Adaptive Bayesian Density Estimation in Lp-metrics with Pitman-Yor or Normalized Inverse-Gaussian Process Kernel Mixtures

[摘要] We consider Bayesian nonparametric density estimation using a Pitman-Yor or a normalized inverse-Gaussian process convolution kernel mixture as the prior distribution for a density. The procedure is studied from a frequentist perspective. Using the stick-

[学科分类] 统计和概率

[关键词] adaptation;nonparametric density estimation;normalized inverseGaussian process;Pitman-Yor process;posterior contraction rate;sinc kernel