Detecting and estimating intensity of jumps for discretely observed \(\mathrm{ARMA}D(1,1)\) processes
From MaRDI portal
Publication:268739
DOI10.1016/j.jmva.2015.08.014zbMath1381.62246OpenAlexW2201898622MaRDI QIDQ268739
Publication date: 15 April 2016
Published in: Journal of Multivariate Analysis (Search for Journal in Brave)
Full work available at URL: https://doi.org/10.1016/j.jmva.2015.08.014
jumpsdiscrete data\(\mathrm{ARMA}D(1,1)\) processesestimation of intensityfunctional linear processes
Time series, auto-correlation, regression, etc. in statistics (GARCH) (62M10) Functional limit theorems; invariance principles (60F17)
Related Items (3)
An introduction to recent advances in high/infinite dimensional statistics ⋮ Estimating jump intensity and detecting jump instants in the context of \(p\) derivatives ⋮ Detecting instants of jumps and estimating their intensity in the context of p derivatives with continuous or discrete data
Uses Software
Cites Work
- Unnamed Item
- Unnamed Item
- Unnamed Item
- Unnamed Item
- Unnamed Item
- Unnamed Item
- Unnamed Item
- Unnamed Item
- Unnamed Item
- Unnamed Item
- Threshold selection in jump-discriminant filter for discretely observed jump processes
- Asymptotic lower bounds in estimating jumps
- Inference for functional data with applications
- A functional linear model for time series prediction with exogenous variables
- Topological structure of solution sets for impulsive differential inclusions in Fréchet spaces
- Mathematical methods for financial markets.
- Estimating the degree of activity of jumps in high frequency data
- Estimation of parameters for diffusion processes with jumps from discrete observations
- Superpositions and higher order Gaussian beams
- Curve forecasting by functional autoregression
- Linear processes in function spaces. Theory and applications
- Measurability of linear operators in the Skorokhod topology
- Nonparametric adaptive estimation for pure jump Lévy processes
- A Darling-Erdős-type CUSUM-procedure for functional data
- Exponential bounds for intensity of jumps
- Density estimation for compound Poisson processes from discrete data
- Asymptotics of prediction in functional linear regression with functional outputs
- Nonparametric density estimation in compound Poisson processes using convolution power estimators
- Functional data analysis.
- Nonparametric functional data analysis. Theory and practice.
- Approximation of epidemic models by diffusion processes and their statistical inference
- Estimation and simulation of autoregressive Hilbertian processes with exogenous variables
- Autoregressive Forecasting of Some Functional Climatic Variations
- Estimation of Jump Tails
- Poisson Approximation of Processes with Locally Independent Increments with Markov Switching
- Nonparametric Quantile Regression Estimation for Functional Dependent Data
- Modelling Electricity Futures by Ambit Fields
- Fisher's Information for Discretely Sampled Lvy Processes
- Linear diffusion with stationary switching regime
- A Test for Second-Order Stationarity and Approximate Confidence Intervals for Localized Autocovariances for Locally Stationary Time Series
- Financial Modelling with Jump Processes
- Estimating and Detecting Jumps. Applications to $$D\left[ 0,1\right $$-Valued Linear Processes]
- Inference and Prediction in Large Dimensions
- Estimation for Nonnegative Lévy-Driven Ornstein-Uhlenbeck Processes
- Tail of a linear diffusion with Markov switching
This page was built for publication: Detecting and estimating intensity of jumps for discretely observed \(\mathrm{ARMA}D(1,1)\) processes
