Strongly consistent density estimation of the regression residual

2012 ◽  
Vol 82 (11) ◽  
pp. 1923-1929 ◽  
Author(s):  
László Györfi ◽  
Harro Walk
2013 ◽  
Vol 30 (3) ◽  
pp. 606-646 ◽  
Author(s):  
Andriy Norets ◽  
Justinas Pelenis

This paper considers Bayesian nonparametric estimation of conditional densities by countable mixtures of location-scale densities with covariate dependent mixing probabilities. The mixing probabilities are modeled in two ways. First, we consider finite covariate dependent mixture models, in which the mixing probabilities are proportional to a product of a constant and a kernel and a prior on the number of mixture components is specified. Second, we consider kernel stick-breaking processes for modeling the mixing probabilities. We show that the posterior in these two models is weakly and strongly consistent for a large class of data-generating processes. A simulation study conducted in the paper demonstrates that the models can perform well in small samples.


2009 ◽  
Author(s):  
Len Thomas ◽  
Tiago Marques ◽  
David Borchers ◽  
Catriona Harris ◽  
David Moretti ◽  
...  

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