Quantile Regression: Name Index

By Roger W. Koenker. 2005

550 citations recorded by the source corpus.

View Paper: https://www.semanticscholar.org/paper/09e7c24de82e7e081470110ea086deea25113eb6

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In Statistics and Probability

Citations in the corpus

Quantile coherency: A general measure for dependence between cyclical economic variables (2015, unknown): “Similar to the regression setting, where this approach provides valuable extra information (cf. Koenker (2005)), the quantile-based approach to spectral analysis supplements the traditional L 2 -spectral analysis.”
Parametric quantile regression based on the generalized gamma distribution (2013, background): “A vast literature has arisen on the subject since the seminal paper of Koenker and Bassett (1978), including the important book by Koenker (2005).”
Non‐crossing non‐parametric estimates of quantile curves (2008, methodology): “Note that this is straightforward for methods (1.1) and (1.2) that are based on the check function but at the cost of obtaining crossing quantile curve estimates; see Koenker (2005), chapter 7.”
The trade-growth nexus in the developing countries: a quantile regression approach (2010, unknown): “For surveys of quantile regression methods, see Buchinsky (1998), Koenker and Hallock (2001), and Koenker (2005).”
Nonlinear Panel Data Estimation via Quantile Regressions (2015, unknown): “Koenker (2005) provides a thorough account of these methods.”
Norm- and Criterion-Referenced Student Growth. (2009, methodology): “In analogous fashion to the least squares regression line representing the solution to a minimization problem involving squared deviations, quantile regression functions represent the solution to the optimization of a loss function (Koenker, 2005, p. 5).”
INLA goes extreme: Bayesian tail regression for the estimation of high spatio-temporal quantiles (2018, methodology): “As the α -quantile of the random variable Y minimizes the expected risk function q (cid:55)→ E { α ( Y, q ) } (Koenker, 2005), this justiﬁes using (16) for assessing quantile predictions, although alternative risk measures may also be used (Daouia et al. , 2018).”
Estimation of Quantile Treatment Effects with Stata (2010, background): “See, for example, Koenker (2005). where k is a univariate kernel function and hn is a bandwidth sequence.”
Sieve Wald and QLR Inferences on Semi/Nonparametric Conditional Moment Models (2014, unknown): “But, as explained by Koenker (2005) and others, quantile Engel curves are more informative.”
Misspecification Testing in a Class of Conditional Distributional Models (2013, unknown): “The leading example is certainly the linear quantile regression model (Koenker and Bassett 1978; Koenker 2005), which implies a linear structure for the inverse of the conditional cumulative distribution function (CDF), namely that F−1(τ |x, θ ) = x ′θ (τ ) for some vector-valued functional…”
The Opportunity Cost of Negative Screening in Socially Responsible Investing (2017, methodology): “See Buhai (2005), Koenker (2005), McMillen (2013) for in-depth overviews. literature, we will carry out the median regressions using the Design Bootstrap procedure (Basset and Chen 2001; Baur et al. 2012) with 10,000 replications.”
Feature-based tuning of single-stage simulated annealing for examination timetabling (2015, methodology): “The lower the AIC, the more accurate In addition, for each of the period and room constraints, the elements involved (exam plus roomorperiod)areselectedatrandom.”
A Test of the Conditional Independence Assumption in Sample Selection Models: A TEST OF THE CONDITIONAL INDEPENDENCE ASSUMPTION (2012, unknown): “Buchinsky (1998b), Koenker and Hallock (2000), and Koenker (2005) provide a comprehensive discussion of quantile regression models and recent developments.”
Bayesian Semiparametric Modelling in Quantile Regression (2009, background): “There is an extensive literature on classical estimation for model (1); see, e.g. the review paper by Yu et al. (2003) and the book by Koenker (2005).”