IMPRS-gBGC course 'Applied statistics & data analysis' 2018
Category: Skill course
0.2 CP per course day
1. Basic statistics
1.1 Organizational issues
Date: September 11-13, 2017
Place: Seminar room B0.002 @ MPI-BGC
Planned sessions:
- 09:00 - 10:30
- 10:45 - 12:15
- 13:15 - 14:45
- 15:00 - 16:30
Instructor: Jens Schumacher
1.2 Aims and scope
The course will start with an overview of the "standard statistical toolbox", reviewing basic statistical approaches like correlation, linear regression and analysis of variance. Special emphasis will be put on test of assumptions and statistical model selection. This will naturally lead us to situations were standard assumptions are not fulfilled but the same type of questions is still to be answered.
Learn R… Here is a list of useful online resources to help you bring your R skills to a new level.
The material from the R basics course might also be useful for you.
The aim of the course is to introduce into basic statistical thinking and to enable you to look at your data statistically. Each block will be accompanied by practicals where example data are analyzed using the software package R.
1.3 Interested?
Prerequisites: Basic knowledge of a language of scientific computing: R, Matlab (exercises will be in R)
The course can be a 'stand-alone' or a preparation for the module 'Advanced statistics and data analysis'. Register here by August 23.
1.4 What you need to prepare
Bring a laptop and make sure that a recent version of R is running on it.
You can download the most recent version here: http://www.r-project.org/.
You might like >> RStudio, an open-source integrated development environment that runs on all platforms. It nicely combines console, script editor, working directory, plots etc. into a an uncluttered layout that you can easily navigate. You need to have R installed before you can use RStudio as a development environment.
Please also make sure that you can access the internet via WLAN (BGC-users, if you have a BGC-account; eduroam or BGC-guests, if you don't have an account)
1.5 Preliminary agenda
| Day | Topic |
|---|---|
| Monday, Sept 11 | |
Introduction to basic statistical tools
| |
| Tuesday, Sept 12 | |
What can be done if standard assumptions are not satisfied?
| |
| Wednesday, Sept 13 | |
Introduction into linear mixed models, basic ideas of nonparametric methods of curve estimation |
1.6 Material
Handouts by Jens Schumacher
Data by Jens Schumacher
Material for the practicals by Jens Schumacher
1.7 Feedback
The survey results are available here. Statistics and statements should not be taken as an exhaustive or exclusive list.
2. Advanced statistics and machine learning for data analysis
2.1 Organizational issues
Date: January 22-24 and 26, 2018
Place: Seminar room B0.002 @ MPI-BGC
Starting time: 9:30 a.m.
Instructors:
- Fabian Gans
- Paul Bodesheim
- Guido Kraemer
- Thomas Wutzler
- Additional people might contribute to specific subjects.
2.2 Aims and scope
The course aims at giving an overview on concepts of (some advanced) applied statistics and machine learning methods for data analysis. We will cover topics such multivariate explorations, dimensionality reduction, data visualization, multivariate predictions, and time series analysis. The doctoral candidate should obtain a broad overview on the currently used techniques, they must be able to “read” results produced by most important methods, and interpret the statistics correctly as well as with caution. We will provide the participants with perspectives offered by state-of-the-art methods and give orientations where to start their own analyses. Exercises will emphasize some techniques that we think are most suitable in the context of Earth system sciences (and depending on the demand: ecology).
Structure
Every day will contain at least one lecture on a specific topic – complemented with exercises. In addition, each participant prepares a presentation on a specific topic and acts as “expert” for this method during the course.
After the breaks, we will have 2 short presentations by the participants (see below). So far we are planning the following topics for the days:
| Day | Topic | Instructor(s) |
|---|---|---|
| Mon, Jan 22 | Concepts of (linear/nonlinear) multivariate data explorations | Guido Kraemer |
| ||
| Tue, Jan 23 | Concepts of multivariate nonlinear predictions I | Paul Bodesheim |
| ||
| Wed, Jan 24 | Concepts of multivariate nonlinear predictions II | Paul Bodesheim |
| ||
| Fri, Jan 26 | Time series analysis | Fabian Gans |
|
2.3 Interested?
Prerequisites:
- Basic knowledge of a language of scientific computing: R, Matlab
- Make use of the R course - The basics
- Either the course 'Basic statistics' or recalling the typical “statistics 1” type of lectures from university.
