Results 31 to 40 of about 76,877 (313)
Identifying Risk Factors for Severe Childhood Malnutrition by Boosting Additive Quantile Regression [PDF]
, 2009 Ordinary linear and generalized linear regression models relate the mean of a response variable to a linear combination of covariate effects and, as a consequence, focus on average properties of the response.
Kneib, Thomas +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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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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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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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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Prediction method of coal calorific value based on quantile regression
Gong-kuang zidonghua, 2022 At present, the traditional linear regression model is mainly used to predict the calorific value of coal. But it is difficult to express the complex relationship between independent variables and dependent variables.
ZHAO Xianzhi, CHEN Junlin
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Vector quantile regression [PDF]
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 +2 more
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Nonparametric estimation of an additive quantile regression model [PDF]
, 2004 This paper is concerned with estimating the additive components of a nonparametric additive quantile regression model. We develop an estimator that is asymptotically normally distributed with a rate of convergence in probability of n^{-r/(2+10)} when ...
Joel L. Horowitz +5 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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