Bayesian Regularized Quantile Regression Analysis Based on Asymmetric Laplace Distribution
نویسندگان
چکیده
منابع مشابه
Posterior Inference in Bayesian Quantile Regression with Asymmetric Laplace Likelihood
The paper discusses the asymptotic validity of posterior inference of pseudo-Bayesian quantile regression methods with complete or censored data when an asymmetric Laplace likelihood is used. The asymmetric Laplace likelihood has a special place in the Bayesian quantile regression framework because the usual quantile regression estimator can be derived as the maximum likelihood estimator under ...
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In longitudinal studies, measurements of the same individuals are taken repeatedly through time. Often, the primary goal is to characterize the change in response over time and the factors that influence change. Factors can affect not only the location but also more generally the shape of the distribution of the response over time. To make inference about the shape of a population distribution,...
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To make inferences about the shape of a population distribution, the widely popular mean regression model, for example, is inadequate if the distribution is not approximately Gaussian (or symmetric). Compared to conventional mean regression (MR), quantile regression (QR) can characterize the entire conditional distribution of the outcome variable, and is more robust to outliers and misspecifica...
متن کاملDiscussion: Posterior Inference in Bayesian Quantile Regression with Asymmetric Laplace Likelihood
To bake a Bayesian π (posterior) I was taught that you needed an L (likelihood) and a p (prior) – Oh yeah, and probably some data, don’t forget the data! So it comes as something of a shock to discover that there are 5,240 web documents employing the phrase “Bayesian quantile regression,” as of September 1, 2015, according to Google. Quantile regression would seem to be the very antithesis of a...
متن کاملDiscussion: Posterior inference in Bayesian quantile regression with asymmetric Laplace likelihood
We begin by congratulating Professors Yang, Wang & He (hereafter YW&H) for a top-notch contribution to the literature, and thanking Professor Hallin for giving us the opportunity to discuss their work. We found it quite satisfying to discover that the MCMC algorithms over which we have toiled can be used to make valid asymptotic inference! In this discussion, we focus on MCMC algorithms for exp...
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ژورنال
عنوان ژورنال: Journal of Applied Mathematics and Physics
سال: 2020
ISSN: 2327-4352,2327-4379
DOI: 10.4236/jamp.2020.81006