Nonparametric regression methods provide an alternative approach to parametric
estimation that requires only weak identification assumptions and thus minimizes the risk of …

Land use optimization (LUO) first considers which types of land use should exist in a certain
area, and secondly, how to allocate these land use types to specific land grid units. As an …

Background In clinical practice, a plethora of medical examinations are conducted to assess
the state of a patient's pathology producing a variety of clinical data. However, investigation …

Kernel density estimation is a simple and effective method that lies at the heart of many
important machine learning applications. Unfortunately, kernel methods scale poorly for …

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 …

Recently, a number of statistical problems have found an unexpected solution by inspecting
them through a 'modal point of view'. These include classical tasks such as clustering or …

Sparse additive models have been successfully applied to high-dimensional data analysis
due to the flexibility and interpretability of their representation. However, the existing …

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 paper, under the stationary α-mixing dependent samples, we develop a novel
nonlinear modal regression for time series sequences and establish the consistency and …

The Weibull distribution is a versatile probability distribution widely applied in modeling the
failure times of objects or systems. Its behavior is shaped by two essential parameters: the …