Biomedical subjects

Steve Pincus

Publications and source records attributed to Steve Pincus.

3 recordsLinked to original sources

PubMed2004

Irregularity, volatility, risk, and financial market time series.

The need to assess subtle, potentially exploitable changes in serial structure is paramount in the analysis of financial data. Herein, we demonstrate the utility of approximate entropy (ApEn), a model-independent measure of sequential irregularity, toward this goal, by several distinct applications. We consider both empirical data and models, including composite indices (Standard and Poor's 500 and Hang Seng), individual stock prices, the random-walk hypothesis, and the Black-Scholes and fractional Brownian motion models. Notably, ApEn appears to be a potentially useful marker of system stability, with rapid increases possibly foreshadowing significant changes in a financial variable.

Economics↗

PubMed1995

Approximate entropy (ApEn) as a complexity measure.

Approximate entropy (ApEn) is a recently developed statistic quantifying regularity and complexity, which appears to have potential application to a wide variety of relatively short (greater than 100 points) and noisy time-series data. The development of ApEn was motivated by data length constraints commonly encountered, e.g., in heart rate, EEG, and endocrine hormone secretion data sets. We describe ApEn implementation and interpretation, indicating its utility to distinguish correlated stochastic processes, and composite deterministic/ stochastic models. We discuss the key technical idea that motivates ApEn, that one need not fully reconstruct an attractor to discriminate in a statistically valid manner-marginal probability distributions often suffice for this purpose. Finally, we discuss why algorithms to compute, e.g., correlation dimension and the Kolmogorov-Sinai (KS) entropy, often work well for true dynamical systems, yet sometimes operationally confound for general models, with the aid of visual representations of reconstructed dynamics for two contrasting processes. (c) 1995 American Institute of Physics.

Journal Article↗

PubMedDate not supplied

Lifelong menstrual histories are typically erratic and trending: a taxonomy.

OBJECTIVE: Menstrual cycles are composites of complex events; the data describing them are correspondingly rich. We seek to quantitatively represent menstrual histories from menarche to menopause and to evaluate the clinical belief that regular and stable cycle lengths are the most normative histories. DESIGN: Using prospective data from the Tremin Trust, we classified the menstrual histories of 628 women as very stable (type I), stable but with greater variability in cycle lengths (type II), oscillating and erratic with a downward trend in cycle length (type III), oscillating and erratic with no downward trend in cycle length (type IV), or highly erratic and variable (type V). Classification criteria were created by examining basic summary statistics of menstrual cycle lengths. Specifically, we identified key features describing variability of median cycle length, the mean of the interquartile range, the consistency of the interquartile range, the slope of median cycle lengths, and the number of stable 5-year intervals between ages 15 and 45+. RESULTS: We present the first characterization of full menstrual histories. Our taxonomy captures the essential features of menstrual bleeding patterns for a heterogeneous population. Persistently stable histories (types I and II) were seen in only 28% of the women; erratic histories (types III through V) characterized 72%. When examining all participants, significant differences were seen in age at menarche (P < 0.05), age at menopause (P < 0.01), and number of births (P < 0.01) between these stable and erratic groups. CONCLUSIONS: Although clinicians have traditionally thought of "normal" menstrual histories as being regular and stable, the distribution of women in our five categories suggest that variable histories are most common. Clinically, these results may suggest the need for a paradigm shift in what gynecologists view as normal and abnormal menstrual cycle histories.

Algorithms↗