Proceedings of the 5th International Conference on Statistics, Mathematics, Teaching, and Research 2023 (ICSMTR 2023)

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Multivariable Semiparametric Regression Used Priestley-Chao Estimators

Authors
Makkulau1, *, Andi Tenri Ampa1, Baharuddin1, Mukhsar1, Agusrawati1, Andi Tenri Pannangngareng Makkulau1, INyoman Sudiasa2
1Statistics Department, Universitas Halu Oleo, Kendari, Indonesia
2Civil Engineering Department, Institut Teknologi Nasional Malang, Malang, Indonesia
*Corresponding author. Email: makkulaufmipa@uho.ac.id
Corresponding Author
Makkulau
Available Online 18 December 2023.
DOI
10.2991/978-94-6463-332-0_14How to use a DOI?
Keywords
GCV; semiparametric; kernel.tatistical Modeling; School Dropout; Indonesia
Abstract

Semiparametric regression combines the goodness of parametric regression estimators and Kernel regression. In this research, a new method of semiparametric regression model was developed which contains parametric and non-parametric components, the second being multivariable, where the data contains outlier data. Here we propose a multivariable Kernel method approach for data that does not follow a certain pattern and has outliers. The kernel function used is a multivariable Gaussian Kernel function with a Priestley-Chao estimators approach. The goodness of the Kernel estimator depends on the value of the bandwidth parameter, to get the optimal bandwidth parameter using the Generalized Cross Validation (GCV) method. The results of the theoretical study obtained from the Mixed Kernel Regression Curve and multivariable Fourier Series estimators in this semiparametric regression, which is a combination of multivariable parametric and multivariable nonparametric estimators. The estimators obtained are biased estimators but are a class of linear estimators.

Copyright
© 2023 The Author(s)
Open Access
Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

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Volume Title
Proceedings of the 5th International Conference on Statistics, Mathematics, Teaching, and Research 2023 (ICSMTR 2023)
Series
Advances in Computer Science Research
Publication Date
18 December 2023
ISBN
10.2991/978-94-6463-332-0_14
ISSN
2352-538X
DOI
10.2991/978-94-6463-332-0_14How to use a DOI?
Copyright
© 2023 The Author(s)
Open Access
Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

Cite this article

risenwbib
TY  - CONF
AU  - Makkulau
AU  - Andi Tenri Ampa
AU  - Baharuddin
AU  - Mukhsar
AU  - Agusrawati
AU  - Andi Tenri Pannangngareng Makkulau
AU  - INyoman Sudiasa
PY  - 2023
DA  - 2023/12/18
TI  - Multivariable Semiparametric Regression Used Priestley-Chao Estimators
BT  - Proceedings of the 5th International Conference on Statistics, Mathematics, Teaching, and Research 2023 (ICSMTR 2023)
PB  - Atlantis Press
SP  - 118
EP  - 127
SN  - 2352-538X
UR  - https://doi.org/10.2991/978-94-6463-332-0_14
DO  - 10.2991/978-94-6463-332-0_14
ID  - 2023
ER  -