Exercises will be in R – the use of any other language is welcome; however support depends on the person in charge and cannot be guaranteed.
The course can be a 'stand-alone' (separate certificate) if you have a solid background in basic statistics. You can brush up your skills with the course 'Basic statistics'. Register here by December 5.
2.4 What else you need to prepare
Bring a laptop and make sure that a recent version of R is running on it.
You can download the most recent version here: http://www.r-project.org/.
Also install >> RStudio, an open-source integrated development environment that runs on all platforms. It nicely combines console, script editor, working directory, plots etc. into a an uncluttered layout that you can easily navigate. You need to have R installed before you can use RStudio as a development environment.
Please also make sure that you can access the internet via WLAN (BGC-users, if you have a BGC-account; BGC-guests, if you don't have an account).
2.5 Requirements for the assignment
All participants have to prepare a short presentation on one "unconventional" method of their choice: Every day will have a few of these presentations and we want to discuss with you about the pros and cons: Please register for one of the following topics (but feel free to add another one).
Important
- Don’t choose a technique that you know already!
- Check the list of participants below and choose a topic that has not yet been selected. Ideally, we would like to cover all topics.
Use the reference as a starting point … and note that we are not necessarily experts in the methods.
| # | Topic | Starting reference | Context | Difficulty (1-3) |
| 1 (Fabian) | Misuses of statistical analysis in climate research | von Storch, H., 1995: Misuses of statistical analysis in climate research. In H. von Storch and A. Navarra (eds.): Analysis of Climate Variability Applications of Statistical Techniques. Springer Verlag, 11-26 | General | 1 |
| 2 (Thomas) | Model validation and verification: perspectives | Environmental perspective: Oreskes N, Shrader-Frechette K & Belitz K (1994) Verification, validation, and confirmation of numerical models in the earth sciences. Science, AAAS, 263, 641 Information science perspective: Sargent R (2005) Verification and validation of simulation models. , 130-143 Philosophical perspective: Kleindorfear G & Geneshan R (1993) The philosophy of science and validation in simulation. , 50-57 | General | 1 |
| 3 (Thomas) | Model validation: metrics | Janssen P & Heuberger P (1995) Calibration of process-oriented models. Ecological Modelling, Elsevier, 83, 55-66 , 0.1016/0304-3800(95)00084-9 Taylor plot: Taylor K (2001) Summarizing multiple aspects of model performance in a single diagram. Journal of Geophysical Research, Wiley-Blackwell, 106, 7183 Kling Gupta efficiency: Gupta H, Kling H, Yilmaz K & Martinez G (2009) Decomposition of the mean squared error and NSE performance criteria: Implications for improving hydrological modelling. Journal of Hydrology, Elsevier BV, 377, 80-91 | General | 2 |
| 4 (Guido) | Small n, large p | Schäfer, J., and K. Strimmer (2005) A shrinkage approach to large-scale covariance matrix estimation and implications for functional genomics. Statist. Appl. Genet. Mol. Biol. 4, 32. | General | 3 |
| 5 (Paul) | Boosted regression trees | Elith et al. (2008) A working guide to boosted regression trees. J of Animal Ecology 77, 802-813. | Prediction | 2 |
| 6 (Paul) | Feature selection | Saeys et al. (2007) A review of feature selection techniques in bioinformatics. Bioinformatics 23 2507-2517. | Prediction | 2 |
| 7 (Fabian) | Visibility graphs for time series | Lacasa et al. PNAS 105, 4972-4975 | Time series | 1 |
