Abstract
This paper is concerned with the statistical inference of skew-normal partial functional linear models which give a useful extension of the normal functional regression model. The maximum likelihood estimation based on functional principal component analysis is proposed. Furthermore, the score test for homogeneity of the variance is discussed. Asymptotic properties of the proposed estimators and test are established and finite sample behavior is studied through a small simulation experiment.
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