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Title: | Quantile estimation in a generalized asymmetric distributional setting | Authors: | Gijbels, Irène KARIM, Rezaul VERHASSELT, Anneleen |
Issue Date: | 2019 | Source: | Steland, Ansgar; Rafajlowicz͕, Ewaryst; Okhrin, Ostap (Ed.). Stochastic Models, Statistics and Their Applications, p. 13-40. | Series/Report: | Springer Proceedings in Mathematics & Statistics | Series/Report no.: | 294 | Abstract: | Allowing for symmetry in distributions is often a necessity in statistical modelling. This paper studies a broad family of asymmetric densities, which in a regression setting shares basic philosophy with generalized (non)linear models. The main focus however for the family of densities studied here is quantile estimation instead of mean estimation. In a similar fashion a broad family of conditional densities is considered in the regression setting. We discuss estimation of the parameters in the unconditional case, and establish an asymptotic normality result, with explicit expression for the asymptotic variance-covariance matrix. In the regression setting, we allow for flexible modelling and estimate nonparametrically the location and scale functions, leading to semiparametric estimation of conditional quantiles, again in the unifying framework of the considered broad family. The practical use of the proposed methods is illustrated in a real data application on locomotor performance in small and large terrestrial mammals. | Document URI: | http://hdl.handle.net/1942/28221 | ISBN: | 9783030286644 9783030286651 |
DOI: | 10.1007/978-3-030-28665-1_2 | Category: | C1 | Type: | Proceedings Paper | Validations: | vabb 2021 |
Appears in Collections: | Research publications |
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PaperGKV2019Final.pdf Restricted Access | Peer-reviewed author version | 822.22 kB | Adobe PDF | View/Open Request a copy |
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