Richard A. Davis

Heavy-tailed distributions, correlations, kurtosis and Taylor’s Law of fluctuation scaling

Proceedings of the Royal Society A Mathematical Physical and Engineering Sciences · 2020 · 29 citations

, 2089-2097; p. 2091) speculated that 'the dependence among [random variables, rvs] can be overwhelmed by the heaviness of their marginal tails ·· ·'. We give examples of statistical models that support this speculation. While under natural conditions the sample correlation of regularly varying (RV) rvs converges to a generally random limit, this limit is zero when the rvs are the reciprocals of powers greater than one of arbitrarily (but imperfectly) positively or negatively correlated normals.…

Are extreme value estimation methods useful for network data?

Extremes · 2019-08-09 · 26 citations

Time series estimation of the dynamic effects of disaster-type shocks

Journal of Econometrics · 2022 · 24 citations

COVID-19 cases and deaths in the United States follow Taylor’s law for heavy-tailed distributions with infinite variance

Proceedings of the National Academy of Sciences · 2022-09-12 · 10 citations

The spatial and temporal patterns of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) cases and COVID-19 deaths in the United States are poorly understood. We show that variations in the cumulative reported cases and deaths by county, state, and date exemplify Taylor's law of fluctuation scaling. Specifically, on day 1 of each month from April 2020 through June 2021, each state's variance (across its counties) of cases is nearly proportional to its squared mean of cases. COVID-19 dea…

Goodness-of-fit testing for time series models via distance covariance

Journal of Econometrics · 2020-08-10 · 9 citations