题目: Time Series Analysis and Data Science
主讲人： 王友乾
时间：2018年7月10日9:00——11:30（I）
2018年7月10日14:00——16:30（II）
地点：科研楼308室
内容简介：
We introduce robust procedures for analyzing quality data collected over time. One challenging task in analyzing such data is how to achieve robustness in presence of outliers while maintaining high estimation efficiency so that we can draw valid conclusions and provide useful advices in water management. The robust approach requires specification of a loss function such as the Huber, Tukey’s bisquare and the exponential loss function, and an associated tuning parameter determining the extent of robustness needed. High robustness is at the cost of efficiency loss in parameter loss. To this end, we propose a data-driven method which leads to more efficient parameter estimation. This data-dependent approach allows us to choose a regularization (tuning) parameter that depends on the proportion of “outliers” in the data so that estimation efficiency is maximized. We illustrate the proposed methods using a study on ammonium nitrogen concentrations from two sites in the Huaihe River in China, where the interest is in quantifying the trend in the most recent years while accounting for possible temporal correlations and “irregular” observations in earlier years.
主讲人简介：
Professor Wang’s research interests include developing statistical methodology for correlated data analysis, robust inferences and model selection and applying advanced techniques that help to solve important problems in medical sciences, environmental research and natural resource management.
In applied statistics, Professor Wang has been working with multidiscipline teams on a wide range of problems. Their findings have significant impacts in resource management (fisheries and hydrology) and clinical trials (biostatistics). Professor Wang has been invited on a number of occasions to organize/speak at international conferences and to review journal papers. His work has substantial impacts and scientific innovations in statistical modelling and data science.
