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Author (up) Araya, H.; Plaza-Vega, F. doi  openurl
  Title Parameter estimation for fractional power type diffusion: A hybrid Bayesian-deep learning approach Type
  Year 2023 Publication Communications In Statistics-Theory And Methods Abbreviated Journal Commun. Stat.-Theory Methods  
  Volume Early Access Issue Pages  
  Keywords Parameter estimation; power-type fractional diffusion; fractional Brownian motion; ABC  
  Abstract In this article, we consider the problem of parameter estimation in a power-type diffusion driven by fractional Brownian motion with Hurst parameter in (1/2,1). To estimate the parameters of the process, we use an approximate bayesian computation method. Also, a particular case is addressed by means of variations and wavelet-type methods. Several theoretical properties of the process are studied and numerical examples are provided in order to show the small sample behavior of the proposed methods.  
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  Corporate Author Thesis  
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  Language Summary Language Original Title  
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  Series Volume Series Issue Edition  
  ISSN 0361-0926 ISBN Medium  
  Area Expedition Conference  
  Notes WOS:001120573700001 Approved  
  Call Number UAI @ alexi.delcanto @ Serial 1919  
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Author (up) Plaza-Vega, F.; Araya, H. doi  openurl
  Title Anchovy (Engraulis ringens) and Pacific sardine (Sardinops sagax) variability changes in northern Chile associated with the environment and inter species synchronicity: GARCH model with exogenous variable and hybrid Bayesian deep learning estimation approach Type
  Year 2024 Publication Progress in Oceanography Abbreviated Journal Prog. Oceanogr.  
  Volume 221 Issue Pages 103190  
  Keywords Small; pelagic; environment; GARCH; variability; exogenous variables; parameters; estimation  
  Abstract Small pelagic fish species, such as anchovy (Engraulis ringens) and Pacific sardine (Sardinops sagax), play a crucial role in marine ecosystems worldwide as they serve as an important food source for higher-order predators, such as seabirds, marine mammals, and larger fish species; also from their high productivity in terms of fishery landings, they help with maintaining food security. However, small pelagic populations are known for their variability, with fluctuations in their distribution and abundance influenced by interactions with environmental and human-related factors in different spatio-temporal scales. This study aims to investigate the variability in anchovy and sardine populations in northern Chile and their potential environmental drivers by using a class of models that address the changes in variability (i.e. variance) over time, namely Generalized autoregressive conditional heteroskedasticity (GARCH) models. In particular, this work considers a GARCH model that includes an additional term in the variance, that could help to better model the variability fluctuations of both anchovy and sardine, explained by environmental factors. Since there is no estimation procedures for those type of models, we propose a hybrid Approximate Bayesian Computation (ABC) procedure that involves the use of Deep Learning structures for estimating the parameters of the model and obtain posterior distributions. The results are two-fold: First, the proposal of a new time series model that better explain conditional variance with exogenous variables and a novel estimation procedure, and second, a novel approach for establishing explicit models that address the variability of small pelagic fisheries and their interaction with the environment.  
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  Corporate Author Thesis  
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  Series Editor Series Title Abbreviated Series Title  
  Series Volume Series Issue Edition  
  ISSN 0079-6611 ISBN Medium  
  Area Expedition Conference  
  Notes WOS:001158438200001 Approved  
  Call Number UAI @ alexi.delcanto @ Serial 1938  
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