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Quantile regression, the prediction of conditional quantiles, finds applications in various fields.
Fonctions dé repartition á n dimensions et leurs marges
Sklar, A. (1959) · 1959
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Regression quantiles
Koenker, R. and Bassett, G. (1978) · 1978
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Multivariate models and multivariate dependence concepts
Joe, H. (1997) · 1997
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Vines: A new graphical model for dependent random variables
Bedford, T. and Cooke, R. M. (2002) · 2002
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Quantile regression
Koenker, R. (2005) · 2005
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A primer on copulas for count data
Genest, C. and Nešlehová, J. (2007) · 2007
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An introduction to copulas
Nelsen, R. B. (2007) · 2007
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Nonparametric econometrics: The np package
Hayfield, T. and Racine, J. S. (2008) · 2008
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Pair-copula constructions of multiple dependence
Aas, K., Czado, C., Frigessi, A., and Bakken, H. (2009) · 2009
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On the simplified pair-copula construction — simply useful or too simplistic?
Hobæk Haff, I., Aas, K., and Frigessi, A. (2010) · 2010
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Additive models for quantite regression: Model selection and confidence bandaids
Koenker, R. (2011) · 2011
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Identifying risk factors for severe childhood malnutrition by boosting additive quantile regression
Fenske, N., Kneib, T., and Hothorn, T. (2012) · 2012
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Pair copula constructions for multivariate discrete data
Panagiotelis, A., Czado, C., and Joe, H. (2012) · 2012
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Event labeling combining ensemble detectors and background knowledge
Fanaee-T, H. and Gama, J. (2013) · 2013
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Quantile prediction
Komunjer, I. (2013) · 2013
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Optimal bandwidth selection for nonparametric conditional distribution and quantile functions
Li, Q., Lin, J., and Racine, J. S. (2013) · 2013
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UCI machine learning repository
Lichman, M. (2013) · 2013
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copula: Multivariate dependence with copulas
Hofert, M., Kojadinovic, I., Maechler, M., and Yan, J. (2016) · 2016
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mboost: Model-based boosting
Hothorn, T., Buehlmann, P., Kneib, T., Schmid, M., and Hofner, B. (2016) · 2016
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Evading the curse of dimensionality in nonparametric density estimation with simplified vine copulas
Nagler, T. and Czado, C. (2016) · 2016
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Mixed vine copulas as joint models of spike counts and local field potentials
Onken, A. and Panzeri, S. (2016) · 2016
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Probit transformation for nonparametric kernel estimation of the copula density
Geenens, G., Charpentier, A., and Paindaveine, D. (2017) · 2017
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Examination and visualisation of the simplifying assumption for vine copulas in three dimensions
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Regular vine copulas with the simplifying assumption, time-variation, and mixed discrete and continuous margins
Stöber, J. (2013) · 2013
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Simplified pair copula constructions—limitations and extensions
Stöber, J., Joe, H., and Czado, C. (2013) · 2013
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Some comments on copula-based regression
Dette, H., Hecke, R. V., and Volgushev, S. (2014) · 2014
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Conditional quantiles and tail dependence
Bernard, C. and Czado, C. (2015) · 2015
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quantreg: Quantile regression
Koenker, R. (2015) · 2015
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Killiches, M., Kraus, D., and Czado, C. (2017) · 2017
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D-vine copula based quantile regression
Kraus, D. and Czado, C. (2017) · 2017
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A generic approach to nonparametric function estimation with mixed data
Nagler, T. (2017) · 2017
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Nonparametric estimation of simplified vine copula models: comparison of methods
Nagler, T., Schellhase, C., and Czado, C. (2017) · 2017
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D-vine quantile regression for mixed discrete and continuous data with applications to bank stress testing
Schallhorn, N. (2017) · 2017
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