Program

Schedule.

 

 

Thusday 14th

Friday 15th

Basic concepts of Time Series Analynis

(stationarity, ARMA models and forecasting)

9am to 12am

9am to 12am

2pm to 5pm

2pm to 5pm

 

Monday 18th

Tuesday 19th

Wednesday 20th

Thusday 21th

Friday 22th

Adv. Time Series An.

9am to 12am

2pm to 5pm

9am to 12am

2pm to 5pm

9am to 12am

Geostatistics

2pm to 5pm

9am to 12am

2pm to 5pm

9am to 12am

2pm to 5pm

Program.

Advanced Time Series Analysis (Vincent Lefieux):

Basic concepts of Time Series Analynis (stationarity, ARMA models and forecasting)

– Reminders on stationarity and ARMA models.

– Multivariate Time Series Analysis, including: VAR models, Cointegration, State-Space Model.

– Introduction to Spatial Time Series.

Geostatistics (Jean-Yves Dauxois):

– Spatial processes, Stationarity, Intrinsic Processes, Variogram, Models of Variogram, Statistical Inference.

– Kriging: Simple Kriging, Ordinary Kriging and Universal Kriging.

Prerequisites.

Probability.

Linear Models.

Inferential Statistics.

Basic concepts in Time Series Analysis.

Basic concepts in R software.

References.

Advanced Time Series Analysis:

– Hamilton, J.D. (1994). Time series analysis. Princeton University Press.

– Lütkepohl, H. (2005). New introduction to multiple time series analysis. Springer.

– Tsay, R.S. (2014). Multivariate time series analysis with R and financial applications. Wiley.

Geostatistics :

– Chilès, J.P. and P. Delfiner (1999). Geostatistics. Wiley, New York.

– Diggle, P.J. and Ribeiro, P.J. (2007). Model-based Geostatistics. Springer.

– Guyon, X. (1995). Random Field on a network. Springer, New York.

Download Lecture:

1. Mini-course-Dauxois.pdf.

2. Mini-course-Dauxois-2.pdf

3. TimesSeriesIntroduction.R

4. TP_Geostat_Rmd.html

5. SlidesLefieux.zip