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- W4380353709 abstract "Data transformations are essential for broad applicability of parametric regression models. However, for Bayesian analysis, joint inference of the transformation and model parameters typically involves restrictive parametric transformations or nonparametric representations that are computationally inefficient and cumbersome for implementation and theoretical analysis, which limits their usability in practice. This paper introduces a simple, general, and efficient strategy for joint posterior inference of an unknown transformation and all regression model parameters. The proposed approach directly targets the posterior distribution of the transformation by linking it with the marginal distributions of the independent and dependent variables, and then deploys a Bayesian nonparametric model via the Bayesian bootstrap. Crucially, this approach delivers (1) joint posterior consistency under general conditions, including multiple model misspecifications, and (2) efficient Monte Carlo (not Markov chain Monte Carlo) inference for the transformation and all parameters for important special cases. These tools apply across a variety of data domains, including real-valued, integer-valued, compactly-supported, and positive data. Simulation studies and an empirical application demonstrate the effectiveness and efficiency of this strategy for semiparametric Bayesian analysis with linear models, quantile regression, and Gaussian processes." @default.
- W4380353709 created "2023-06-13" @default.
- W4380353709 creator A5014210568 @default.
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- W4380353709 date "2023-06-08" @default.
- W4380353709 modified "2023-10-16" @default.
- W4380353709 title "Monte Carlo inference for semiparametric Bayesian regression" @default.
- W4380353709 doi "https://doi.org/10.48550/arxiv.2306.05498" @default.
- W4380353709 hasPublicationYear "2023" @default.
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