Non gaussian time series modeling, dealing with outliers, censoring and long memory and process improvement

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The proposed research is part of an ongoing research programme in the areas of time series modeling and statistics in business, finance and industry. Assessing and predicting volatility in financial markets require the appropriate modeling and analysis of financial data. One of the objectives in this project is to develop new stochastic volatility models, devising efficient methods for estimating them, and checking for their adequacy. Many environmental policy decisions and the implementation of various environmental regulations require accurate information obtained from properly collected and appropriately analyzed data. A second objective of the proposal is to develop new methods and enhance existing ones for the collection and analysis of water quality time series data to assess trends and other aspects of water quality. Improved competitive position of business and manufacturing organizations is clearly linked to Quality Improvement activities. The use of appropriate statistical methods is an important part of Quality Improvement. One of the objectives of the proposed research is to develop new statistical methodology and to enhance existing methods that can be applied to Quality Improvement efforts in Canadian business and industry.