Concept
Kernel smoother
Publications associées (41)
Robust Data-Driven Dynamic Programming
Daniel Kuhn, Grani Adiwena Hanasusanto
In stochastic optimal control the distribution of the exogenous noise is typically unknown and must be inferred from limited data before dynamic programming (DP)-based solution schemes can be applied. If the conditional expectations in the DP recursions ar ...
2013Methodology to account for uncertainties and tradeoffs in pharmaceutical environmental hazard assessment
David Andrew Barry, Luca Rossi, Sylvain Coutu
Many pharmaceutical products find their way into receiving waters, giving rise to concerns regarding their environmental impact. A procedure was proposed that enables ranking of the hazard to aquatic species and human health due to such products. In the pr ...
Elsevier2012Kernel regression for real-time building energy analysis
This study proposes a new technique for real-time building energy modelling and event detection using kernel regression. We show that this technique can exceed the performance of conventional neural network algorithms, and do so by a large margin when the ...
Taylor & Francis Ltd2012Nonparametric Construction of Multivariate Kernels
We propose a nonparametric method for constructing multivariate kernels tuned to the configuration of the sample, for density estimation in R-d, d moderate. The motivation behind the approach is to break down the construction of the kernel into two parts: ...
American Statistical Association2012Integration of Urban Structures in Point Process Analysis
Traditionally, spatial analysis of point pattern has been mostly focused on Euclidean space. As many human related phenomena take place on a network, the assumption of a continuous isotropic space fails to describe events which actually occur on a one-dime ...
2011Convolution on the n-Sphere With Application to PDF Modeling
In this paper, we derive an explicit form of the convolution theorem for functions on an n-sphere. Our motivation comes from the design of a probability density estimator for n-dimensional random vectors. We propose a probability density function (pdf) est ...
Institute of Electrical and Electronics Engineers2010