Local polynomial expectile regression
| Autor: | C. Adam, Irène Gijbels |
|---|---|
| Rok vydání: | 2022 |
| Předmět: |
Statistics and Probability
Polynomial 05 social sciences Asymptotic distribution Estimator Context (language use) 01 natural sciences Regression 010104 statistics & probability Simple (abstract algebra) 0502 economics and business Statistics::Methodology Applied mathematics 0101 mathematics Smoothing 050205 econometrics Mathematics Quantile |
| Popis: | This paper studies local polynomial estimation of expectile regression. Expectiles and quantiles both provide a full characterization of a (conditional) distribution function, but have each their own merits and inconveniences. Local polynomial fitting as a smoothing technique has a major advantage of being simple, allowing for explicit expressions and henceforth advantages when doing inference theory. The aim of this paper is twofold: to study in detail the use of local polynomial fitting in the context of expectile regression and to contribute to the important issue of bandwidth selection, from theoretical and practical points of view. We discuss local polynomial expectile regression estimators and establish an asymptotic normality result for them. The finite-sample performance of the estimators, combined with various bandwidth selectors, is investigated in a simulation study. Some illustrations with real data examples are given. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
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