Powerful Strategy for Point Process Data Analysis and the Archive of Models

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Various Exploratory Data Analysis techniques have been developed particularly for time point data. The aim of this research is to provide a general way of building appropriate models for the given data. The models we have investigated are clustering marked point process, hidden Markov point process, hidden semi Markov point process, mixed point process, multiple point process and zero reset intensity model. We have developed not only mathematical theory but also algorithms of solving the maximum likelihood equation and a total environment for supporting the process of model building. The targeted phenomena are neural activation, hear beat, rain fall, earthquakes, customer visit to a beauty parlor, new bid price of foreign exchange. The environment is based on DandD and one of the features is a new data visualization technique called Textile plot.