Results 31 to 40 of about 2,329,699 (248)
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 +3 more
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Penalized flexible Bayesian quantile regression [PDF]
, 2012 Copyright © 2012 SciResThis article has been made available through the Brunel Open Access Publishing Fund.The selection of predictors plays a crucial role in building a multiple regression model. Indeed, the choice of a suitable subset of predictors can
Yu, K, Alkenani, A, Alhamzawi, R
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Quantile Regression Approach to Model Censored Data
Science and Technology Indonesia, 2020 The censored quantile regression model is derived from the censored model. This method is used to overcome problems in modeling censored data as well as to overcome the assumptions of linear models that are not met.
Sarmada Sarmada, Ferra Yanuar
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Local quantile regression [PDF]
Journal of Statistical Planning and Inference, 2013 Quantile regression is a technique to estimate conditional quantile curves. It provides a comprehensive picture of a response contingent on explanatory variables. In a flexible modeling framework, a specific form of the conditional quantile curve is not a priori fixed.
Wolfgang Karl Härdle +2 more
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On elliptical quantiles in the quantile regression setup
Journal of Multivariate Analysis, 2013 zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Daniel Hlubinka, Miroslav Siman
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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 +2 more
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ERM Scheme for Quantile Regression
Abstract and Applied Analysis, 2013 This paper considers the ERM scheme for quantile regression. We conduct error analysis for this learning algorithm by means of a variance-expectation bound when a noise condition is satisfied for the underlying probability measure. The learning rates are
Dao-Hong Xiang
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Factors Affecting Productivity of Upland and Lowland Rice Farms in Matalom, Leyte: A Quantile Regression Approach [PDF]
Review of Socio-Economic Research and Development Studies, 2017 This study investigates the determinants of productivity in selected upland and lowland rice farms in Matalom, Leyte using quantile regression approach. Data on rice production are obtained from 40 upland and 40 lowland rice farming households which are ...
Brenda M. Ramoneda, Junnel K. Pene
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Quantile regression, asset pricing and investment decision
IIMB Management Review, 2021 The present study compares the Fama-French three factor coefficient estimates obtained from both ordinary least squares (OLS) and quantile regression for 25 size-value sorted portfolios of BSE 500.
Moinak Maiti
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Linear Quantile Mixed Models: The lqmm Package for Laplace Quantile Regression
Journal of Statistical Software, 2014 Inference in quantile analysis has received considerable attention in the recent years. Linear quantile mixed models (Geraci and Bottai 2014) represent a ?exible statistical tool to analyze data from sampling designs such as multilevel, spatial, panel or
Marco Geraci
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