Estimation of the error distribution function in a varying coefficient regression model

Anton Schick, Department of Mathematical Sciences, Binghamton University, USA

This talk discusses estimation of the error distribution function in a varying coefficient regression model. Three estimators are introduced and their asymptotic properties described by uniform stochastic expansions. The first estimator is a residual-based empirical distribution function utilizing an under-smoothed local quadratic smoother of the coefficient function. The second estimator exploits the fact that the error distribution has mean zero. It improves on the first estimator, but is not yet efficient. An efficient estimator is obtained by adding a stochastic correction term to the second estimator.

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• MSUStatSymposium2018@stt.msu.edu
• Michigan State University
  Department of Statistics and Probability
  Room C413 Wells Hall
  619 Red Cedar Road
  East Lansing, MI 48824