Zobrazeno 1 - 10
of 73
pro vyhledávání: '"Shirota, Shinichiro"'
Spatially and temporally varying coefficient (STVC) models are currently attracting attention as a flexible tool to explore the spatio-temporal patterns in regression coefficients. However, these models often struggle with balancing computational eff
Externí odkaz:
http://arxiv.org/abs/2410.07229
Autor:
Shirota, Shinichiro, Gelfand, Alan E.
Preferential sampling provides a formal modeling specification to capture the effect of bias in a set of sampling locations on inference when a geostatistical model is used to explain observed responses at the sampled locations. In particular, it ena
Externí odkaz:
http://arxiv.org/abs/2202.09168
Autor:
Gelfand, Alan E., Shirota, Shinichiro
Joint species distribution modeling is attracting increasing attention these days, acknowledging the fact that individual level modeling fails to take into account expected dependence/interaction between species. These models attempt to capture speci
Externí odkaz:
http://arxiv.org/abs/1908.09410
A key challenge in spatial statistics is the analysis for massive spatially-referenced data sets. Such analyses often proceed from Gaussian process specifications that can produce rich and robust inference, but involve dense covariance matrices that
Externí odkaz:
http://arxiv.org/abs/1907.10109
Autor:
Gelfand, Alan. E., Shirota, Shinichiro
Presence/absence data and presence-only data are the two customary sources for learning about species distributions over a region. We illuminate the fundamental modeling differences between the two types of data. Most simply, locations are considered
Externí odkaz:
http://arxiv.org/abs/1809.01322
Autor:
Shirota, Shinichiro, Banerjee, Sudipto
The log-Gaussian Cox process is a flexible and popular class of point pattern models for capturing spatial and space-time dependence for point patterns. Model fitting requires approximation of stochastic integrals which is implemented through discret
Externí odkaz:
http://arxiv.org/abs/1802.06151
Species distribution models usually attempt to explain presence-absence or abundance of a species at a site in terms of the environmental features (socalled abiotic features) present at the site. Historically, such models have considered species indi
Externí odkaz:
http://arxiv.org/abs/1711.05646
For a given region, we have a dataset composed of car theft locations along with a linked dataset of recovery locations which, due to partial recovery, is a relatively small subset of the set of theft locations. For an investigator seeking to underst
Externí odkaz:
http://arxiv.org/abs/1701.05863
Autor:
Shirota, Shinichiro, Gelfand, Alan E.
The log Gaussian Cox process is a flexible class of point pattern models for capturing spatial and spatio-temporal dependence for point patterns. Model fitting requires approximation of stochastic integrals which is implemented through discretization
Externí odkaz:
http://arxiv.org/abs/1611.10359
Autor:
Shirota, Shinichiro, Gelfand, Alan E.
We view the locations and times of a collection of crime events as a space-time point pattern. So, with either a nonhomogeneous Poisson process or with a more general Cox process, we need to specify a space-time intensity. For the latter, we need a \
Externí odkaz:
http://arxiv.org/abs/1611.08719