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

Bayesian Analysis

Catia Scricciolo¹ 

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

DOI  :  10.1214/14-BA863

学科分类：统计和概率

来源: Institute of Mathematical Statistics

【 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-

【 授权许可】

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