Results 21 to 30 of about 3,477,506 (374)

Batch effects removal for microbiome data via conditional quantile regression

Nature Communications, 2022
Batch effects in microbiome data arise from differential processing of specimens and can lead to spurious findings and obscure true signals. Strategies designed for genomic data to mitigate batch effects usually fail to address the zero-inflated and over-
Wodan Ling, Jiuyao Lu, Ni Zhao, Anju Lulla, Anna M. Plantinga, Weijia Fu, Angela Zhang, Hongjiao Liu, Hoseung Song, Zhigang Li, Jun Chen, T. Randolph, W. L. Koay, J. White, L. Launer, A. Fodor, K. Meyer, Michael C. Wu   +17 more
semanticscholar   +1 more source

Smoothing Quantile Regressions [PDF]

Journal of Business & Economic Statistics, 2019
We propose to smooth the entire objective function, rather than only the check function, in a linear quantile regression context. Not only does the resulting smoothed quantile regression estimator yield a lower mean squared error and a more accurate Bahadur-Kiefer representation than the standard estimator, but it is also asymptotically differentiable.
Marcelo Fernandes, Emmanuel Guerre, Eduardo Horta   +2 more
openaire   +3 more sources

Quantile regression in high-dimension with breaking [PDF]

Journal of Statistical Theory and Applications (JSTA), 2013
The paper considers a linear regression model in high-dimension for which the predictive variables can change the influence on the response variable at unknown times (called change-points).
Gabriela Ciuperca
doaj   +1 more source

Nonparametric Smoothing for Extremal Quantile Regression with Heavy Tailed Data

Revstat Statistical Journal, 2021
In several different fields, it is interested in analyzing the upper or lower tail quantile of the underlying distribution rather than mean or center quantile.
Takuma Yoshida
doaj   +1 more source

Spatial Quantile Regression

Comparative Economic Research, 2012
In a number of applications, a crucial problem consists in describing and analyzing the influence of a vector Xi of covariates on some real-valued response variable Yi.
Grażyna Trzpiot
doaj   +1 more source

Nonparametric C- and D-vine-based quantile regression

Dependence Modeling, 2022
Quantile regression is a field with steadily growing importance in statistical modeling. It is a complementary method to linear regression, since computing a range of conditional quantile functions provides more accurate modeling of the stochastic ...
Tepegjozova Marija, Zhou Jing, Claeskens Gerda, Czado Claudia   +3 more
doaj   +1 more source

Comparison of Bayesian and frequentist quantile regressions in studying the trend of discharge changes in several hydrometric stations of the Gorganroud basin in Iran

Journal of Water and Climate Change, 2023
This research utilized Bayesian and quantile regression techniques to analyze trends in discharge levels across various seasons for three stations in the Gorganroud basin of northern Iran. The study spanned a period of 50 years (1966–2016).
Khalil Ghorbani, Meysam Salarijazi, Sedigheh Bararkhanpour, Laleh Rezaei Ghaleh   +3 more
doaj   +1 more source

Vector quantile regression [PDF]

, 2014
We propose a notion of conditional vector quantile function and a vector quantile regression. A conditional vector quantile function (CVQF) of a random vector Y, taking values in ℝd given covariates Z=z, taking values in ℝk, is a map u↦QY∣Z(u,z), which is monotone, in the sense of being a gradient of a convex function, and such that given that vector U
Guillaume Carlier, Victor Chernozhukov, Alfred Galichon   +2 more
openaire   +5 more sources

The impact of technological innovation on renewable energy production: accounting for the roles of economic and environmental factors using a method of moments quantile regression

Heliyon, 2022
Advancement in renewables is one of the most effective techniques for sustained long-term development, and nations across the globe are making efforts to change their economic and industrial structures in a bid to boost green growth.
S. Solarin, Mufutau Opeyemi Bello, A. Tiwari   +2 more
semanticscholar   +1 more source

Gradient boosting for extreme quantile regression [PDF]

Extremes, 2021
Extreme quantile regression provides estimates of conditional quantiles outside the range of the data. Classical quantile regression performs poorly in such cases since data in the tail region are too scarce.
J. Velthoen, C. Dombry, J. Cai, Sebastian Engelke   +3 more
semanticscholar   +1 more source