A Survey of Nonparametric Mixing Density Estimation via the Predictive Recursion Algorithm
Ryan Martin ()
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Ryan Martin: North Carolina State University
Sankhya B: The Indian Journal of Statistics, 2021, vol. 83, issue 1, No 5, 97-121
Abstract Nonparametric estimation of a mixing density based on observations from the corresponding mixture is a challenging statistical problem. This paper surveys the literature on a fast, recursive estimator based on the predictive recursion algorithm. After introducing the algorithm and giving a few examples, I summarize the available asymptotic convergence theory, describe an important semiparametric extension, and highlight two interesting applications. I conclude with a discussion of several recent developments in this area and some open problems.
Keywords: Empirical Bayes; high-dimensional inference; Jayanta K. Ghosh; mixture model; recursive estimation.; Primary 62G07; Secondary 62C12 (search for similar items in EconPapers)
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