Financial time series modeling, dealing with outliers and censoring, water quality modeling and process improvement

这项拟议的研究是商业、金融和工业中时间序列建模和统计领域正在进行的研究计划的一部分。 波动率的建模和预测在评估金融市场的风险和不确定性方面发挥着重要作用。该项目的目标之一是开发新的随机波动率模型，设计有效的方法来估计它们，检查它们的充分性，并生成预测和预测区间。最近，我们引入了一类新的模型，称为伽马随机波动(GSV)过程。我们计划为这类模型以及传统的波动率模型开发预测和推断程序。 政策决策和各种环境法规的实施需要从适当收集和分析的数据中获得准确的信息。该项目的第二个目标是开发新方法并加强现有方法，以分析时间序列数据，以评估水质趋势。我们计划设计新的程序来处理水质时间序列中经常遇到的离群值、截尾和条件异方差。 质量改进工作对加拿大商业和工业组织的成功非常重要。该项目的第三个目标是开发新的统计方法并加强可应用于加拿大工商业的现有方法。我们计划开发用于工业建模的经验似然程序、设计中的绩效衡量的自举分析以及用于多变量预测的降维方法。

The proposed research is part of an ongoing research program in the areas of Time Series Modeling and Statistics in Business, Finance and Industry. Modeling and predicting volatility plays an important role in assessing risk and uncertainty in financial markets. One of the objectives in this project is to develop new stochastic volatility models, devising efficient methods of estimating them, checking for their adequacy and generating predictions and prediction intervals. Recently we have introduced a new class of models referred to as a gamma stochastic volatility (GSV) process. We plan to develop prediction and inference procedures for this class of models as well as for traditional volatility models. Policy decisions and the implementation of various environmental regulations require accurate information from appropriately collected and analyzed data. A second objective of the project is to develop new methods and enhance existing ones for the analysis of time series data to assess trends in water quality. We plan to devise new procedures to deal with outliers, censoring and conditional heteroscedasticity which are often encountered in water quality time series. Quality Improvement efforts are very important for the success of Canadian Business and Industrial organizations. A third objective of the project is to develop new statistical methods and enhance existing ones which can be applied to Canadian Business and Industry. We plan to develop empirical likelihood procedures for industrial modeling, bootstrap analysis of performance measures in designs and dimension reduction methods for multivariate prediction.