Empirical likelihood for quantile regression
WebJul 23, 2012 · Bayesian inference provides a flexible way of combining data with prior information. However, quantile regression is not equipped with a parametric likelihood, and therefore, Bayesian inference for quantile regression demands careful investigation. This paper considers the Bayesian empirical likelihood approach to quantile … Webused large-sample approximations to the likelihood to do Bayesian quantile regression. Their approach also aims to achieve global efficiency over multiple quantiles, and can adapt to account ... Yang, Y. and He, X. (2012). Bayesian empirical likelihood for quantile regression. Ann. Statist. 40 1102–1131. MR2985945 [17] Yu, K. and Moyeed, R.A ...
Empirical likelihood for quantile regression
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WebEmpirical Likelihood Quantile Regression for Right-Censored Data by Shimeng Huang A thesis presented to the University of Waterloo in fulfillment of the ... Quantile regression is originated byBasset and Koenker(1978). After the first paper based on a location model, the authors further consider a location-scale model and ... WebAbstract. In this paper, we adopt a three-stage estimation procedure and statistical inference methods for quantile regression (QR) based on empirical likelihood (EL) approach with nonignorable dropouts. In the first stage, we consider a parametric model on the dropout propensity of response and handle the parameter identifiability issue by ...
WebJul 23, 2012 · Through empirical likelihood, the proposed method enables us to explore various forms of commonality across quantiles for efficiency gains. By using an MCMC … Because quantile regression does not normally assume a parametric likelihood for the conditional distributions of Y X, the Bayesian methods work with a working likelihood. A convenient choice is the asymmetric Laplacian likelihood, because the mode of the resulting posterior under a flat prior is the usual quantile regression estimates. The posterior inference, however, must be interpreted with care. Yang, Wang and He provided a posterior variance adjustment for valid inference. In a…
WebIn this paper, we focus on the partially linear varying-coefficient quantile regression model with observations missing at random ... In order to do variable selection, we consider penalized empirical likelihood by using smoothly clipped absolute deviationv (SCAD) penalty, and the oracle property of the penalized likelihood estimator of the ... WebDec 1, 2011 · We consider a random effects quantile regression analysis of clustered data and propose a semiparametric approach using empirical likelihood. The random regression coefficients are assumed independent with a common mean, following parametrically specified distributions. The common mean corresponds t …
WebFeb 9, 2006 · This paper considers an empirical likelihood method to estimate the parameters of the quantile regression (QR) models and to construct confidence regions …
WebDec 27, 2016 · Quantitle regression (QR) is a popular approach to estimate functional relations between variables for all portions of a probability distribution. Parameter estimation in QR with missing data is one of the most challenging issues in statistics. Regression quantiles can be substantially biased when observations are subject to missingness. snowman asia tour dvdWebintroduce the Bayesian empirical likelihood approach for quantile regression. We begin with notation and definitions of the underlying models and moment restric-tions. Let D … snowman back plowWebIn the present paper, a novel spatial quantile regression model based on the Birnbaum–Saunders distribution is formulated. This distribution has been widely studied and applied in many fields. To formulate such a spatial model, a parameterization of the multivariate Birnbaum–Saunders distribution, where one of its parameters is … snowman astdWebJul 19, 2016 · The empirical analysis shows that: (i) the economic growth in Shanghai leads to a drop in the carbon allowance prices; (ii) the oil price has a slightly positive effect on the allowance prices regardless of the ordinary least squares (OLS) or quantile regression method; (iii) a long-run negative relationship exists between the coal price and ... snowman aslWebAbstract:This paper develops the Bayesian empirical likelihood (BEL) method and the BEL variable selection for linear regression models with censored data. Empirical likelihood is a multivariate analysis tool that has been widely applied to many fields such as biomedical and social sciences. snowman asl storyWebThe empirical-likelihood method can also be also employed for discrete distributions: ... In quantile estimation, an EL-based categorization procedure helps determine the shape of … snowman background clip artWebSmoothed empirical likelihood inference and variable selection for quantile regression with nonignorable missing response. Ting Zhang and Lei Wang. Computational Statistics & Data Analysis, 2024, vol. 144, issue C . Abstract: With nonignorable missing responses, an efficient estimator and a variable selection method for quantile regression … snowman asmr