Quantile Regression

By Furno Marilena, Vistocco Domenico. Wiley Series in Probability and Statistics, 2018

1,745 citations recorded by the source corpus.

View Paper: https://www.semanticscholar.org/paper/0c0a97ebb11b9773900f682618815443b2b5153a

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Citations in the corpus

A quantile correlated random coefficients panel data model (2016, methodology): “In order to identify the ACQE, β̄(τ), we write it as the limit of a sequence of “trimmed” expectations β̄(τ) = E[β(τ ; X)] = lim h↓0 E[β(τ ; X)1(|D| > h)] = lim h↓0 E[X−1(QY|X(τ |X)−Wδ(τ))1(|D| > h)], (26) where the second equality holds because β̄(τ)− E[β(τ ; X)1(|D| > h)] = E[β(τ ; X)1(|D| ≤ h)] =”
Forecasting: theory and practice (2020, methodology): “41 such issues as decomposing combination accuracy gains, constructing prediction intervals (Koenker, 2005; Grushka-Cockayne and Jose, 2020), and generating combined probability forecasts (Raftery et al., 1997; Ranjan and Gneiting, 2010; Hall and Mitchell, 2007; Clements and Harvey, 2011).”
Optimal subsampling for quantile regression in big data (2020, methodology): “However, the interior point algorithm still need polynomial time for optimization; its worst-case time complexity is O(N5/2p3), where N is the sample size and p is the dimension of the regression coefficient (Sec 6.4.4 of Koenker, 2005).”
The benefits are at the tail: Uncovering the impact of macroprudential policy on growth-at-risk (2020, methodology): “Application f quantile models with fixed effects is straightforward as it proceeds in a qua tile-by-quantile fashion by allowing for a ifferent fixed effect at each quantile (Koenker, 2005).”
Relative Citation Ratio (RCR): A New Metric That Uses Citation Rates to Measure Influence at the Article Level (2015, methodology): “Alternatively, if benchmarking to the median (rather than mean) field-normalized performance for the benchmark group is desired, quantile regression can be used in lieu of simple linear regression (Koenker, 2005).”
Predicting Pre-click Quality for Native Advertisements (2016, methodology): “To evaluate how the features react when predicting different quantiles of OFR, we perform an in-depth study using quantile regression analysis [17].”
High‐dimensional quantile regression: Convolution smoothing and concave regularization (2021, methodology): “We refer to Koenker (2005) and Koenker et al. (2017) for an overview of QR theory, methods and applications.”
Tourism and Economic Growth (2016, background): “For the latter, readers may refer to Lee and Chang (2008), and Sánchez-Rivero, Pulido-Fernández, and Cárdenas-García (2013).”
The World Health Organization Fetal Growth Charts: A Multinational Longitudinal Study of Ultrasound Biometric Measurements and Estimated Fetal Weight (2017, methodology): “Quantile regression is now a well-established technique [26,27], and statistical software is available to fit quantile regression models.”
Investigating the asymmetric linkages between infrastructure development, green innovation, and consumption-based material footprint: Novel empirical estimations from highly resource-consuming economies (2021, methodology): “This method is established 335 by Koenker and Hallock (2001), primarily, this method is only used for quantile asymmetries or a range 336 of quantiles where the response variable depends on the values of the exogenous variable.”
Deep Learning for Time Series Forecasting: Tutorial and Literature Survey (2020, methodology): “So naturally, estimating the quantiles directly via quantile regression approaches [103] is a common choice in forecasting either via choosing a single quantile (in a point-forecasting approach) or multiple quantiles simultaneously [51, 190].”
Elicitability and backtesting: Perspectives for banking regulation (2016, background): “Patton [2011] underlines the importance of positive homogeneity of the scoring function for forecast ranking.”
Oil Palm Adoption, Household Welfare, and Nutrition Among Smallholder Farmers in Indonesia (2017, background): “Instead of restricting covariate effects on conditional means, these regressions allow analyzing whether the effect of a given covariate changes over the conditional distribution of the dependent variable (Koenker and Hallock, 2001).”
Short term electricity demand forecasting using partially linear additive quantile regression with an application to the unit commitment problem (2018, methodology): “The simplex approach to solving the general `1-regression problem [30,36] is given as follows: minβ∈Rp n ∑ i=1 ρτ ( Yi − Z i β − s(X) ) (7) This is then reformulated as a linear programming problem.”
Uniform Inference for High-dimensional Quantile Regression: Linear Functionals and Regression Rank Scores (2017, background): “Assumption (D) on the density of the error f is common in the literature on quantile regression (Koenker, 2005).”
Forecasting Value at Risk and Expected Shortfall Using a Semiparametric Approach Based on the Asymmetric Laplace Distribution (2019, methodology): “Quantile regression has been used in a variety of applications for the estimation of the parameters in a quantile model (see Koenker 2005).”