Nonparametric relative recursive regression
| Autor: | Salah Khardani, Yousri Slaoui |
|---|---|
| Rok vydání: | 2020 |
| Předmět: |
Statistics and Probability
Science (General) relative regression 01 natural sciences Q1-390 010104 statistics & probability curve fitting 0502 economics and business Statistics QA1-939 0101 mathematics 050205 econometrics Mathematics stochastic approximation algorithm Computer Science::Information Retrieval Applied Mathematics 05 social sciences Nonparametric statistics smoothing Regression Nonparametric regression nonparametric regression 62g08 Modeling and Simulation Curve fitting 62l20 65d10 Smoothing |
| Zdroj: | Dependence Modeling, Vol 8, Iss 1, Pp 221-238 (2020) |
| ISSN: | 2300-2298 |
| DOI: | 10.1515/demo-2020-0013 |
| Popis: | In this paper, we propose the problem of estimating a regression function recursively based on the minimization of the Mean Squared Relative Error (MSRE), where outlier data are present and the response variable of the model is positive. We construct an alternative estimation of the regression function using a stochastic approximation method. The Bias, variance, and Mean Integrated Squared Error (MISE) are computed explicitly. The asymptotic normality of the proposed estimator is also proved. Moreover, we conduct a simulation to compare the performance of our proposed estimators with that of the two classical kernel regression estimators and then through a real Malaria dataset. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
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