Spline-based quasi-likelihood estimation of mixed Poisson regression with single-index models

Minggen Lu ()
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Minggen Lu: University of Nevada, Reno

Metrika: International Journal for Theoretical and Applied Statistics, 2018, vol. 81, issue 1, No 1, 17 pages

Abstract: Abstract We consider spline-based quasi-likelihood estimation for mixed Poisson regression with single-index models. The unknown smooth function is approximated by B-splines, and a modified Fisher scoring algorithm is employed to compute the estimates. The spline estimate of the nonparametric component is shown to achieve the optimal rate of convergence, and the asymptotic normality of the regression parameter estimates is still valid even if the variance function is misspecified. The semiparametric efficiency of the model can be established if the variance function is correctly specified. The variance of the regression parameter estimates can be consistently estimated by a simple procedure based on the least-squares estimation. The proposed method is evaluated via an extensive Monte Carlo study, and the methodology is illustrated on an air pollution study.

Keywords: B-spline; Mixed Poisson regression; Quasi-likelihood estimation; Semiparametric efficiency; Single-index model (search for similar items in EconPapers)
Date: 2018
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DOI: 10.1007/s00184-017-0631-2

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