Estimation and diagnostic for partially linear models with first-order autoregressive skew-normal errors

Clécio da Silva Ferreira (), Gilberto A. Paula () and Gustavo C. Lana ()
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Clécio da Silva Ferreira: Federal University of Juiz de Fora
Gilberto A. Paula: University of São Paulo
Gustavo C. Lana: Federal University of Juiz de Fora

Computational Statistics, 2022, vol. 37, issue 1, No 19, 445-468

Abstract: Abstract Estimation and diagnostic procedures for partially linear models with first-order autoregressive [AR(1)] skew-normal errors are proposed in this paper. An EM iterative process with analytic expressions for the M and E-steps, which combines back-fitting and Newton–Raphson algorithms, is developed for the parameter estimation. A linear smoother for the estimation of the effective degrees of freedom concerning the nonparametric component is derived from the iterative process. Local influence analysis is developed based on the conditional expectation of the complete-data log-likelihood function, used in the EM algorithm. A simulation study is also conducted to evaluate the efficiency of the EM algorithm. Finally, the methodology developed through the paper is illustrated with a real data set on daily ozone concentration.

Keywords: Back-fitting algorithm; EM-algorithm; Local influence; Penalized Smoothing; Semiparametric models; Skew-normal distribution (search for similar items in EconPapers)
Date: 2022
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Citations: View citations in EconPapers (1)

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DOI: 10.1007/s00180-021-01130-2

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