Title of Dissertation: Nonparametric Quasi-likelihood in Longitudinal Data Analysis Xiaoping Jiang, Doctor of Philosophy, 2004 Dissertation directed by: Professor Paul J. Smith Statistics Program Department of Mathematics This dissertation proposes a nonparametric quasi-likelihood approach to estimate regression coefficients in the class of generalized linear regression models for longitudinal ...

The present review introduces nonparametric methods. Three of the more common nonparametric methods are described in detail, and the advantages and disadvantages of nonparametric versus parametric methods in general are discussed.

In this paper we derive nonparametric stochastic volatility models in discrete time. These models generalize parametric autoregressive random variance models, which have been applied quite successfully to nancial time series. For the proposed models we investigate nonparametric kernel smoothers. It is seen that so-called nonparametric deconvolution estimators could be applied in this situation ...

The problem of prediction in time series using nonparametric functional techniques is considered. An extension of the local linear method to regression with functional explanatory variable is proposed. This forecasting method is compared with the functional Nadaraya–Watson method and with fi nitedimensional nonparametric predictors for several real-time series. Prediction intervals based on the...

This paper considers a class of nonparametric autoregressive processes and then a class of nonparametric time series regression models with a nonstationary regressor. For the autoregression case, we propose a nonparametric unit–root test for the conditional mean. For the nonparametric time series regression case, we construct a nonparametric test for testing whether the regression is of a known...

In this paper we derive nonparametric stochastic volatility models in discrete time. These models generalize parametric autoregressive random variance models, which have been applied quite successfully to financial time series. For the proposed models we investigate nonparametric kernel smoothers. It is seen that so-called nonparametric deconvolution estimators could be applied in this situatio...

This paper study about using of nonparametric models for Gross National Product data in Turkey and Stanford heart transplant data. It is discussed two nonparametric techniques called smoothing spline and kernel regression. The main goal is to compare the techniques used for prediction of the nonparametric regression models. According to the results of numerical studies, it is concluded that smo...