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Nonparametric Regression Model for Longitudinal Data with Mixed Truncated Spline and Fourier Series

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  • Made Ayu Dwi Octavanny
  • I. Nyoman Budiantara
  • Heri Kuswanto
  • Dyah Putri Rahmawati

Abstract

Existing literature in nonparametric regression has established a model that only applies one estimator to all predictors. This study is aimed at developing a mixed truncated spline and Fourier series model in nonparametric regression for longitudinal data. The mixed estimator is obtained by solving the two‐stage estimation, consisting of a penalized weighted least square (PWLS) and weighted least square (WLS) optimization. To demonstrate the performance of the proposed method, simulation and real data are provided. The results of the simulated data and case study show a consistent finding.

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Handle: RePEc:wly:jnlaaa:v:2020:y:2020:i:1:n:4710745
DOI: 10.1155/2020/4710745
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