Results 241 to 250 of about 7,257 (284)

Nonparametric quantile regression with missing data using local estimating equations

Journal of nonparametric statistics (Print), 2022
In this paper, we propose augmented inverse probability weighted (AIPW) local estimating equations in dealing with missing data in nonparametric quantile regression context. The missing mechanism here is missing at random.
Chunyu Wang, M. Tian, M. Tang
semanticscholar   +1 more source

On nonparametric regression estimators based on regression quantiles

Communications in Statistics - Theory and Methods, 1987
In the ciassical regression model Yi=h(xi) + ∊ i, i=1,…,n, Cheng (1984) introduced linear combinations of regression quantiles as a new class of estimators for the unknown regression function h(x). The asymptotic properties studied in Cheng (1984) are reconsidered.
P Janssen, N Veraverbeke
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Parametric and nonparametric conditional quantile regression modeling for dependent spatial functional data

, 2021
The problem of estimating the spatio-functional quantile regression for a given spatial mixing structure ( X i , Y i ) ∈ F × R , when i ∈ Z N , N ≥ 1 and F is a separable Hilbert space, is investigated.
Mustapha Rachdi, Ali Laksaci, F. Al-Awadhi   +2 more
semanticscholar   +1 more source

Better nonparametric confidence intervals via robust bias correction for quantile regression

Stat, 2021
In this article, we revisit the problem of how to construct better nonparametric confidence intervals for the conditional quantile function from an optimization perspective.
Shaojun Guo, Yu Han, Qingsong Wang
semanticscholar   +1 more source

Nonparametric regression M-quantiles

Statistics & Probability Letters, 1989
zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Antoch, J., Janssen, P.
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Communication-Efficient Nonparametric Quantile Regression via Random Features

Journal of Computational And Graphical Statistics
This article introduces a refined algorithm designed for distributed nonparametric quantile regression in a reproducing kernel Hilbert space (RKHS).
Caixing Wang, Tao Li, Xinyi Zhang, Xingdong Feng, Xin He   +4 more
semanticscholar   +1 more source

Variational Inference for Nonparametric Bayesian Quantile Regression

Proceedings of the AAAI Conference on Artificial Intelligence, 2015
Quantile regression deals with the problem of computing robust estimators when the conditional mean and standard deviation of the predicted function are inadequate to capture its variability. The technique has an extensive list of applications, including health sciences, ecology and finance.
Sachinthaka Abeywardana, Fabio Ramos 0001   +1 more
openaire   +1 more source

Nonparametric Probabilistic Forecasting of Regional Photovoltaic Power Based on Spatial Clustering and Combining Quantile Regression

2024 IEEE/IAS Industrial and Commercial Power System Asia (I&CPS Asia)
As the share of distributed photovoltaic power generation increases rapidly, accurate and reliable regional photovoltaic power uncertainty quantifying becomes crucial to the economic and secure operation of power systems.
Zhiqiang He, Yan Peng, Can Wan, Hao Sun, Chengyu Lu, Wenjin Chen   +5 more
semanticscholar   +1 more source

Local asymptotics for nonparametric quantile regression with regression splines

Statistics & Probability Letters, 2016
zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Zhao, Weihua, Lian, Heng
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Comparison between the estimated of nonparametric methods by using the methodology of quantile regression models

Periodicals of Engineering and Natural Sciences (PEN), 2020
This paper study two stratified quantile regression models of the marginal and the conditional varieties. We estimate the quantile functions of these models by using two nonparametric methods of smoothing spline (B-spline) and kernel regression (Nadaraya-
M. Ibrahim, Q. N. N. Al-Qazaz
semanticscholar   +1 more source