Likelihood theory for the graph Ornstein-Uhlenbeck process

DOI10.1007/s11203-021-09257-1OpenAlexW3209893466MaRDI QIDQ2144193

Valentin Courgeau, Almut E. D. Veraart

Publication date: 1 June 2022

Published in: Statistical Inference for Stochastic Processes (Search for Journal in Brave)

Full work available at URL: https://arxiv.org/abs/2005.12720

zbMATH Keywords

central limit theoremmaximum likelihood estimatorOrnstein-Uhlenbeck processesadaptive Lassomultivariate Lévy processgraphical modellingcontinuous-time likelihood

Mathematics Subject Classification ID

Inference from stochastic processes (62Mxx)

Related Items (2)

Stochastic optimization of a mixed moving average process for controlling non-Markovian streamflow environments ⋮ High-frequency estimation of the Lévy-driven graph Ornstein-Uhlenbeck process

Uses Software

• GNAR

Cites Work

• Unnamed Item
• Unnamed Item
• Unnamed Item
• Unnamed Item
• Unnamed Item
• The Adaptive Lasso and Its Oracle Properties
• Asymptotic theory for large volatility matrix estimation based on high-frequency financial data
• Optimal sparse volatility matrix estimation for high-dimensional Itô processes with measurement errors
• Statistical estimation of multivariate Ornstein-Uhlenbeck processes and applications to co-integration
• Statistics for high-dimensional data. Methods, theory and applications.
• On the definition, stationary distribution and second order structure of positive semidefinite Ornstein-Uhlenbeck type processes
• Multivariate supOU processes
• The equivalence of ergodicity and weak mixing for infinitely divisible processes
• Operator-selfdecomposable distributions as limit distributions of processes of Ornstein-Uhlenbeck type
• Ergodicity and exponential \(\beta\)-mixing bounds for multidimensional diffusions with jumps
• Absolute continuity of operator-self-decomposable distributions on \(R^ n\)
• Spectral representations of infinitely divisible processes
• Parameter estimation with exact distribution for multidimensional Ornstein-Uhlenbeck processes
• Exponential families of stochastic processes
• On multidimensional Ornstein-Uhlenbeck processes driven by a general Lévy process
• Ergodic properties of generalized Ornstein-Uhlenbeck processes
• Tail event driven networks of SIFIs
• Sparse inference of the drift of a high-dimensional Ornstein-Uhlenbeck process
• Random networks with sublinear preferential attachment: the giant component
• Asymptotic properties of an estimator of the drift coefficients of multidimensional Ornstein-Uhlenbeck processes that are not necessarily stable
• Approximate self-weighted LAD estimation of discretely observed ergodic Ornstein-Uhlenbeck processes
• Network vector autoregression
• Drift estimation for a Lévy-driven Ornstein-Uhlenbeck process with heavy tails
• Data-driven inference for stationary jump-diffusion processes with application to membrane voltage fluctuations in pyramidal neurons
• Multivariate spatial autoregressive model for large scale social networks
• Sparse covariance matrix estimation in high-dimensional deconvolution
• Efficient maximum likelihood estimation for Lévy-driven Ornstein-Uhlenbeck processes
• Non-Gaussian Ornstein–Uhlenbeck-based Models and Some of Their Uses in Financial Economics
• Stochastic Integration on the Real Line
• Ergodicity of Lévy-Type Processes
• Matrix Subordinators and Related Upsilon Transformations
• Non‐parametric Threshold Estimation for Models with Stochastic Diffusion Coefficient and Jumps
• Lévy–Driven Continuous–Time ARMA Processes
• Time Change Representation of Stochastic Integrals
• Rényi Statistics in Directed Families of Exponential Experiments*
• Ambit Stochastics
• Change of Time and Change of Measure
• Mixing conditions for multivariate infinitely divisible processes with an application to mixed moving averages and the supOU stochastic volatility model
• Estimation for Nonnegative Lévy-Driven Ornstein-Uhlenbeck Processes
• On the Existence and Uniqueness of the Real Logarithm of a Matrix
• A characterization of limiting distributions of regular estimates
• On the estimation of integrated volatility in the presence of jumps and microstructure noise
• Estimating structured high-dimensional covariance and precision matrices: optimal rates and adaptive estimation

This page was built for publication: Likelihood theory for the graph Ornstein-Uhlenbeck process