Statistical test for anomalous diffusion based on empirical anomaly measure for Gaussian processes
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Publication:2076159
DOI10.1016/j.csda.2021.107401OpenAlexW4200627892MaRDI QIDQ2076159
Diego Krapf, Katarzyna Maraj-Zygmąt, Grzegorz Sikora, Dawid Szarek, Agnieszka Wyłomańska
Publication date: 18 February 2022
Published in: Computational Statistics and Data Analysis (Search for Journal in Brave)
Full work available at URL: https://doi.org/10.1016/j.csda.2021.107401
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Cites Work
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- Stationary Gaussian Markov processes as limits of stationary autoregressive time series
- Calibration tests for multivariate Gaussian forecasts
- Prediction of non-stationary response functions using a Bayesian composite Gaussian process
- Parallel cross-validation: a scalable fitting method for Gaussian process models
- Maximum likelihood ratio test for the stability of sequence of Gaussian random processes
- Gaussian process methods for nonparametric functional regression with mixed predictors
- Joint asymptotics for estimating the fractal indices of bivariate Gaussian processes
- Composite likelihood estimation for a Gaussian process under fixed domain asymptotics
- Testing of fractional Brownian motion in a noisy environment
- Propagation of uncertainty in the mechanical and biological response of growing tissues using multi-fidelity Gaussian process regression
- Statistical test for fractional Brownian motion based on detrending moving average algorithm
- A phylogenetic Gaussian process model for the evolution of curves embedded in \(d\)-dimensions
- Random Walks on Lattices. II
- Fast and Exact Simulation of Complex-Valued Stationary Gaussian Processes Through Embedding Circulant Matrix
- Spatio‐Temporal Dependence Measures for Bivariate AR(1) Models with α‐Stable Noise
- Empirical anomaly measure for finite-variance processes
- Superstatistics
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