SBIR PHASE II: Statistical Analysis and Software for Long Memory Processes

*** 9801614 Bruce这个小企业创新研究二期项目将对长记忆过程的统计方法进行新的创新研究。这些过程可用于模拟地球物理学、海洋学、天文学、经济学、生理学和其他学科中常见的时间序列。长记忆过程的本质是观察结果之间的相关性随着时间间隔的增加而缓慢降低。使用标准时间序列方法可能导致不正确的统计分析；例如，平均值的置信区间可能小得不切实际。这一研究项目将为长期记忆过程的完整方法论取得重要进展，这是对科学的一项基础性贡献。最终目标是将研究和现有的方法实现到一个面向对象的软件工具包中，用于为广泛的统计学家、数据分析师和科学家建模长记忆时间序列。如果成功，这项研究将导致一个软件工具包，为长记忆时间序列建模提供完整的统计方法。该工具包将作为S-PLUS模块、MathCAD功能包和电子书销售。此外，还将单独编写和销售从业者案例手册。＊＊＊

*** 9801614 Bruce This Small Business Innovation Research Phase II project will conduct new and innovative research into statistical methodology for long memory processes. These processes can be used to model time series commonly encountered in geophysics, oceanography, astronomy, economics, physiology and other disciplines. The essence of a long memory process is that correlation between observations decreases slowly as separation in time increases. Use of standard time series methodology with these can lead to incorrect statistical analysis; e.g., confidence intervals for the mean can be unrealistically small. This research project will make important advances toward a complete methodology for long memory processes, a fundamental contribution to science. The ultimate objective is to implement the research and existing methodology into an object-oriented software tool kit for modeling long memory time series for use by a broad range of statisticians, data analysts and scientists. If successful, the research will lead to a software tool kit offering complete statistical methodology for modeling long memory time series. The tool kit will be marketed as an S-PLUS module and as a MathCAD function pack and electronic book. In addition, a practitioner's casebook of examples will be developed and marketed separately. ***