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J. H. Ramirez-Gonzalez

Publications and source records attributed to J. H. Ramirez-Gonzalez.

2 recordsLinked to original sources

Weighted Sub-fractional Brownian Motion: Covariance Structure, Path Properties and Euler Approximation

Weighted sub-fractional Brownian motion was introduced as a centered Gaussian process with covariance $$ Q_{a,b}(s,t)=\frac{1}{1-b}\int_0^{s\wedge t}u^a[(s-u)^b+(t-u)^b-(s+t-2u)^b]\,du. $$ This kernel was proved to be positive definite for $a>-1$ and $b\in[0,2]\setminus{1}$, and also for $a>-1$, $-1 0, $$ and consider the kernel $$ R_{f,b}(s,t)=\frac{1}{1-b}\int_0^{s\wedge t}f(u)[(s-u)^b+(t-u)^b-(s+t-2u)^b]\,du. $$ We prove that, for $b>-1$, $b\ne1$, the kernel $R_{f,b}$ is positive definite for every such function $f$ if and only if $b\in[0,1)\cup(1,2]$. For every $b\in(-1,0)\cup(2,\infty)$, we construct a non-negative function $f$ satisfying the integrability condition for which $R_{f,b}$ is not positive definite. The case $b=1$ is obtained as a logarithmic limit. For the associated Gaussian process, we study Hölder regularity, total and quadratic variation, non-stationarity, and long-range dependence. For $b\in(0,1)\cup(1,2]$, we also define differential equations driven by this process and establish pathwise convergence of the corresponding Euler approximation, together with strong $L^p$-rates under the additional regularity assumptions stated below. The logarithmic boundary $b=1$ is included in the covariance theory but is not part of the pathwise-equation and Euler results.

math.PR

Integral Fractional Ornstein-Uhlenbeck Process Model for Animal Movement

Modeling the trajectories of animals is challenging due to the complexity of their behaviors, the influence of unpredictable environmental factors, individual variability, and the lack of detailed data on their movements. Additionally, factors such as migration, hunting, reproduction, and social interactions add additional layers of complexity when attempting to accurately forecast their movements. In the literature, various models exits that aim to study animal telemetry, by modeling the velocity of the telemetry, the telemetry itself or both processes jointly through a Markovian process. In this work, we propose to model the velocity of each coordinate axis for animal telemetry data as a fractional Ornstein-Uhlenbeck (fOU) process. Then, the integral fOU process models position data in animal telemetry. Compared to traditional methods, the proposed model is flexible in modeling long-range memory. The Hurst parameter $H \in (0,1)$ is a crucial parameter in integral fOU process, as it determines the degree of dependence or long-range memory. The integral fOU process is nonstationary process. In addition, a higher Hurst parameter ($H > 0.5$) indicates a stronger memory, leading to trajectories with transient trends, while a lower Hurst parameter ($H < 0.5$) implies a weaker memory, resulting in trajectories with recurring trends. When H = 0.5, the process reduces to a standard integral Ornstein-Uhlenbeck process. We develop a fast simulation algorithm of telemetry trajectories using an approach via finite-dimensional distributions. We also develop a maximum likelihood method for parameter estimation and its performance is examined by simulation studies. Finally, we present a telemetry application of Fin Whales that disperse over the Gulf of Mexico.

stat.ME