Courses > ASDA 2017

Applied statistics & data analysis

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

  • correlation
  • linear regression
  • analysis of variance
  • model selection
Tuesday, Sept 12

What can be done if standard assumptions are not satisfied?

  • dealing with variance heterogeneity,
  • spatial and temporal autocorrelation
Wednesday, Sept 13

Introduction into linear mixed models, basic ideas of nonparametric methods of curve estimation

1.6  Participants

2.  Advanced statistics and machine learning for data analysis

2.1  Organizational issues

Date: September 25-27, 2017
Place: Seminar room B0.002/C2.011 @ MPI-BGC
Starting time: 9 a.m.
Instructors:

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)
Sept 25 Concepts of (linear/nonlinear) multivariate data explorations Guido Kraemer
  • multivariate visualizations
  • dimensionality reduction
Sept 26 Concepts of multivariate (nonlinear) predictions Paul Bodesheim
  • Regression trees + cross validation
  • ANNs
  • Gaussian processes
Sept 27 Time series analysis Fabian Gans
  • Fourier
  • Wavelets
  • SSA,
  • multivariate time series

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 September 6.

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.

http://strimmerlab.org/software/corpcor/

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*

http://www.recurrence-plot.tk/glance.php

Time series 2-3
15 (Fabian) Recurrence plot metrics (RQA) To be discussed by email*

http://www.recurrence-plot.tk/glance.php

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  Participants




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