128,419 research outputs found
Bias in Estimating Multivariate and Univariate Diffusions
Published in Journal of Econometrics, 2011, https://doi.org/10.1016/j.jeconom.2010.12.006</p
A construction of continuous-time ARMA models by iterations of Ornstein-Uhlenbeck processes
We present a construction of a family of continuous-time ARMA processes based on p iterations of the linear operator that maps a Lévy process onto an Ornstein-Uhlenbeck process. The construction resembles the procedure to build an AR(p) from an AR(1). We show that this family is in fact a subfamily of the well-known CARMA(p,q) processes, with several interesting advantages, including a smaller number of parameters. The resulting processes are linear combinations of Ornstein-Uhlenbeck processes all driven by the same L´evy process. This provides a straightforward computation of covariances, a state-space model representation and methods for estimating parameters. Furthermore, the discrete and equally spaced sampling of the process turns to be an ARMA(p, p-1) process. We propose methods for estimating the parameters of the iterated Ornstein-Uhlenbeck process when the noise is either driven by a Wiener or a more general Lévy process, and show simulations and applications to real data.Peer ReviewedPostprint (published version
On a flexible construction of a negative binomial model
This work presents a construction of stationary Markov models with
negative-binomial marginal distributions. A simple closed form expression for
the corresponding transition probabilities is given, linking the proposal to
well-known classes of birth and death processes and thus revealing interesting
characterizations. The advantage of having such closed form expressions is
tested on simulated and real data.Comment: Forthcoming in "Statistics & Probability Letters
A construction of continuous-time ARMA models by iterations of Ornstein-Uhlenbeck processes
We present a construction of a family of continuous-time ARMA processes based on p iterations of the linear operator that maps a Lévy process onto an Ornstein-Uhlenbeck process. The construction resembles the procedure to build an AR(p) from an AR(1). We show that this family is in fact a subfamily of the well-known CARMA(p,q) processes, with several interesting advantages, including a smaller number of parameters. The resulting processes are linear combinations of Ornstein-Uhlenbeck processes all driven by the same L´evy process. This provides a straightforward computation of covariances, a state-space model representation and methods for estimating parameters. Furthermore, the discrete and equally spaced sampling of the process turns to be an ARMA(p, p-1) process. We propose methods for estimating the parameters of the iterated Ornstein-Uhlenbeck process when the noise is either driven by a Wiener or a more general Lévy process, and show simulations and applications to real data.Peer ReviewedPostprint (published version
Generalizing the first-difference correlated random walk for marine animal movement data
Animal telemetry data are often analysed with discrete time movement models
assuming rotation in the movement. These models are defined with equidistant
distant time steps. However, telemetry data from marine animals are observed
irregularly. To account for irregular data, a time-irregularised
first-difference correlated random walk model with drift is introduced. The
model generalizes the commonly used first-difference correlated random walk
with regular time steps by allowing irregular time steps, including a drift
term, and by allowing different autocorrelation in the two coordinates. The
model is applied to data from a ringed seal collected through the Argos
satellite system, and is compared to related movement models through
simulations. Accounting for irregular data in the movement model results in
accurate parameter estimates and reconstruction of movement paths. Measured by
distance, the introduced model can provide more accurate movement paths than
the regular time counterpart. Extracting accurate movement paths from uncertain
telemetry data is important for evaluating space use patterns for marine
animals, which in turn is crucial for management. Further, handling irregular
data directly in the movement model allows efficient simultaneous analysis of
several animals
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