TY - JOUR
T1 - Dynamic spatio-temporal zero-inflated Poisson models for predicting capelin distribution in the Barents Sea
AU - Sugasawa, Shonosuke
AU - Nakagawa, Tomoyuki
AU - Solvang, Hiroko Kato
AU - Subbey, Sam
AU - Alrabeei, Salah
N1 - Funding Information:
S. Sugasawa is supported by Japan Society for Promotion of Science (KAKENHI) under Grant Nos. 18H03628, 20H00080, and 21H00699. T. Nakagawa is supported by Japan Society for Promotion of Science (KAKENHI) under Grant Nos. 19K14597 and 21H00699. S. Subbey and H. K. Solvang have been supported by Grants 84126—Management Strategy for the Barents Sea, and GA19-NOR-081 from the Sasakawa foundation (Norway).
Publisher Copyright:
© 2022, The Author(s) under exclusive licence to Japanese Federation of Statistical Science Associations.
PY - 2022
Y1 - 2022
N2 - We consider modeling and prediction of Capelin distribution in the Barents Sea based on zero-inflated count observation data that vary continuously over a specified survey region. The model is a mixture of two components; a one-point distribution at the origin and a Poisson distribution with spatio-temporal intensity, where both intensity and mixing proportions are modeled by some auxiliary variables and unobserved spatio-temporal effects. The spatio-temporal effects are modeled by a dynamic linear model combined with the predictive Gaussian process. We develop an efficient posterior computational algorithm for the model using a data augmentation strategy. The performance of the proposed model is demonstrated through simulation studies, and an application to the number of Capelin caught in the Barents Sea from 2014 to 2019.
AB - We consider modeling and prediction of Capelin distribution in the Barents Sea based on zero-inflated count observation data that vary continuously over a specified survey region. The model is a mixture of two components; a one-point distribution at the origin and a Poisson distribution with spatio-temporal intensity, where both intensity and mixing proportions are modeled by some auxiliary variables and unobserved spatio-temporal effects. The spatio-temporal effects are modeled by a dynamic linear model combined with the predictive Gaussian process. We develop an efficient posterior computational algorithm for the model using a data augmentation strategy. The performance of the proposed model is demonstrated through simulation studies, and an application to the number of Capelin caught in the Barents Sea from 2014 to 2019.
KW - Marine species
KW - Markov Chain Monte Carlo
KW - Poisson distribution
KW - Predictive Gaussian process
KW - Spatio-temporal distribution
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U2 - 10.1007/s42081-022-00183-x
DO - 10.1007/s42081-022-00183-x
M3 - Article
AN - SCOPUS:85141740276
SN - 2520-8764
JO - Japanese Journal of Statistics and Data Science
JF - Japanese Journal of Statistics and Data Science
ER -
