Bayesian Semiparametric Density Deconvolution in the Presence of Conditionally Heteroscedastic Measurement Errors

• Published in: Journal of computational and graphical statistics Vol. 23; no. 4; pp. 1101 - 1125
• Main Authors: Sarkar, Abhra, Mallick, Bani K., Staudenmayer, John, Pati, Debdeep, Carroll, Raymond J.
• Format: Journal Article
• Language: English
• Published: United States Taylor & Francis 02.10.2014; American Statistical Association, Institute of Mathematical Statistics, and Interface Foundation of North America; Taylor & Francis Ltd
• Subjects: B-spline; Bayesian analysis; Bayesian Methods; Conditional heteroscedasticity; Density; Density estimation; Dirichlet problem; Dirichlet process mixture models; Epidemiology; Error rates; Estimating techniques; Estimation methods; Mathematical models; Measurement errors; Nonparametric models; Parametric models; Random variables; Sample size; Simulations; Skew-normal distribution; Statistical variance; Studies; Variance function; Measurement errors; Variance function; Dirichlet process mixture models; Skew-normal distribution; B-spline; Density deconvolution; Conditional heteroscedasticity

We consider the problem of estimating the density of a random variable when precise measurements on the variable are not available, but replicated proxies contaminated with measurement error are available for sufficiently many subjects. Under the assumption of additive measurement errors this reduce...