Advanced Spatial Modeling With Stochastic Partial Differential Equations Using R and Inla (Hardcover)

Preamble

What this book is and isn’t

1. The Integrated Nested Laplace Approximation and the R-INLA package

Introduction

The INLA method

A simple example

Additional arguments and control options

Manipulating the posterior marginals

Advanced features

2. Introduction to spatial modeling

Introduction

The SPDE approach

A toy example

Projection of the random field

Prediction

Triangulation details and examples

Tools for mesh assessment

Non-Gaussian response: Precipitation in Parana

3. More than one likelihood

Coregionalization model

Joint modeling: Measurement error model

Copying part of or the entire linear predictor

4. Point processes and preferential sampling

Introduction

Including a covariate in the log-Gaussian Cox process

Geostatistical inference under preferential sampling

5. Spatial non-stationarity

Explanatory variables in the covariance

The Barrier model

Barrier model for noise data in Albacete (Spain)

6. Risk assessment using non-standard likelihoods

Survival analysis

Models for extremes

7. Space-time models

Discrete time domain

Continuous time domain

Lowering the resolution of a spatio-temporal model

Conditional simulation: Combining two meshes

8. Space-time applications

Space-time coregionalization model

Dynamic regression example

Space-time point process: Burkitt example

Large point process dataset

Accumulated rainfall: Hurdle Gamma model