Nonparametric methods form an important core of statistical techniques and are typically used when data do not meet parametric assumptions. Understanding the foundation of these methods, as well as ...

Kernel density estimation (KDE) and nonparametric methods form a cornerstone of contemporary statistical analysis. Unlike parametric approaches that assume a specific functional form for the ...

Jeffrey S. Morris, Naisyin Wang, Joanne R. Lupton, Robert S. Chapkin, Nancy D. Turner, Mee Young Hong and Raymond J. Carroll An important problem in studying the etiology of colon cancer is ...

We introduce a non-parametric estimator of the diffusion coefficient of a diffusion process using a projection method on a wavelets orthonormal basis of L2( R). The sample path is observed at discrete ...

A research team has developed a new hybrid artificial intelligence framework that can accurately estimate leaf nitrogen ...