Araya, H., Bahamonde, N., Fermin, L., Roa, T., & Torres, S. (2023). ON THE CONSISTENCY OF LEAST SQUARES ESTIMATOR IN MODELS SAMPLED AT RANDOM TIMES DRIVEN BY LONG MEMORY NOISE: THE JITTERED CASE. Stat. Sin., 33(1), 331–351.
Abstract: In numerous applications, data are observed at random times. Our main purpose is to study a model observed at random times that incorporates a longmemory noise process with a fractional Brownian Hurst exponent H. We propose a least squares estimator in a linear regression model with long-memory noise and a random sampling time called “jittered sampling”. Specifically, there is a fixed sampling rate 1/N, contaminated by an additive noise (the jitter) and governed by a probability density function supported in [0, 1/N]. The strong consistency of the estimator is established, with a convergence rate depending on N and the Hurst exponent. A Monte Carlo analysis supports the relevance of the theory and produces additional insights, with several levels of long-range dependence (varying the Hurst index) and two different jitter densities.
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Araya, H., Bahamonde, N., Fermin, L., Roa, T., & Torres, S. (2023). ON THE CONSISTENCY OF THE LEAST SQUARES ESTIMATOR IN MODELS SAMPLED AT RANDOM TIMES DRIVEN BY LONG MEMORY NOISE: THE RENEWAL CASE. Stat. Sin., 33(1), 1–26.
Abstract: In this study, we prove the strong consistency of the least squares estimator in a random sampled linear regression model with long-memory noise and an independent set of random times given by renewal process sampling. Additionally, we illustrate how to work with a random number of observations up to time T = 1. A simulation study is provided to illustrate the behavior of the different terms, as well as the performance of the estimator under various values of the Hurst parameter H.
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Calderon, F., Lozada, A., Morales, P., Borquez-Paredes, D., Jara, N., Olivares, R., et al. (2022). Heuristic Approaches for Dynamic Provisioning in Multi-Band Elastic Optical Networks. IEEE Commun. Lett., 26(2), 379–383.
Abstract: Multi-band elastic optical networks are a promising alternative to meet the bandwidth demand of the ever-growing Internet traffic. In this letter, we propose a family of band allocation algorithms for multi-band elastic optical networks. Employing simulation, we evaluate the blocking performance of 3 algorithms of such a family and compare their performance with the only heuristic proposed to date. Results show that the three new algorithms outperform the previous proposal, with up to one order of magnitude improvement. We expect these results to help advance the area of dynamic resource allocation in multi-band elastic optical networks.
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Carrasco, J. A., Carrasco, M., & Yanez, R. (2022). An inexpert expert. Appl. Econ. Lett., Early Access.
Abstract: We explore strategic information transmission when there is noise at the observation stage, when an expert observes signals, before he advises a policymaker. That is, the expert might be inexpert. We account for the fact that his signals might be totally uninformative, which is commonly known by players. We find that this inexpertise translates into a greater preference misalignment between players and that this yields a less informative equilibrium. We show that our results follow from the fact that the strategic effect of noise – the welfare change exclusive due to changes in the equilibrium partition – is always negative. Numerical simulations show that noise might be beneficial if the policymaker openly disagrees about noise chances. This makes the point that whether noise is beneficial or not crucially depends on how early in the game it arises, and also whether noise chances are commonly known by players or not.
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Lopatin, J. (2023). Estimation of Foliar Carotenoid Content Using Spectroscopy Wavelet-Based Vegetation Indices. IEEE Geosci. Remote. Sens. Lett., 20, 2500405.
Abstract: The plant carotenoid (Car) content plays a crucial role in the xanthophyll cycle and provides essential information on the physiological adaptations of plants to environmental stress. Spectroscopy data are essential for the nondestructive prediction of Car and other traits. However, Car content estimation is still behind in terms of accuracy compared to other pigments, such as chlorophyll (Chl). Here, I examined the potential of using the continuous wavelet transform (CWT) on leaf reflectance data to create vegetation indices (VIs). I compared six CWT mother families and six scales and selected the best overall dataset using random forest (RF) regressions. Using a brute-force approach, I created wavelet-based VIs on the best mother family and compared them against established Car reflectance-based VIs. I found that wavelet-based indices have high linear sensitivity to the Car content, contrary to typical nonlinear relationships depicted by the reflectance-based VIs. These relations were theoretically contrasted with the synthetic data created using the PROSPECT-D radiative transfer model. However, the best selection of wavelength bands in wavelet-based VIs varies greatly depending on the spectral characteristics of the input data before the transformation.
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Mora, F., Coullet, P., Rica, S., & Tirapegui, E. (2018). Numerical path integral calculation of the probability function and exit time: an application to non-gradient drift forces. Philos. Trans. R. Soc. A-Math. Phys. Eng. Sci., 376(2135), 11 pp.
Abstract: We provide numerical solutions based on the path integral representation of stochastic processes for non-gradient drift Langevin forces in the presence of noise, to follow the temporal evolution of the probability density function and to compute exit times even for arbitrary noise. We compare the results with theoretical calculations, obtaining excellent agreement in the weak noise limit. This article is part of the theme issue 'Dissipative structures in matter out of equilibrium: from chemistry, photonics and biology (part 2)'.
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