Technical Terms Kernel Density Estimation (KDE): A nonparametric technique that estimates the probability density function by averaging over localised kernel functions centred at data points.

Kernel density estimation is a nonparametric technique for density estimation in which a known density function (the kernel) is averaged across the observed data points to create a smooth ...

We demonstrate that regularisation by convolution, a smoothing technique based on the convolution of Gaussian kernels with Gaussian mixture models (Molina and Niranjan, 1997), is equivalent to ...

Nonparametric estimation of probability density functions, both marginal and joint densities, is a very useful tool in statistics. The kernel method is popular and applicable to dependent data, ...

In addition, we extend a likelihood cross validation method to the multi-bandwidth kernel density estimation to determined the suboptimum bandwidths of kernels and combining weights. To confirm the ...

In this manuscript we discuss some mathematical details of the ex-Gaussian distribution and apply the ExGUtils package, a set of functions and numerical tools, programmed for python, developed for ...

Gordon Lee et al introduce a data-driven and model-agnostic approach for computing conditional expectations. The new method combines classical techniques with machine learning methods, in particular ...

A third force that is reshaping statistics is the computational revolution, and The Annals will also welcome developments in this area. The purpose of the Institute of Mathematical Statistics (IMS) is ...