Abstract
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Highlights
Randomness is the label assigned to the enigmatic and relatively unknown universe of observation-disturbing influence that all scientists experience always and everywhere
The fluctuations one can discern and describe as part of secular morbidity and mortality curves are treated as an annoying feature; by most epidemiologists, noise is regarded as caused by anonymous, completely uninteresting, naturally occurring ‘error-producing’ processes, which should be eliminated
The fact that properties of noise may depend on the phenomenon being studied, makes it relevant to ask whether noise itself might be the most prominent feature of many phenomena [5]. It is the purpose of this study to present fractal methods for the study of time-series and their fluctuations and apply these methods on secular series of cancer incidence rates
Summary
Randomness is the label assigned to the enigmatic and relatively unknown universe of observation-disturbing influence that all scientists experience always and everywhere. With regard to specific classes of randomness, a sub-class is the time-correlated noise [4] For this subclass, noise at a given time is associated to noise at a different time, e.g. pink noise or 1/f noise indicates long-range correlations, implying complex, non-linear processes that generate fluctuations on a wide range of time scales. The fact that properties of noise may depend on the phenomenon being studied, makes it relevant to ask whether noise itself might be the most prominent feature of many phenomena [5]. It is the purpose of this study to present fractal methods for the study of time-series and their fluctuations and apply these methods on secular series of cancer incidence rates
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