Results 41 to 50 of about 2,329,699 (248)
Factorisable Multitask Quantile Regression [PDF]
SSRN Electronic Journal, 2020 A multivariate quantile regression model with a factor structure is proposed to study data with multivariate responses with covariates. The factor structure is allowed to vary with the quantile levels, which is more flexible than the classical factor models.
Chao, Shih-Kang +2 more
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Simulation Study The Using of Bayesian Quantile Regression in Nonnormal Error
Cauchy: Jurnal Matematika Murni dan Aplikasi, 2018 The purposes of this paper is to introduce the ability of the Bayesian quantile regression method in overcoming the problem of the nonnormal errors using asymmetric laplace distribution on simulation study.
Catrin Muharisa +2 more
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CoRR, 2019 Quantile regression is a tool for learning conditional distributions. In this paper we study quantile regression in the setting where a protected attribute is unavailable when fitting the model. This can lead to "unfair'' quantile estimators for which the effective quantiles are very different for the subpopulations defined by the protected attribute ...
Dana Yang, John Lafferty, David Pollard
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An Algorithm of Nonparametric Quantile Regression
Journal of Statistical Theory and Practice, 2023 Extreme events, such as earthquakes, tsunamis, and market crashes, can have substantial impact on social and ecological systems. Quantile regression can be used for predicting these extreme events, making it an important problem that has applications in many fields. Estimating high conditional quantiles is a difficult problem.
Mei Ling Huang +2 more
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Quantile Uncorrelation and Instrumental Regressions [PDF]
Journal of Econometric Methods, 2012 Abstract We introduce a notion of median uncorrelation that is a natural extension of mean (linear) uncorrelation. A scalar random variable Y is median uncorrelated with a k-dimensional random vector X if and only if the slope from an LAD regression of Y on X is zero.
Komarova, Tatiana +2 more
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Testing the Equality of Two Parametric Quantile Regression Curves : The Application for Comparing Two Data Sets [PDF]
, 2016 This study aims to compare the different between two data sets that having the relationship between the dependent and independent variables at each quantile using testing the equality of two parametric quantile regression functions, the conditional ...
Tonggumnead, Unchalee; Faculty of Science and Technology, Rajamangala University of Technology Thanyaburi, 39 Moo1, Rangsit-Nakhonnayok Rd. Klong6, Thanyaburi, Pathum Thani 12110Thailand
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Journal of Economy Culture and Society, 2021 This study comparatively analyzes wage inequality in the Turkish labor force by estimating the generalized Mincer wage equation with quantile regression methods using the 2018 Turkish Household Budget Survey data.
Ebru Çağlayan Akay +1 more
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ReModels: Quantile Regression Averaging models
SoftwareXElectricity price forecasts are essential for making informed business decisions within the electricity markets. Probabilistic forecasts, which provide a range of possible future prices rather than a single estimate, are particularly valuable for ...
Grzegorz Zakrzewski +3 more
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Bayesian quantile semiparametric mixed-effects double regression models
Statistical Theory and Related Fields, 2021 Semiparametric mixed-effects double regression models have been used for analysis of longitudinal data in a variety of applications, as they allow researchers to jointly model the mean and variance of the mixed-effects as a function of predictors ...
Duo Zhang +3 more
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Statistics and ComputingAbstractWe introduce quantile ratio regression. Our proposed model assumes that the ratio of two arbitrary quantiles of a continuous response distribution is a function of a linear predictor. Thanks to basic quantile properties, estimation can be carried out on the scale of either the response or the link function.
Alessio Farcomeni, Marco Geraci
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