2024 Empirical likelihood for quantile regression

Empirical likelihood for quantile regression

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August undefined, 2024

WebApr 14, 2024 · The relationship between financialization and innovation has become a common focus of academic attention. This paper analyzes the influence of corporate … WebIn this paper, we discuss the quantile regression and variable selection of partially linear single-index model when data are missing at random, which allows the response and covariates missing simultaneously. By using iteration algorithm and local linear method, we construct the inverse probability weighted quantile estimators of both the ...

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WebThe empirical-likelihood method can also be also employed for discrete distributions: ... In quantile estimation, an EL-based categorization procedure helps determine the shape of … WebAbstract:This paper propose a direct generalization quantile regression estimation method (DGQR estimation) for quantile regression with varying-coefficient models with interval censored data, which is a direct generalization for complete observed data. ... Next One: Bayesian empirical likelihood and variable selbeection for censored linear ... rrtp st cloud

Smoothed Empirical Likelihood Methods for Quantile …

WebSmoothed 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 … WebApr 6, 2024 · This paper studies quantile linear regression models with response data missing at random. A quantile empirical-likelihood-based method is proposed firstly to study a quantile linear regression ... WebThis paper considers an empirical likelihood method to estimate the parameters of the quantile regression (QR) models and to construct con ﬁdence regions that are … rrtpo safety plan

长春工业大学教师个人主页 Li Chunjing--Home-- Bayesian empirical likelihood …

[1207.5378] Bayesian empirical likelihood for quantile …

WebJul 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 ... WebJun 9, 2024 · Tang CY, Leng C. Empirical likelihood and quantile regression in longitudinal data analysis. Biomerika. 2011;98:1001–6. Article Google Scholar Fu L, Wang YG. Quantile regression for longitudinal ... rrtp west lothianWebOct 15, 2001 · The paper introduces the idea of Bayesian quantile regression employing a likelihood function that is based on the asymmetric Laplace distribution. It is shown that irrespective of the original distribution of the data, the use of the asymmetric Laplace distribution is a very natural and effective way for modelling Bayesian quantile regression. rrtp substance abuse

"Webrameters in mean regression model with missing covariates under NMAR assumption (3). They showed that the empirical likelihood estimator can improve estimation e ciency if P( = 1jY;Z) is correctly speci ed. In this paper, we put forward an empirical likelihood-based weighted (ELW) estimator for estimating quantile regression model with nonignor- " - Empirical likelihood for quantile regression

Empirical likelihood for quantile regression

Smoothed Empirical Likelihood Methods for Quantile Regression M…

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 …

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WebMar 6, 2024 · In this paper, we present three empirical likelihood (EL)-based inference procedures to construct confidence intervals for quan-tile regression models with longitudinal data. WebJul 16, 2014 · Hence, this paper provides a positive result of the composite quantile regression modeling, and extends the application literature of the empirical likelihood …

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. WebAbstract: Quantile regression is a useful approach to modeling various aspects of conditional distributions. The Bayesian approach provides a natural framework for incorporating spatial correlation in a quantile regression model. This paper con siders Bayesian spatial quantile regression using empirical likelihood as a working likelihood.

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 ... 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 ...

WebMar 15, 2024 · In quantile regression for longitudinal data, kink point where the kink effect happens is often assumed to be heterogeneous across different quantiles. However, the kink point tends to be the same across different quantiles, especially in a region of neighboring quantile levels. ... Tang, C. Y., Leng, C.: Empirical likelihood and quantile ...

WebJan 1, 2012 · 4. Discussion. We have proposed a new quantile regression estimator which utilizes auxiliary information via the empirical likelihood method. We have shown … rrtp vha directiveWebThrough empirical likelihood, the proposed method enables us to explore various forms of commonality across quantiles for efficiency gains. By using an MCMC algorithm in the … rrtr25-xl inaWebMay 1, 2024 · Rao and Wu (2010) apply Bayesian empirical likelihood to survey sampling; Chaudhuri and Ghosh (2011) to small area estimation; Yang and He (2012) to quantile regression; Mengersen et al. (2013) to approximate Bayesian computation; and Chib et al. (2024) to handle moment condition models, where they use the exponentially tilted … rrtrack.comWebJul 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 … rrts cbpWebintroduce the Bayesian empirical likelihood approach for quantile regression. We begin with notation and deﬁnitions of the underlying models and moment restric-tions. Let D ={(Xi,Yi),i = 1,...,n} be a random sample from the following quantile regression model: (2.1) Qτ(Y X)=X β0(τ), where X ∈Rp+1 is composed of an intercept term and p ... rrts alwar mapWebWe now demonstrate how (1) can be used as auxiliary information to improve the quantile regression. Applying empirical likelihood (Qin & Lawless, 1994; Owen, 2001) leads … rrtp white cityWebA variation of this approach is the penalized ETEL appearing in, for example, Tang and Yang (2024), in the context of quantile regression. The PETEL posterior combines the … rrts ceo