Handbook of Bayesian variable selection
MG Tadesse, M Vannucci - 2021 - books.google.com
Bayesian variable selection has experienced substantial developments over the past 30
years with the proliferation of large data sets. Identifying relevant variables to include in a …
years with the proliferation of large data sets. Identifying relevant variables to include in a …
Insurance risk assessment in the face of climate change: Integrating data science and statistics
V Lyubchich, NK Newlands, A Ghahari… - Wiley …, 2019 - Wiley Online Library
Local extreme weather events cause more insurance losses overall than large natural
disasters. The evidence is provided by long‐term observations of weather and insurance …
disasters. The evidence is provided by long‐term observations of weather and insurance …
Assessing present and future risk of water damage using building attributes, meteorology, and topography
C Heinrich-Mertsching, JC Wahl… - Journal of the Royal …, 2023 - academic.oup.com
Weather-related risk makes the insurance industry inevitably concerned with climate and
climate change. Buildings hit by pluvial flooding is a key manifestation of this risk, giving rise …
climate change. Buildings hit by pluvial flooding is a key manifestation of this risk, giving rise …
The risk typology of healthcare access and its association with unmet healthcare needs in Asian Americans
Using data from the 2015 Asian American Quality of Life Survey (N= 2,609), latent profile
analysis was conducted on general (health insurance, usual place for care and income) and …
analysis was conducted on general (health insurance, usual place for care and income) and …
Spatial variable selection methods for investigating acute health effects of fine particulate matter components
Multi‐site time series studies have reported evidence of an association between short term
exposure to particulate matter (PM) and adverse health effects, but the effect size varies …
exposure to particulate matter (PM) and adverse health effects, but the effect size varies …
Performance evaluation of automotive dealerships using grouped mixture of regressions
Abstract Finite Mixture of Regressions (FMR) are among the most widely used models for
dealing with heterogeneity in regression problems. FMR is a model-based clustering …
dealing with heterogeneity in regression problems. FMR is a model-based clustering …
Bayesian Image-on-Scalar Regression with a Spatial Global-Local Spike-and-Slab Prior
Z Zeng, M Li, M Vannucci - Bayesian Analysis, 2024 - projecteuclid.org
Bayesian Image-on-Scalar Regression with a Spatial Global-Local Spike-and-Slab Prior Page
1 Bayesian Analysis (2024) 19, Number 1, pp. 235–260 Bayesian Image-on-Scalar …
1 Bayesian Analysis (2024) 19, Number 1, pp. 235–260 Bayesian Image-on-Scalar …
Disease mapping of zero-excessive mesothelioma data in Flanders
Purpose To investigate the distribution of mesothelioma in Flanders using Bayesian disease
mapping models that account for both an excess of zeros and overdispersion. Methods The …
mapping models that account for both an excess of zeros and overdispersion. Methods The …
Extreme value modelling of water-related insurance claims
This paper considers the dependence between weather events, for example, rainfall or
snow-melt, and the number of water-related property insurance claims. Weather events …
snow-melt, and the number of water-related property insurance claims. Weather events …
Spatiotemporal multivariate mixture models for Bayesian model selection in disease mapping
It is often the case that researchers wish to simultaneously explore the behavior of, and
estimate the overall risk for, multiple related diseases with varying rarity while accounting for …
estimate the overall risk for, multiple related diseases with varying rarity while accounting for …