Dennis Sun, Stanford University, Summer 2015
The following is the schedule for Summer 2015.
| Theme | Monday | Wednesday | Friday | |
|---|---|---|---|---|
| Introduction and Review | What is spatial and temporal data? Pitfalls of linear regression | Three justifications for OLS: BLUE, MLE, MMSE. | Autocovariance function, generalized least squares | |
| Lecture Slides | Lecture Notes | Lecture Slides | ||
| Covariance Modeling | Estimating the covariance [Quiz 1] |
Kriging and prediction | No Class: Independence Day | |
| Lecture Slides Reference: Cressie Ch 1 |
Lecture Slides Reference: Cressie Ch 2-4 |
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| Autoregressive Processes | AR processes in time | AR processes in space | Models for Non-Gaussian Data | |
| Lecture Slides | Lecture Slides Reference: Cressie Ch 6-7 |
Lecture Slides Reference: Sherman Ch 4, Besag (1974) |
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| Bayesian Methods | The Bayesian Paradigm | Gibbs sampling and Bayesian computations | Diagnostics and Model Checking | |
| Lecture Slides JAGS: Model, R Code |
Lecture Slides Reference: Banerjee et al: Basics of Bayes, Bayes for Spatial Data |
Lecture Slides JAGS: Model, R Code |
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| Special Topics | Kernel Methods and Poisson Processes | |||
| Lecture Slides Reference: Diggle: Statistical Analysis of Spatial and Spatio-Temporal Point Patterns |
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| Introduction to ArcGIS (Guest Lecture by Pooja Loftus) | ||||
| Lecture Slides |