Approximate integrated likelihood via ABC methods

DOI10.4310/SII.2015.v8.n2.a4zbMath1405.62029arXiv1403.0387MaRDI QIDQ1747443

Clara Grazian, Brunero Liseo

Publication date: 8 May 2018

Published in: Statistics and Its Interface (Search for Journal in Brave)

Full work available at URL: https://arxiv.org/abs/1403.0387

zbMATH Keywords

Monte Carlo methodsnuisance parameterssemiparametric regressionquantile estimationprofile likelihoodNeyman and Scott problem

Mathematics Subject Classification ID

Nonparametric regression and quantile regression (62G08) Software, source code, etc. for problems pertaining to statistics (62-04) Bayesian inference (62F15)

Related Items (4)

An approximate likelihood perspective on ABC methods ⋮ Extending approximate Bayesian computation methods to high dimensions via a Gaussian copula model ⋮ Approximate maximum likelihood estimation using data-cloning ABC ⋮ Statistical and computational tradeoff in genetic algorithm-based estimation

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