Course TopicsThis short course will provide an overview of non-parametric statistical techniques. The course will first describe what non-parametric statistics are, when they should be used, and their ...

Parametric tests make assumptions that aspects of the data follow some sort of theoretical probability distribution. Non-parametric tests or distribution free methods do not, and are used when the ...

We have introduced some new non-parametric estimators for VaR. Comparison between these estimators are made using in-sample and out-of-sample backtesting techniques. It is found that one of the newly ...

Hong Zhu, Non-parametric Analysis of Gap Times for Multiple Event Data: An Overview, International Statistical Review / Revue Internationale de Statistique, Vol. 82, No. 1 (April 2014), pp. 106-122 ...

We use influence functions as a basic tool to study unconditional nonparametric and parametric expected shortfall (ES) estimators with regard to returns data influence, standard errors and coherence.

Gang Li, Ingrid Van Keilegom, Likelihood Ratio Confidence Bands in Non-Parametric Regression with Censored Data, Scandinavian Journal of Statistics, Vol. 29, No. 3 (Sep., 2002), pp. 547-562 ...

Recent advances in nonparametric regression for functional data have focused on enhancing estimator performance whilst addressing practical issues such as bandwidth selection and model adaptivity.