Jiti Gao: Nonlinear time series – semiparametric and nonparametric methods

Nonlinear Time Series: Nonparametric and Parametric Methods

On Nonparametric and Semiparametric Testing for Multivariate Linear Time Series

We formulate nonparametric and semiparametric hypothesis testing of multivariate stationary linear time series in a unified fashion and propose new test statistics based on estimators of the spectral density matrix. The limiting distributions of these test statistics under null hypotheses are always normal distributions, and they can be implemented easily for practical use. If null hypotheses a...

Semiparametric Bootstrap Prediction Intervals in time Series

One of the main goals of studying the time series is estimation of prediction interval based on an observed sample path of the process. In recent years, different semiparametric bootstrap methods have been proposed to find the prediction intervals without any assumption of error distribution. In semiparametric bootstrap methods, a linear process is approximated by an autoregressive process. The...

Nonparametric and Semiparametric Methods for Interval-censored Failure Time Data

Interval-censored failure time data commonly arise in follow-up studies such as clinical trials and epidemiology studies. For their analysis, what interests researcher most includes comparisons of survival functions for different groups and regression analysis. This dissertation, which consists of three parts, consider these problems on two types of interval-censored data by using nonparametric...

An Improved Nonparametric Unit–Root Test Jiti Gao and Maxwell King An Improved Nonparametric Unit–Root Test

This paper proposes a simple and improved nonparametric unit–root test. An asymptotic distribution of the proposed test is established. Finite sample comparisons with an existing nonparametric test are discussed. Some issues about possible extensions are outlined.