multivariate quantile regressionmultiresponse linear modelsequivarianceloss functionasymptotic normalityblood pressuressales performancesTo detect the dependence on the covariates in the lower and upper tails of the response distribution, regression quantiles are very useful tools in linear model problems with ...
quantilesdo not sufficiently control the probability content, while others do not always accurately reflect the concentration of probability mass. We suggest superlevel sets of conditional multivariate densities as an alternative to current multivariate quantile definitions. Hence, the superlevel set is a ...
QUANTILEregressionBAHADURrepresentationGAUSSIANprocessesWe focus on the construction of confidence corridors for multivariate nonparametric generalized quantile regression functions. This construction is based on asymptotic results for the maximal deviation between a suitable nonparametric estimator and the true ...
Quantile treatment effectSmoothing and nonparametric regressionWe focus on the construction of confidence corridors for multivariate nonparametric generalized quantile regression functions. This construction is based on asymptotic results for the maximal deviation between a suitable nonparametric estimator and the ...
Quantile regressionTobit modelsZero-inflationQuantile regression is a powerful tool for modeling non-Gaussian data, and also for modeling different quantiles of the probability distributions of the responses. We propose a Bayesian approach of estimating the quantiles of multivariate longitudinal data where ...
We propose a M-quantile regression model for the analysis of multivariate, continuous, longitudinal data. M-quantile regression represents an appealing alternative to standard regression models, as it combines the robustness of quantile and the efficiency of expectile regression, providing a complete ...
> Multivariate Regression:多元回归 下载文档 收藏 打印 转格式 25阅读文档大小:183.46K7页浙大文库上传于2014-10-27格式:PDF Multivariate Regression The so-called superwised learning problem is the following: we want to ap- proximate the random variable Y with an appropriate function of the random vari...
Segmented model selection in quantile regression using the minimum description length principle. J. Am. Stat. Assoc. To appear; 10.1080/01621459.2014.889022 (2014). Tibshirani, R. Regression shrinkage and selection via the lasso. J. R. Stat. Soc. Series B Stat. Methodol. 58, 267–288, ...
In a time series context, quantile regression approaches need to be adapted to account for the dependence induced by the time-ordering of the data. A natural extension is the quantile autoregressive approach developed by Koenker and Xiao (Citation2006) and, as pointed out above, one of the mos...
We consider a system of nonseparable structural equations with infinite dimensional unobservables, and employ quantile regression methods because they allow us to utilize the entire distribution of the data. Difficulties arise because the restriction involves several equations, while the quantile is a ...