ArticleComputational statistics & data analysis2019
Modelling and estimation of nonlinear quantile regression with clustered data.
Article in Computational statistics & data analysis, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
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Who cites it
9 citing papers in PubMed.
- Valid Standard Errors for Bayesian Quantile Regression With Clustered and Independent Data.Journal of educational and behavioral statistics : a quarterly publication sponsored by the American Educational Research Association and the American Statistical Association · 2026Article
- Quantile regression analysis of the heterogeneous association between digital cultural engagement and health literacy: evidence from rural China.Frontiers in public health · 2026Article
- Divergent Pattern of Development in Rats and Humans.Neurotoxicity research · 2023Article
- Quantile hidden semi-Markov models for multivariate time series.Statistics and computing · 2022Article
- Modified check loss for efficient estimation via model selection in quantile regression.Journal of applied statistics · 2021Article
- Asymmetric dependence between stock market returns and news during COVID-19 financial turmoil.Finance research letters · 2020Article
- Article
- Quantile Regression Modeling of Latent Trajectory Features with Longitudinal Data.Journal of applied statistics · 2019Article
- Associations of sex hormone levels with body mass index (BMI) in men: a cross-sectional study using quantile regression analysis.Asian journal of andrologyArticle
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1 author.
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Abstract
In regression applications, the presence of nonlinearity and correlation among observations offer computational challenges not only in traditional settings such as least squares regression, but also (and especially) when the objective function is nonsmooth as in the case of quantile regression. Methods are developed for the modelling and estimation of nonlinear conditional quantile functions when data are clustered within two-level nested designs. The proposed estimation algorithm is a blend of a smoothing algorithm for quantile regression and a second order Laplacian approximation for nonlinear mixed models. This optimization approach has the appealing advantage of reducing the original nonsmooth problem to an approximated
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