讲座主题：
1. Nonparametric estimation of the quantile differences for right-censored and length-biased data
2. Extreme Quantile Estimation for Autoregressive Models
主讲人：
1.刘玉涛（副教授，硕士生导师，中央财经大学统计与数学学院）
2.黎德元（教授，博士生导师，复旦大学管理学院统计学系）
讲座时间：2018年3月28日下午2点
讲座地点：数经学院研究中心（10211）
主办单位：澳门尼威斯人网站8311
讲座简介：
1. Length-biased and right-censored data is frequently encountered in prevalent cohort studies, which has drawn considerable attention in survival analysis. In this article, we consider survival data arising from length-biased sampling, and propose a new semiparametric model-based procedure to estimate quantile differences of failure time. Asymptotic theoretical properties are established under mild technical conditions, and a resampling method is also proposed to estimate the asymptotic variance of the estimators. We design simulation studies to examine the empirical performance and efficiency of the proposed estimators, and illustrate the proposed estimation method through a real data analysis.
2. Quantile autoregresive model is a useful extension to classical autoregresive models as it can capture the influences of conditioning variables on the location, scale and shape of the response distribution. However, at the extreme tails, standard quantile autoregression estimator is often unstable due to data sparsity. In this paper, assuming quantile autoregresive models, we develop a new estimator for extreme conditional quantiles of time series data based on extreme value theory. We build the connection between the second-order conditions for the autoregression coefficients and for the conditional quantile functions, and establish the asymptotic properties of the proposed estimator. The finite sample performance of the proposed method is illustrated through a simulation study and the analysis of US retail gasoline price.