Here is a representation of the precision of a kernel density estimate (second axis) against the true value of the density (first axis), which looks like a lasso of sorts, hence the title. I am not sure this tells much, except that the estimated values are close to the true values and that a given […]
The joint paper, written with Gery Geenens and Davy Paindaveine, entitled “Probit transformation for nonparametric kernel estimation of the copula density” is now online on http://arxiv.org/abs/1404.4414 “Copula modelling has become...
Introduction This is a follow-up post to my recent introduction of histograms. Previously, I presented the conceptual foundations of histograms and used a histogram to approximate the distribution of the “Ozone” data from the built-in data set “airquality” in R. Today, I will examine this distribution in more detail by overlaying the histogram with parametric […]
Update on July 15, 2013: Thanks to Harlan Nelson for noting on AnalyticBridge that the ozone concentrations for both New York and Ozonopolis are non-negative quantities, so their kernel density plot should have non-negative support sets. This has been corrected in this post by - defining new variables called max.ozone and max.ozone2 - using the […]
For the sake of brevity, this post has been created from the first half of a previous long post on kernel density estimation. This first half focuses on the conceptual foundations of kernel density estimation. The second half will focus on constructing kernel density plots and rug plots in R. Introduction Recently, I began a […]
The example is more of a statistical exercise that shows the true significance and the density curve of simulated random normal data. The code can be changed to generate data using either a different mean and standard deviation or a different distribution altogether. This extends the idea of estimating pi by generating random normal data to determine [...]
