• Corpus ID: 88516114

An Alternative Approach to Functional Linear Partial Quantile Regression

  title={An Alternative Approach to Functional Linear Partial Quantile Regression},
  author={Dengdeng Yu and Linglong Kong and Ivan Mizera},
  journal={arXiv: Statistics Theory},
We have previously proposed the partial quantile regression (PQR) prediction procedure for functional linear model by using partial quantile covariance techniques and developed the simple partial quantile regression (SIMPQR) algorithm to efficiently extract PQR basis for estimating functional coefficients. However, although the PQR approach is considered as an attractive alternative to projections onto the principal component basis, there are certain limitations to uncovering the corresponding… 

Figures from this paper

Specification Testing in Functional Quantile Regression Models with an Application to Income Differences in Germany
We propose a novel consistent specification test for quantile regression models where we allow the covariate effects to be quantile dependent and nonlinear. To achieve this, we parameterize the


Partial functional linear quantile regression for neuroimaging data analysis
Quantile Correlations and Quantile Autoregressive Modeling
In this article, we propose two important measures, quantile correlation (QCOR) and quantile partial correlation (QPCOR). We then apply them to quantile autoregressive (QAR) models, and introduce two
Partial functional linear quantile regression
This paper studies estimation in partial functional linear quantile regression in which the dependent variable is related to both a vector of finite length and a function-valued random variable as
Variable Screening via Quantile Partial Correlation
The proposed method can successfully select predictors when the variables are highly correlated, and it can also identify variables that make a contribution to the conditional quantiles but are marginally uncorrelated or weakly correlated with the response.
Partial quantile regression
Partial least squares regression (PLSR) is a method of finding a reliable predictor of the response variable when there are more regressors than observations. It does so by eliciting a small number
Estimation in functional linear quantile regression
This paper studies estimation in functional linear quantile regression in which the dependent variable is scalar while the covariate is a function, and the conditional quantile for each fixed
Semiparametric Efficient Estimation of Partially Linear Quantile Regression Models
Lee (2003) develops a n-consistent estimator of the parametric component of a partially linear quantile regression model, which is used to obtain his one-step semiparametric efficient estimator. As a
The decomposition of the SCAD penalty function is taken as the difference of two convex functions and proposed to solve the corresponding optimization using the Difference Convex Algorithm (DCA).
Quantile Regression via an MM Algorithm
Abstract Quantile regression is an increasingly popular method for estimating the quantiles of a distribution conditional on the values of covariates. Regression quantiles are robust against the
Gradient descent algorithms for quantile regression with smooth approximation
A smooth function to approximate the check loss function so that the gradient based optimization methods could be employed for fitting quantile regression model and can achieve higher prediction accuracy and are more efficient in removing noninformative predictors.