We review recent advances in modal regression studies using kernel density estimation.
Modal regression is an alternative approach for investigating the relationship between a …

This paper studies the nonparametric modal regression problem systematically from a
statistical learning viewpoint. Originally motivated by pursuing a theoretical understanding of …

Modal regression is a good alternative of the mean regression and likelihood based
methods, because of its robustness and high efficiency. A robust communication-efficient …

In this paper, we consider estimation of the conditional mode of an outcome variable given
regressors. To this end, we propose and analyze a computationally scalable estimator …

We consider modal linear regression models when neither the response variable nor the
covariates can be directly observed, but are measured with multiplicative distortion …

Most research on panel data focuses on mean or quantile regression, while there is not
much research about regression methods based on the mode. In this paper, we propose a …

In this article, we propose a novel nonparametric statistical learning tool based on modal
regression, which can complement the standard mean and quantile regression and has …

Representation-based classification (RC) methods, such as sparse RC, have shown great
potential in face recognition (FR) in recent years. Most previous RC methods are based on …

In this paper, we propose a simple parametric modal linear regression model where the
response variable is gamma distributed using a new parameterization of this distribution that …

Greedy algorithm (GA) is an efficient sparse representation framework with numerous
applications in machine learning and computer vision. However, conventional GA methods …