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Umweltstatistik – Enviromental Statistics

Betreuung durch:

(1) Estimating and Predicting a Spatial Process using Experimental Variogram Fitting - Schätzung und Vorhersage eines Räumlichen Prozesses unter Benutzung des Experimentalen Variograms (Master - Topic)
  • Environmental process modelling
  • Empirical Semi-Variogram Calculation
  • Fitting a Covariance Function
Cressie, N and Wikle, C. K. (2011). Statistics for Spatio-Temporal Data, Wiley Desktop Editions
Genton, M. G. (1998). Variogram Fitting by Generalized Least Squares Using an Explicit Formula for the Covariance Structure, Mathematical Geology, Vol. 30, No. 4

(2) Optimal Measurement Network Design – Minimizing the Kriging Variance - Optimales Messnetzwerkdesign – Minimierung des Kriging Fehlers (Master - Topic)
  • Environmental process modeling
  • Optimal locations of measurement stations
  • Predictions and Prediction errors
Cressie, N,(1988). Spatial prediction and ordinary kriging. Mathematical Geology,Vol. 20, No.4,405-421.
Clark, I.(1979). Practical Geostatistics. Essex, England: Applied Science Publishers
Müller, W. G. and Zimmerman, D. L. (1999). Optimal Designs for Variogram Estimation, Environmetrics, Vol. 10, 23-37

(3) Modellierung von erneuerbaren Energien – Modelling of Renewable Energy (Bachelor/Master - Topic)
  • renewable energy and the underlying physical process
  • windspeed main influence on windenergy, solar radiation and their influence on solar energy
  • regression analysis, time series, limitations
Hamilton, J. (1994). Time series analysis, Princeton University Press, New Jersey. Abdel H. El-Shaarawi and Walter W. Piegorsch: Encyclopedia of Environmetrics. Wiley, Chichester, 2002.
C. A. Glasbey and D. J. Allcroft (2008) A spatiotemporal auto-regressive moving average model for solar radiation. Journal of the Royal Statistical Society, 57, 343-355.
Haslett, J. and Raftery, A. E. (1989), Space-time Modelling with Long-memory Dependence: Assessing Ireland's Wind Power Resource. App. Statist., 38,1, 1-50.

(4) Analyse und Evaluation von Schadstoffindizes - Analyzing and Evaluating Indices of Pollutants (Bachelor/Master - Topic)
  • Bruno-Cocchi indices
  • strength and weaknesses of indices
  • time series analysis
Bruno F. and Cocchi D. (2002). A unified strategy for building simple air quality indices. Environmetrics, 13, 243-261.
Bruno. F. and Cocchi D. (2007). Recovering information from synthetic air quality indexes. Environmetrics, 18, 345-359.
Hamilton, J. (1994). Time series analysis, Princeton University Press, New Jersey.

(5) Vorhersage von Zeitreihen und deren Erklärungskraft anhand von Umweltdaten – Time Series and their Power to Forecast (Bachelor/Master - Topic)
  • time series modelling and forecasting, differences
  • prediction or forecasting
  • forecasting a suitable time series, model performance
Brockwell, P. and Davis, R. A. (1996). Introduction to time series and forecasting, Springer, New York.
Hussain, S., Elbergali, A., Al-Masri , A. and Shu, G. (2004). Parsimonious modelling, testing and forecasting of long-range dependence in wind speed, Environmetrics, 15:151-171.
Taylor, J.W., Mcsharry, P.E. and Buizza, R. (2009). Wind power density forecasting using ensemble predictions and time series models, IEEE Transactions on Energy Conversion, 24:775-782.

(6) Spectral Analysis of Time Series and their Application to Environmental Data - Spektralanalyse von Zeitreihen und deren Anwendung in Bezug auf Umweltdaten (Master - Topic)
  • deriving the spectral density
  • choice of relevant frequencies
  • time series analysis for modelling seasonal effects
Fuller, W.A. (1976). Introduction to statistical time series, Wiley, New York.
Brockwell, P. and Davis, R. A. (1996). Introduction to time series and forecasting, Springer, New York.
Hamilton, J.D. (1994). Time series analysis, Princeton University Press, New Jersey.
Alpuim, T. and El-Shaarawi, A. (2009). Modeling monthly temperature data in Lisbon and Prague, Environmetrics, 20:835-852.

(7) Non- or Semiparametric Modelling of Pollutants - Nicht- oder semiparametrische Modellierung von Schadstoffen (Master - Topic)
  • estimation of single index model
  • comparisson of semi- or nonparametric with parametric model
  • advantages and/or limitations of semi- or nonparametric models
Härdle, W., Müller, M., Sperlich, S. and Werwatz, A. (2004), Nonparametric and Semiparametric Models, Springer, New York.
Ichimura, H.(1993). Semiparametric least squares (SLS) and weighted SLS estimation of single-index model, Journal of Econometrics, 58:71-120.