Multivariate kernel density estimation

Kernel density estimation is a nonparametric technique for density estimation i.e., estimation of probability density functions, which is one of the…

Papers overview

Semantic Scholar uses AI to extract papers important to this topic.

2013

2012

In this paper we propose a regularized kernel diffusion filter for 3D mesh denoising in the weighted graph framework. The…

2011

2011

This paper presents a new method to accurately characterize and predict the annual variation of wind conditions. Estimation of…

2007

Minimal invasive catheter-guided interventions play an important role in most hospitals all over the world. During the treatment…

2007

This paper applies time-varying autoregressive (TVAR) models with sto hastially evolving parameters to the problem of spee h…

2006

Detection and tracking of foreground objects in a video scene requires a robust technique for background modeling. The modeling…

2003

ABSTRACT In this study, it was obtained kernel estimation of probability density function for the earthquakes data with magnitude…

2000

This paper applies time-varying autoregressive (TVAR) models with sto hastially evolving parameters to the problem of spee h…

1992

In recent years, the focus of study in smoothing parameter selection for kernel density estimation has been on the univariate…