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Nonparametric Regression Estimation under Kernel Polynomial Model for Unstructured Data
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Farzad Eskandari   1, Sima Naghizade Ardebili2 , Dana Naderi3 , Mohammad Mahdavi3 , Ali Fakhrae3 |
1- Allameh Tabataba'i University , askandari@atu.ac.ir
2- National Organization for Education Testing
3- Allameh Tabataba'i University |
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Abstract: (513 Views) |
| The nonparametric estimation(NE) of kernel polynomial regression (KPR) model is a powerful tool to visually depict the effect of covariates on response variable, when there exist unstructured and heterogeneous data. In this paper we introduce KPR model that is the mixture of nonparametric regression models with bootstrap algorithm, which is considered in a heterogeneous and unstructured framework. Also, the optimal properties of estimators have been considered. Finallly, we have studied a real heterogeneous and unstructured data using the KPR model. |
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| Keywords: COVID-19, nonparametric estimation, Kernel polynomial regression model, pridiction analysis, graphical model. |
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Full-Text [PDF 5246 kb]
(662 Downloads)
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Type of Study: Applicable |
Subject:
General
Received: 2021/09/13 | Accepted: 2022/05/29 | Published: 2020/08/22
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| References |
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Eskandari F, Naghizade Ardebili S, Naderi D, Mahdavi M, Fakhrae A. Nonparametric Regression Estimation under Kernel Polynomial Model for Unstructured Data. JSRI 2020; 17 (1) :135-156 URL: http://jsri.srtc.ac.ir/article-1-424-en.html
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