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Transitioning away from stochastic process models
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DOI：10.1016/j.jsv.2023.117871.png)
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It has been over 30 years since a paradigm shift from abstract stochastic process models to more concrete Fraction-of-Time Probability models for time-series data was called for and was supported by this journal's editor in chief. Yet, little, if any, detectable progress in making this transition has occurred. This paper reviews this needed transition and attempts to facilitate it with a new type of stochastic process model. The primary purpose of this model is to serve as a pedagogical tool for facilitating the conceptual transition from the standard relatively abstract way of thinking to a more concrete alternative. The utility of this parsimonious alternative was thoroughly proven when it was introduced in an advanced 1987 textbook, and the evidence in support has continued to accumulate in subsequent theoretical and applied research publications. But resistance to change is ever present.
Keyword:
Vibration data modeling
Sound data modeling
Stochastic processes
Statistical inference
Time-series analysis
Probabilistic modeling
Ergodicity
Cyclostationarity
期刊
IF:
4.9
论文数:
1.7W
被引数:
4.8W
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