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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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Quantile Based Geometric Vitality Function of Order Statistics
AbstractIn [
1 ], the authors introduced the geometric vitality function that explains the failure pattern of components or systems based on the...
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Kernel Quantile Estimation
From kernel distribution function, quantile estimators can be defined naturally. Using the kernel estimator of the p-th quantile of a distribution...
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An adapted loss function for composite quantile regression with censored data
This paper investigates an adapted loss function for the estimation of a linear regression with right censored responses. The adapted loss function...
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Function-on-Function Partial Quantile Regression
A function-on-function linear quantile regression model, where both the response and predictors consist of random curves, is proposed by extending...
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Additive hazards quantile model
Even though the proportional hazards model has been used extensively in reliability and survival analysis, it often fails to satisfy the basic...
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Bayesian joint quantile autoregression
Quantile regression continues to increase in usage, providing a useful alternative to customary mean regression. Primary implementation takes the...
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An Algorithm of Nonparametric Quantile Regression
Extreme events, such as earthquakes, tsunamis, and market crashes, can have substantial impact on social and ecological systems. Quantile regression...
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A Study on Quantile based Cumulative Residual Extropy of Order Statistics
In recent times, there has been a growing interest among researchers in utilizing quantile-based approaches for assessing the uncertainty associated...
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Statistical inference for linear quantile regression with measurement error in covariates and nonignorable missing responses
In this paper, we consider quantile regression estimation for linear models with covariate measurement errors and nonignorable missing responses....
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Gradient boosting for extreme quantile regression
Extreme quantile regression provides estimates of conditional quantiles outside the range of the data. Classical quantile regression performs poorly...
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Quantile Regression for Longitudinal Functional Data with Application to Feed Intake of Lactating Sows
This article focuses on the study of lactating sows, where the main interest is the influence of temperature, measured throughout the day, on the...
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Functional Linear Partial Quantile Regression with Guaranteed Convergence for Neuroimaging Data Analysis
Functional data such as curves and surfaces have become more and more common with modern technological advancements. The use of functional predictors...
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Random forest based quantile-oriented sensitivity analysis indices estimation
We propose a random forest based estimation procedure for Quantile-Oriented Sensitivity Analysis—QOSA. In order to be efficient, a cross-validation...
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Quantile forward regression for high-dimensional survival data
Despite the urgent need for an effective prediction model tailored to individual interests, existing models have mainly been developed for the mean...
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Quantile regression for varying-coefficient partially nonlinear models with randomly truncated data
This paper is concerned with quantile regression (QR) inference of varying-coefficient partially nonlinear models where the response is subject to...
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A spatial semiparametric M-quantile regression for hedonic price modelling
This paper proposes an M-quantile regression approach to address the heterogeneity of the housing market in a modern European city. We show how...
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Jackknife model averaging for mixed-data kernel-weighted spline quantile regressions
In the past two decades, model averaging has attracted more and more attention and is regarded as a much better tool to solve model uncertainty than...
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A Bayesian quantile joint modeling of multivariate longitudinal and time-to-event data
Linear mixed models are traditionally used for jointly modeling (multivariate) longitudinal outcomes and event-time(s). However, when the outcomes...
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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,...
