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Quantile Regression
Quantile regression is an extension of linear regression that allows researchers to estimate the conditional quantiles of a response variable given a...
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Jackknife Model Averaging for Composite Quantile Regression
In this paper, the authors propose a frequentist model averaging method for composite quantile regression with diverging number of parameters....
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Semi-Functional Partial Linear Quantile Regression Model with Randomly Censored Responses
Censored data with functional predictors often emerge in many fields such as biology, neurosciences and so on. Many efforts on functional data...
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Analysis of hospitalization costs in adult inguinal hernia: based on quantile regression model
BackgroundInguinal hernia repair is a common surgical procedure with significant variability in hospitalization costs. Traditional cost analysis...
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Quantile Regression
The problem with OLS is demonstrated via Engel’s problem. Concept and definitions of quantiles as well as their connection with other statistical...
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A Nonparametric Model Checking Test for Functional Linear Composite Quantile Regression Models
This paper is focused on the goodness-of-fit test of the functional linear composite quantile regression model. A nonparametric test is proposed by...
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Nonlinear prediction of fuzzy regression model based on quantile loss function
In this paper, a new approach is presented to fit a fuzzy regression model with the fuzzy coefficients when the explanatory variables and the...
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Quantile ratio regression
We introduce quantile ratio regression. Our proposed model assumes that the ratio of two arbitrary quantiles of a continuous response distribution is...
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An Efficient Multi Quantile Regression Network with Ad Hoc Prevention of Quantile Crossing
This article presents the Sorting Composite Quantile Regression Neural Network (SCQRNN), an advanced quantile regression model designed to prevent...
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Composite quantile estimation in partially functional linear regression model with randomly censored responses
In this paper, we focus on the studying of composite quantile estimation for the partially functional linear regression model with randomly censored...
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Differentially Private Quantile Regression
Quantile regression (QR) is a powerful and robust statistical modeling method broadly used in many fields such as economics, ecology, and healthcare....
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Multi-dimensional Panels in Quantile Regression Models
This chapter studies estimation and inference methods for multi-dimensional quantile regression panel data models. First, we discuss the fixed...
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Construction of optimal designs for quantile regression model via particle swarm optimization
As an extension of mean regression and being robust against outliers, quantile regression has been used in many fields such as biomedicine, ecology,...
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Data Augmentation Based Quantile Regression Estimation for Censored Partially Linear Additive Model
As a common semiparametric mode, the partially linear additive model has flexible structures, and it has been widely used in practice. In this paper,...
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Multiple-output quantile regression neural network
Quantile regression neural network (QRNN) model has received increasing attention in various fields to provide conditional quantiles of responses....
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Robust Inference for Censored Quantile Regression
In various fields such as medical science and finance, it is not uncommon that the data are heavy-tailed and/or not fully observed, calling for...
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A new quantile regression model with application to human development index
A new odd log-logistic unit omega distribution is defined and studied, and some of its structural properties are obtained. A quantile regression...
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Interpretable Quantile Regression by Optimal Decision Trees
The field of machine learning is subject to an increasing interest in models that are not only accurate but also interpretable and robust, thus...
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Penalized function-on-function linear quantile regression
We introduce a novel function-on-function linear quantile regression model to characterize the entire conditional distribution of a functional...
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Zero-Adjusted Log-Symmetric Quantile Regression Models
This paper proposes zero-adjusted log-symmetric quantile regressions to deal with the issue of regression estimation when there are many zeros in the...
