Generalized kernel regression estimator for dependent size-biased data

This paper considers nonparametric regression estimation in the context of dependent biased nonnegative data using a generalized asymmetric kernel. It may be applied to a wider variety of practical situations, such as the length and size biased data. We derive theoretical results using a deep asymptotic analysis of the behavior of the estimator that provides consistency and asymptotic normality in addition to the evaluation of the asymptotic bias term. The asymptotic mean squared error is also derived in order to obtain the optimal value of smoothing parameters required in the proposed estimator. The results are stated under a stationary ergodic assumption, without assuming any traditional mixing conditions. A simulation study is carried out to compare the proposed estimator with the local linear regression estimate.