22 papers · ranked by Valyu relevance
Pedro Carpena, Manuel Gómez-Extremera, Pedro A. Bernaola-Galván, Paolo Castiglioni
'Paolo Castiglioni'] Detrended Fluctuation Analysis (DFA) has become a standard method to quantify the correlations and scaling properties of real-world complex time series. For a given scale ℓ of observation, DFA provides the function $F(ℓ)$, which quantifies the fluctuations of the time series around the local trend…
Yoshiki Kaneoke, Tomohiro Donishi, Jun Iwatani, Satoshi Ukai + 3 more
'Kazuhiro Shinosaki' 'Masaki Terada' 'Carles Soriano-Mas'] Slow (<0.1 Hz) oscillatory activity in the human brain, as measured by functional magnetic imaging, has been used to identify neural networks and their dysfunction in specific brain diseases. Its intrinsic properties may also be useful to investigate brain…
Salvatore Miccichè
Many natural phenomena exhibit a stochastic nature that one attempts at modeling by using stochastic processes of different types. In this context, often one is interested in investigating the memory properties of the natural phenomenon at hand. This is usually accomplished by computing the autocorrelation function of…
Min Yu, Hang-Hyun Jo
higher-order temporal correlations: An exponential case Authors: ['Min Yu' 'Hang-Hyun Jo'] Temporal correlations in the time series observed in various systems have been characterized by the autocorrelation function. Such correlations can be explained by heavy-tailed interevent time distributions as well as by…
Pedro Carpena, Pedro A. Bernaola-Galván, Manuel Gómez-Extremera, Ana V. Coronado
'Ana V. Coronado'] > The observable outputs of many complex dynamical systems consist in time series exhibiting autocorrelation functions of great diversity of behaviors, including long-range power-law autocorrelation functions, as a signature of interactions operating at many temporal or spatial scales. Often…
Christen H. Fleming, Justin M. Calabrese
Estimation of autocorrelations and spectral densities is of fundamental importance in many fields of science, from identifying pulsar signals in astronomy to measuring heart beats in medicine. In circumstances where one is interested in specific autocorrelation functions that do not fit into any simple families of…
Marlou Nadine Perquin, Marieke K. van Vugt, Craig Hedge, Aline Bompas
Human performance shows substantial endogenous variability over time, and this variability is a robust marker of individual differences. Of growing interest to psychologists is the realisation that variability is not fully random, but often exhibits temporal dependencies. However, their measurement and interpretation…
Li Su, Michael J Daniels
In long-term follow-up studies, irregular longitudinal data are observed when individuals are assessed repeatedly over time but at uncommon and irregularly spaced time points. Modeling the covariance structure for this type of data is challenging, as it requires specification of a covariance function that is positive…
Christoph Bandt
The study of order patterns of three equally-spaced values $x_{t},x_{(t+d)},x_{(t+2d)}$ in a time series is a powerful tool. The lag d is changed in a wide range so that the differences of the frequencies of order patterns become autocorrelation functions. Similar to a spectrogram in speech analysis, four ordinal…
Soroosh Afyouni, Stephen M. Smith, Thomas E. Nichols
The dependence between pairs of time series is commonly quantified by Pearson’s correlation. However, if the time series are themselves dependent (i.e. exhibit temporal autocorrelation), the effective degrees of freedom (EDF) are reduced, the standard error of the sample correlation coefficient is biased, and Fisher’s…
Johan Medrano, Abderrahmane Kheddar, Sofiane Ramdani
Correlation coefficients play a pivotal role in quantifying linear relationships between random variables. Yet, their application to time series data is very challenging due to temporal dependencies. This paper introduces a novel approach to estimate the statistical significance of correlation coefficients in time…
Authors not listed
Quantities calculated from molecular simulations are often subject to an initial bias due to unrepresentative starting configurations. Initial data are usually discarded to reduce bias. Chodera's method for automated truncation point selection [J. Chem. Theory Comput. 2016, 12, 4, 1799–1805] is popular but has not been…
Gabriel Riegner, Samuel Davenport, Bradley Voytek, Armin Schwartzman
Brain activity unfolds over hierarchical timescales that reflect how brain regions integrate and process information, linking functional and structural organization. While timescale studies are prevalent, existing estimation methods rely on the restrictive assumption of exponentially decaying autocorrelation and only…
Authors not listed
Accurate and efficient simulation of vibrational Raman spectra for systems with strong anharmonicity and nuclear quantum effects remains challenging. Herein, we apply the recently developed constrained nuclear–electronic orbital (CNEO) framework to simulate Raman spectra. We implement analytic static polarizabilities…
Nils Damaschke, Volker Kühn, Holger Nobach
Signal processing of uniformly spaced data from stationary stochastic processes with missing samples is investigated. Besides randomly and independently occurring outliers also correlated data gaps are investigated. Non-parametric estimators for the mean value, the signal variance, the autocovariance and…
Holger Nobach
To derive the auto-covariance function from a sampled and time-limited signal or the cross-covariance function from two such signals, the mean values must be estimated and removed from the signals. If no a priori information about the correct mean values is available and the mean values must be derived from the time…
Armin P. Schoech, Omer Weissbrod, Luke J. O’Connor, Nick Patterson + 3 more
Most models of complex trait genetic architecture assume that signed causal effect sizes of each SNP (defined with respect to the minor allele) are uncorrelated with those of nearby SNPs, but it is currently unknown whether this is the case. We develop a new method, autocorrelation LD regression (ACLR), for estimating…
Gabriel Riegner, Samuel Davenport, Bradley Voytek, Armin Schwartzman
Brain activity unfolds over hierarchical timescales that reflect how brain regions integrate and process information, linking functional and structural organization. While timescale studies are prevalent, existing estimation methods rely on the restrictive assumption of exponentially decaying temporal autocorrelation…
Andraž Matkovič, Alan Anticevic, John D. Murray, Grega Repovš
Functional connectivity (FC) of blood-oxygen-level-dependent (BOLD) fMRI time series can be estimated using methods that differ in sensitivity to the temporal order of time points (static vs. dynamic) and the number of regions considered in estimating a single edge (bivariate vs. multivariate). Previous research…
Dominic Edelmann, Konstantinos Fokianos, Maria Pitsillou
The concept of distance covariance/correlation was introduced recently to characterize dependence among vectors of random variables. We review some statistical aspects of distance covariance/correlation function and we demonstrate its applicability to time series analysis. We will see that the auto-distance…
Amelia Carolina Sparavigna
In an article by Thibault et al., 2002, we can find measurements of Raman linewidths in the Q branch of carbon monoxide, for mixtures with Argon at different temperatures. A plot is available for the Q(5) line with a fitted Voigt function. Here we show that a q-Gaussian Tsallis function can be used for fitting this…
Authors not listed
This study presents a validation and refinement of the “yellow cards” error detection workflow that can be applied to any property connected to molecular structure. In our implementation the workflow employed 5 predictive models with each assigning a “yellow card” to 5% of the entries with worst prediction accuracy.…