| 8 (Fabian) | Visibility graphs for spatial data | de Berg, Mark; van Kreveld, Marc; Overmars, Mark; Schwarzkopf, Otfried (2000), Chapter 15: Visibility Graph", Computational Geometry (2nd ed.), Springer-Verlag, pp. 307–317 | Explorative | ? |
| 9 (Paul) | Clustering with k-means and Gaussian mixture models | Chapter 9 of Christopher M. Bishop: Pattern Recognition and Machine Learning. Springer 2006. | Classification | 1 |
| 10 (Fabian) | Detecting large spatiotemporal extreme events | Lloyd-Hughes, B., (2012) A spatiotemporal structure-based approach to drought characterization. International Journal of Climatology 32, 406–41. Zscheischler et al. (2013) Ecological Informatics 15, 66-73. | Spatiotemporal exploration | 1 |
| 11 (Paul) | Probability distributions and density estimation | Chapter 2 of Christopher M. Bishop: Pattern Recognition and Machine Learning. Springer 2006. | General | 1 |
| 12 (Paul) | Large-scale nearest neighbor search | Muja, M. & Lowe, D. G.: Scalable Nearest Neighbor Algorithms for High Dimensional Data. IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI), 2014, 36, pages 2227-2240 | General / Prediction | 1 |
| 13 (Paul) | Anomaly detection with isolation forest | Fei Tony Liu, Kai Ming Ting, and Zhi-Hua Zhou: Isolation Forest. International Conference on Data Mining (ICDM) 2008, pages 413-422 | General / Explorative | 2 |
| 14 (Fabian) | Recurrence plots | To be discussed by email* | Time series | 2-3 |
| 15 (Fabian) | Recurrence plot metrics (RQA) | To be discussed by email* | Time series | 2-3 |
| 16 (Guido) | Autoencoder | Hsieh, W.W., 2001. Nonlinear principal component analysis by neural networks. Tellus 53A: 599-615 Chapter 2 of Gorbanʹ, A.N. (Ed.), 2008. Principal manifolds for data visualization and dimension reduction, Lecture notes in computational science and engineering. Springer, Berlin ; New York. Section 2 of Hsieh, W.W., 2004. Nonlinear multivariate and time series analysis by neural network methods. Rev. Geophys. 42, RG1003. doi:10.1029/2002RG000112 | Dimensionality Reduction | 2 |
| 17 (Fabian) | What is long-range memory in time series | To be discussed by email* | Time series | 2 |
| 18 (Thomas) | Model calibration | van Oijen M, Rougier J & Smith R (2005) Bayesian calibration of process-based forest models: bridging the gap between models and data. Tree Physiol, 25, 915-927 Omlin M & Reichert P (1999) A comparison of techniques for the estimation of model prediction uncertainty. Ecological modelling, Elsevier, 115, 45-59 | Time series | 2-3 |
| 19 (Fabian) | What are surrogate data? | Venema et al. (2006) Nonlinear Processes in Geophysics 13, 449-466. http://www2.meteo.uni-bonn.de/mitarbeiter/venema/themes/surrogates/iaaft/iaaft_articles.html | Time series | 3 |
| 20 (Thomas) | How can I use bootstrapping? | Efron B & Tibshirani R (1986) Bootstrap methods for standard errors, confidence intervals, and other measures of statistical accuracy. Statistical science, Institute of Mathematical Statistics, , 54-75 , | Time series | 2 |
| 21 (Guido) | Multivariate Indicator Approaches | Wolter, K., Timlin, M., 1993. Monitoring ENSO in COADS with a seasonally adjusted principal component index. NOAA/NMC/CAC, NSSL, Oklahoma Clim. Survey, CIMMS and the School of Meteor., Univ. of Oklahoma, Norman, OK. Wolter, K., Timlin, M.S., 2011. El Niño/Southern Oscillation behaviour since 1871 as diagnosed in an extended multivariate ENSO index (MEI.ext). Int. J. Climatol. 31, 1074–1087. doi:10.1002/joc.2336 https://www.esrl.noaa.gov/psd/enso/mei/ https://www.esrl.noaa.gov/psd/enso/mei.ext/index.html | Dimensionality Reduction | 2 |
| 22 (Guido) | t-SNE | van der Maaten, L., Hinton, G., 2008. Visualizing Data using t-SNE. J. Mach. Learn. Res. 9, 2579–2605. https://lvdmaaten.github.io/tsne/ | Dimensionality Reduction | 2 |
2.6 Feedback
The survey results are available here. Statistics and statements should not be taken as an exhaustive or exclusive list.