In this article we overview nonparametric (spline and kernel) regression methods and illustrate how they may be used in …

This paper introduces a multivariate kernel based forecasting tool for the prediction of variance-covariance matrices …

is considered. A pricing kernel framework is used to model an economy that is segmented into distinct markets, each … and rational pricing kernel models are recovered, reviewed and generalised and single-curve models extended. In another …

kernel hazard regression. We use the introduction of the NDYP program for young unemployed individuals in the UK to estimate …

statistics. We introduce a new method which is based on kernel density estimates. It is a modification of the SEM algorithm … proposed by Gross et al. (2016), which was used for the transformation of totals on rectangular areas to kernel densities … kernel density estimates offers additional useful features to display concentration areas. …

kernel density estimates (KDE) offer advantages over classical choropleth maps. However, kernel density estimation needs … aggregates for kernel density estimation. Their algorithm simulates "exact" geo-coordinates which reflect the information on the … in the vicinity of boundaries. Here we propose a modification of the original algorithm which uses rescaled kernel …

conditional heteroscedasticity (GARCH) formulation. Based on this, VaR is derived by applying kernel density estimation (KDE …

the distributions can be modeled as i.i.d realizations of a measure on the set of distributions, we prove that the kernel …

covariates to be the same in different treatment groups. We also introduce a new balance measure called kernel distance, which is … the empirical estimate of the probability metric defined in the reproducing kernel Hilbert spaces. Compared to the … traditional balance metrics, the kernel distance measures the difference in the two multivariate distributions instead of the …