This paper investigates variable selection in the spatial autoregressive model with
independent and identical distributed errors. A penalized quasi-maximum likelihood method …

Purpose This study aims to validate a direct method to measure relational capital through
the estimation of corporate brands. Considering the influence of relational capital …

Variable selection in linear models is essential for improved inference and interpretation, an
activity which has become even more critical for high dimensional data. In this article, we …

Sparse approximation arises in many applications and often leads to a constrained or
penalized ℓ 0 minimization problem, which was proved to be NP-hard. This paper proposes …

Lung cancer, with its most prevalent form non-small-cell lung carcinoma (NSCLC), is one of
the leading causes of cancer-related deaths worldwide, and is commonly treated with …

We introduce an efficient iterative algorithm, intended for various least squares problems,
based on a design of experiments perspective. The algorithm, called orthogonalizing EM …

Background Statistical boosting is a computational approach to select and estimate
interpretable prediction models for high-dimensional biomedical data, leading to implicit …

In this paper, the properties of 10 different feature selection algorithms for generalized
additive models (GAMs) are compared on one simulated and two real-world datasets under …

Motivation: Feature selection, identifying a subset of variables that are relevant for predicting
a response, is an important and challenging component of many methods in statistics and …

We show that for a class of penalty functions, finding the global optimizer in the penalized
least-squares estimation is equivalent to the 'exact cover by 3-sets' problem, which belongs …