed Sensing Recovery via Nonconvex Shrinkage Penalties
The ` minimization of compressed sensing is often relaxed to `, which yields easy computation using the shrinkage mapping known as soft thresholding, and can be shown to recover the original solution under certain hypotheses. Recent work has derived a general class of shrinkages and associated nonconvex penalties that better approximate the original ` penalty and empirically can recover the ori...
متن کاملConvolutional Sparse Representations with Gradient Penalties
While convolutional sparse representations enjoy a number of useful properties, they have received limited attention for image reconstruction problems. The present paper compares the performance of block-based and convolutional sparse representations in the removal of Gaussian white noise. While the usual formulation of the convolutional sparse coding problem is slightly inferior to the block-b...
متن کاملSparse Density Estimation with l1 Penalties
This paper studies oracle properties of `1-penalized estimators of a probability density. We show that the penalized least squares estimator satisfies sparsity oracle inequalities, i.e., bounds in terms of the number of non-zero components of the oracle vector. The results are valid even when the dimension of the model is (much) larger than the sample size. They are applied to estimation in spa...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
ژورنال
عنوان ژورنال: Communications for Statistical Applications and Methods
سال: 2022
ISSN: ['2287-7843', '2383-4757']
DOI: https://doi.org/10.29220/csam.2022.29.1.053