Copertina

Evaluating the Uncertainty in Mean Residual Times: Estimators Based on Residence Times from Discrete Time Processes

Fuente: arXiv

Salvato in:

Dettagli Bibliografici
Autori principali:	Sánchez, Hernán R., Garcia, Javier
Natura:	Preprint
Pubblicazione:	2024
Soggetti:	Methodology Data Analysis, Statistics and Probability
Accesso online:	Acceder al recurso
Tags:	Aggiungi Tag Nessun Tag, puoi essere il primo ad aggiungerne!!

Documenti analoghi

Multivariate extensions of the Multilevel Best Linear Unbiased Estimator for ensemble-variational data assimilation
di: Destouches, Mayeul, et al.
Pubblicazione: (2023)

Pairwise Distance-Diffusion Analysis (PDDA): A Geometric Framework for Estimating Hurst Exponents in Multivariate Long-Memory Processes
di: Soriano, Diogo C., et al.
Pubblicazione: (2026)

Linear scaling causal discovery from high-dimensional time series by dynamical community detection
di: Allione, Matteo, et al.
Pubblicazione: (2025)

Identifying Intended Effects with Causal Models
di: Compagno, Dario
Pubblicazione: (2024)

Disentangling high order effects in the transfer entropy
di: Stramaglia, Sebastiano, et al.
Pubblicazione: (2024)

Local statistical moments to capture Kramers-Moyal coefficients
di: Wiedemann, Christian, et al.
Pubblicazione: (2024)

Identifiability and improper solutions in the probabilistic partial least squares regression with unique variance
di: Arai, Takashi
Pubblicazione: (2025)

Non-parametric finite-sample credible intervals with one-dimensional priors: a middle ground between Bayesian and frequentist intervals
di: Ritmeester, Tim
Pubblicazione: (2026)

Testing of tempered fractional Brownian motions
di: Macioszek, Katarzyna, et al.
Pubblicazione: (2025)

Variational Dimension Lifting for Robust Tracking of Nonlinear Stochastic Dynamics
di: Ashenafi, Yonatan L.
Pubblicazione: (2026)

Identifiable factor analysis for mixed continuous and binary variables based on the Gaussian-Grassmann distribution
di: Arai, Takashi
Pubblicazione: (2025)

Thermodynamically rational decision making under uncertainty
di: Daimer, Dorian, et al.
Pubblicazione: (2023)

Dissecting Spectral Granger Causality through Partial Information Decomposition
di: Faes, Luca, et al.
Pubblicazione: (2026)

Particle Filtering for Non-Deterministic Electrocardiographic Imaging
di: Lagracie, Emma, et al.
Pubblicazione: (2025)

Predictability Analysis and Prediction of Discrete Weather and Financial Time-Series Data with a Hamiltonian-Based Filter-Projection Approach
di: Kiefer, Henrik, et al.
Pubblicazione: (2024)

Least Squares as Random Walks
di: Kostinski, Alexander, et al.
Pubblicazione: (2025)

Testing maximum entropy models with e-values
di: Giuffrida, Francesca, et al.
Pubblicazione: (2025)

A statistical mechanics approach to de-biasing and uncertainty estimation in LASSO for random measurements
di: Takahashi, Takashi, et al.
Pubblicazione: (2018)

Covering Unknown Correlations in Bayesian Priors by Inflating Uncertainties
di: Koch, Lukas
Pubblicazione: (2025)

Complexity Analysis of Environmental Time Series
di: Lange, Holger, et al.
Pubblicazione: (2025)

Quantifying uncertainty in the numerical integration of evolution equations based on Bayesian isotonic regression
di: Miyatake, Yuto, et al.
Pubblicazione: (2024)

PoissonRatioUQ: An R package for band ratio uncertainty quantification
di: LeDuc, Matthew, et al.
Pubblicazione: (2026)

Accurate Memory Kernel Extraction from Discretized Time Series Data
di: Tepper, Lucas, et al.
Pubblicazione: (2024)

On the Practical Use of Blaschke Decomposition in Nonstationary Signal Analysis
di: Coifman, Ronald R., et al.
Pubblicazione: (2025)

On the statistical analysis of grouped data: when Pearson $χ^2$ and other divisible statistics are not goodness-of-fit tests
di: Algeri, Sara, et al.
Pubblicazione: (2024)

A Class of Markovian Self-Reinforcing Processes with Power-Law Distributions
di: Bulanchuk, Pavlo, et al.
Pubblicazione: (2025)

Bayesian hypergraph inference from scarce and noisy dynamical observations
di: Tang, Katerina, et al.
Pubblicazione: (2026)

A feature-based information-theoretic approach for detecting interpretable, long-timescale pairwise interactions from time series
di: Nguyen, Aria, et al.
Pubblicazione: (2024)

Resolution of Simpson's paradox via the common cause principle
di: Hovhannisyan, A., et al.
Pubblicazione: (2024)

On the choice of the non-trainable internal weights in random feature maps
di: Mandal, Pinak, et al.
Pubblicazione: (2024)

Wide-Surface Furnace for In Situ X-Ray Diffraction of Combinatorial Samples using a High-Throughput Approach
di: Cordaro, Giulio, et al.
Pubblicazione: (2026)

Bayesian Hierarchical Models and the Maximum Entropy Principle
di: Brewer, Brendon J.
Pubblicazione: (2026)

Reliable data clustering with Bayesian community detection
di: Neuman, Magnus, et al.
Pubblicazione: (2025)

When correcting for regression to the mean is worse than no correction at all
di: Fontanari, José F., et al.
Pubblicazione: (2025)

Learning dynamical systems with hit-and-run random feature maps
di: Mandal, Pinak, et al.
Pubblicazione: (2025)

Towards Continuous-time Causal Foundation Models
di: Thumm, Dennis, et al.
Pubblicazione: (2026)

A New Method for High-Resolution Dating of Radiocarbon Data: The Example of the First Three Centuries B.C
di: Fürst, Sebastian
Pubblicazione: (2025)

Fitting an Equation to Data Impartially
di: Tofallis, Chris
Pubblicazione: (2024)

Hierarchical Maximum Likelihood Estimation for Time-Resolved NMR Data
di: Bosch, Lennart H., et al.
Pubblicazione: (2025)

Proposal for the Application of Fractional Operators in Polynomial Regression Models to Enhance the Determination Coefficient $R^2$ on Unseen Data
di: Torres-Hernandez, Anthony
Pubblicazione: (2025)