Wavelet estimation of the geographically and temporally weighted variable coefficient regression model

Zhaoxuan Sun; Rong Ke

doi:10.1117/12.2679218

14 June 2023 Wavelet estimation of the geographically and temporally weighted variable coefficient regression model

Zhaoxuan Sun, Rong Ke

Proceedings Volume 12725, International Conference on Pure, Applied, and Computational Mathematics (PACM 2023); 127250Q (2023) https://doi.org/10.1117/12.2679218
Event: International Conference on Pure, Applied, and Computational Mathematics (PACM 2023), 2023, Suzhou, China

Abstract

As one of the forms of semiparametric model, the variable coefficient regression model increases the flexibility and adaptability of the model by assuming that the regression coefficient in the linear regression model is the unknown of other independent variables, overcomes the "dimensional disaster" in the high-dimensional data model, and embeds the geographically and temporally weighted variable coefficient regression model (GTWRM). Based on the basic theory of wavelet estimation, this paper proposes a wavelet kernel coefficient estimation method for the model, and uses the wavelet kernel function to obtain the coefficient estimation.

Citation Download Citation

Zhaoxuan Sun and Rong Ke "Wavelet estimation of the geographically and temporally weighted variable coefficient regression model", Proc. SPIE 12725, International Conference on Pure, Applied, and Computational Mathematics (PACM 2023), 127250Q (14 June 2023); https://doi.org/10.1117/12.2679218

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