| Document Type
|
:
|
BL
|
| Record Number
|
:
|
640520
|
| Doc. No
|
:
|
dltt
|
| Main Entry
|
:
|
Blangiardo, Marta.
|
| Title & Author
|
:
|
Spatial and spatio-temporal Bayesian models with R-INLA /\ by Marta Blangiardo and Michela Cameletti
|
| Page. NO
|
:
|
1 online resource
|
| ISBN
|
:
|
9781118950197
|
|
|
:
|
: 1118950194
|
|
|
:
|
: 9781118950210
|
|
|
:
|
: 1118950216
|
|
|
:
|
: 9781118950203
|
|
|
:
|
: 1118950208
|
|
|
:
|
: 1118326555
|
|
|
:
|
: 9781118326558
|
|
|
:
|
9781118326558
|
| Bibliographies/Indexes
|
:
|
Includes bibliographical references and index
|
| Contents
|
:
|
Introduction -- Why spatial and spatio-temporal statistics? -- Why do we use Bayesian methods for modeling spatial and spatio-temporal structures? -- Why INLA?p3 -- Datasets -- National Morbidity, Mortality, and Air Pollution Study -- Average income in Swedish municipalities -- Stroke in Sheffield -- Ship accidents -- CD4 in HIV patients -- Lip cancer in Scotland -- Suicides in London -- Brian cancer in Navarra, Spain -- Respiratory hospital admission in Turin province -- Malaria in the Gambia -- Swiss rainfall data -- Lung cancer mortality in Ohio -- Low birth weight births in Georgia -- Air pollution in Piemonte -- Introduction to R -- The R language -- R objects -- Data and session management -- Packages -- Programming in R -- Basic statistical analysis with R -- Introduction to Bayesian methods -- Bayesian philosophy -- Thomas Bayes and Simon Pierre Laplace -- Bruno de Finetti and colleagues -- After the Second World War -- The 1990s and beyond -- Basic probability elements -- What is an event? -- Probability of events -- Conditional probability -- Bayes theorem -- Prior and posterior distributions -- Bayesian inference -- Working with the posterior distribution -- Choosing the prior distribution -- Type of distribution -- Conjugacy -- Noninformative or informative prior -- Bayesian computing -- Monte Carlo integration -- Monte Carlo method for Bayesian inference -- Probability distributions and random number generation in R -- Examples of Monte Carlo simulation -- Markov chain Monte Carlo methods -- Gibbs sampler -- Metropolis - Hastings algorithm -- MCMC implementation: software and output analysis -- The integrated nested Laplace approximations algorithm -- Laplace approximation -- INLA setting: the class of latent Gaussian models -- Approximate Bayesian inference with INLA -- The R - INLA package -- How INLA works: step-by-step example -- Bayesian regression and hierarchical models -- Linear regression -- Comparing the Bayesian to the classical regression model -- Example: studying the relationship between temperature and PM10 -- Nonlinear regression: random walk -- Example: studying the relationship between average household age and income in Sweden -- Generalized linear models -- Hierarchical models -- Exchangeability -- INLA as a hierarchical model -- Hierarchical regression -- Example: a hierarchical model for studying CD4 counts in AIDS patients -- Example: a hierarchical model for studying lip cancer in Scotland -- Example: studying stroke mortality in Sheffield (UK) -- Prediction -- Model checking and selection -- Methods based on the predictive distribution -- Methods based on the deviance -- Spatial modeling -- Areal data - GMRF -- Disease mapping -- BYM model: suicides in London -- Ecological regression -- Zero-inflated models -- Zero-inflated Poisson model: brain cancer in Navarra -- Zero-inflated binomial model: air pollution and respiratory hospital admissions -- Geostatistical data -- The stochastic partial differential equation approach -- Nonstationary Gaussian field -- SPDE within R - INLA -- SPDE toy example with simulated data -- Mesh construction -- The observation or projector matrix -- More advanced operations through the inla stack function -- Spatial prediction -- Prior specification for the stationary case -- Example with simulated data -- SPDE for Gaussian response: Swiss rainfall data -- SPDE with nonnormal outcome: malaria in the Gambia -- Prior specification for the nonstationary case -- Example with simulated data -- Spatio-temporal models -- Spatio-temporal disease mapping -- Nonparametric dynamic trend -- Space-time interactions -- Spatio-temporal modeling particulate matter concentration -- Change of support -- Advanced modeling / Elias T. Krainski -- Bivariate model for spatially misaligned data -- Bivariate model for spatially misaligned data -- Joint model with Gaussian distributions -- Joint model with non-Gaussian distributions -- Semicontinuous model to daily rainfall -- Spatio-temporal dynamic models -- Dynamic model with Besag proper specification -- Dynamic model with generic specification -- Space-time model lowering the time resolution -- Spatio-temporal model
|
| Subject
|
:
|
Bayesian statistical decision theory.
|
| Subject
|
:
|
Spatial analysis (Statistics)
|
| Subject
|
:
|
Asymptotic distribution (Probability theory)
|
| Subject
|
:
|
R (Computer program language)
|
| Dewey Classification
|
:
|
519.5/42
|
| LC Classification
|
:
|
QA279.5
|
| Added Entry
|
:
|
Cameletti, Michela.
|
| Added Entry
|
:
|
Ohio Library and Information Network.
|