Nonstationary stochastic processes, time series, data analysis
Turning point location, functional data, feature extraction, nonparametric estimation.
Mathematical methods and models in time series data analytics.
Spectral and wavelet analysis, multiscale variance decomposition.
Functional data: reduction of dimensionality, nonparametric estimation of trend (mean and confidence band).
Feature extraction (maxima, minima and their locations) in nonstationary time series and test of significance.
Collaborations and database: Enea Frascati, Assessment of essential climate variables: subsurface and surface temperature, subsurface salinity, Ocean Heat fluxes, subsurface currents.
Research Unit on “Food Science and human Nutrition”- Department of Experimental Medicine “Sapienza” University of Rome
Variable selection by recursive partitioning of multifrequency human impedance data.
Cardiology Laboratory of Clinical Department University La Sapienza Rome: Detection and treatment of electrocardiogram: rest, stress test, Holter. Heart rate variability of normal and pathologic condition (atrial fibrillation, heart transplant, aging, stress).
 Cammarota, C. Estimating the turning point location in shifted exponential model of time series, Journal of Applied Statistics, 44:7, 1269-1281 (2017)
 C. Cammarota, M. Curione, Trend Extraction in Functional Data of Amplitudes of R and T Waves in Exercise Electrocardiogram, Fluctuation and Noise Letters, 2017 on line (doi: 10.1142/S0219477517500146)