Research

Keywords & Phrases

Statistical Theory & Methods: nonparametric curve estimation, integral transforms, goodness-of-fit tests, time series, spatial processes, nonstationary & locally stationary processes, irregularly spaced data, ratio of means estimation, statistical applications of large deviations

Some applications: change points & bump hunting (e.g. rapid climate change research), age-depth relations (palaeo environmental research), regime change & multivariate dependence among species populations (e.g. palaeo ecology), species count problems (spatial ecology, biodiversity), correlation models for random fields & their applications (e.g. properties of background processes that are decisive of species occurrence and how these are reflected in the shapes of species-area curves - spatial ecology; space-time quantile maps - e.g. statistically estimated precipitation quantile maps showing heavy precipitation zones or drought zones and their changes over time, etc.), statistical issues in large scale environmental monitoring, extreme quantiles & related inference (quantification of risk, risk maps), data mining (e.g. analysis of massive space-time data sets - such as lattice processes to model total column ozone amounts on a global scale; multivariate statistical analysis for large vegetation databases - as for instance uncovering simple structures in very high dimensional species data vectors)

Projects

• Nonparametric smoothing problems in palaeo research: In palaeo-environmental research, stable isotope ratios of oxygen are used as temperature proxies and have been proven to be useful for quantifying temperature changes of the past. Similarly, charcoal records serve as proxies for fire events whereas pollen assemblages are used to assess presence of tree species in the region. One issue is that strong fluctuations in the environmental conditions may lead to plant species becoming extinct or abundant. Thus the range of variability in the vegetation response over time, as well as how this has been influenced by fluctuations in the environmental conditions in the past are of considerable interest.

Collaborators: Patricia Menendez (WSL, ETH: 2005-2008. Doctoral thesis 2008, ETHZ; now at Biometris, Wageningen, NL) ; Institute for Plant Sciences, University of Bern, Willy Tinner & Brigitta Ammann ; University of Bergen, John Birks ; Seminar für Statistik, ETH, Zürich, Hans Rudolf Künsch. Funding: Swiss National Science Foundation (PI: Sucharita Ghosh).

Some publications:

• Menendez Galvan, P. (2009) Statistical Tools for Palaeo Data. Diss. ETH No 18060: 134 S. [Includes analysis of GRIP oxygen isotope data & pollen records from Switzerland]
• Menendez, P. & Ghosh, S. (2006) On some nonparametric smoothing methods for assessing climate change. Proceedings of the Joint Statistical Meetings (JSM), August 6 - 10, 2006.
• Menendez, P., Ghosh, S., Künsch, H., Tinner, W. On estimating trend and its derivatives with long-range dependent data from palaeo cores. Manuscript.
• Menendez, P., Ghosh, S., Beran, J. (2010) On rapid change points under long memory. Journal of Statistical Planning and Inference, 140: 3343-3354. [Includes analysis of GRIP oxygen isotope data]
  
• Ghosh, S. (2006)  Regression based age estimates of a stratigraphic isotope sequence in Switzerland. Journal of Vegetation History and Archaeobotany, 15: 273-278.

• Quantile estimation for time series with applications to the Swiss precipitation records: Due to the adverse impacts of extreme weather phenomena in practically all spheres of life, there is an increasing interest in modeling long term stochastic variations in climate events. This aim of this project is to develop some nonparametric estimation & prediction methods for assessing changing precipitation patterns in Switzerland through estimation and prediction of the probability distributions of precipitation events.

Collaborators:
Dana Draghicescu (WSL, EPFL:1999-2002. Doctoral thesis 2002, EPFL; now at Hunter College, CUNY, USA). Institute for Climate Research, ETH, Christoph Frei; now at Meteoswiss, Zurich. Department of Mathematics, Applied Statistics Group, EPFL, Lausanne, Stephan Morgenthaler. Funding: Swiss National Science Foundation (PI: Sucharita Ghosh).

Some publications:

• Draghicescu, D. (2002) Nonparametric quantile estimation for depepdent data. Ph.D. thesis, EPFL. [Includes analysis of long-term Swiss precipitation records; source MeteoSwiss]

• Draghicescu, D., Ghosh, S. (2003) Smooth nonparametric quantiles. In, Proceedings of the 2nd International colloquium of Mathematics in Engineering and Numerical Physics (MENP-2), Geometry Balkan Press, Bucharest, Romania 2003, pp. 45-52. [Includes analysis of long-term Swiss precipitation records; source MeteoSwiss]

• Ghosh, S., Draghicescu, D. (2001) Quantile estimation to assess extreme climate events. Geophysical Research Abstracts, Volume 3, 2001, European Geophysical Society, Nice, France, 25-30 March, 2001.

• Ghosh, S., Draghicescu, D. (2002) An algorithm for optimal bandwidth selection for smooth nonparametric quantiles and distribution functions. In, Statistics in Industry and Technology: Statistical Data Analysis based on the L₁-norm and related methods, Birkhäuser Verlag, Basel, Switzerland, pp. 161-168. [Includes analysis of long-term Swiss precipitation records; source MeteoSwiss]

• Ghosh, S., Draghicescu, D. (2002) Predicting the distribution function for long-memory processes. International Journal of Forecasting 18: 283-290. [Includes analysis of long-term Swiss precipitation records; source MeteoSwiss]

• Time series modeling & analysis with environmental and other applications: Ongoing research among statisticians on statistical methods for time series and spatio-temporal data. The topics include developing models and tests that would distinguish between deterministic and spurious patterns, data driven methods, as well as merging of physical models with empirical evidences so as to bridge gaps between theory and data.

Some publications:

• Ghosh, S. (2001) Nonparametric trend estimation in replicated time series. Journal of Statistical Planning and Inference, Vol. 97, 263-274.
• Beran, J., Ghosh, S, Sibbertsen, P. (2003) Nonparametric M-estimation with long-memory errors. Journal of Statistical Planning and Inference 117: 199-205. [Includes analysis of hourly wind speed maxima in Zurich in 1999; source MeteoSwiss]
• Ghosh, S., Beran, J., Heiler, S., Percival, D., Tinner, W. (2007) Memory, non-stationarity and trend: analysis of environmental time series. In Kienast, F., Wildi, O., Ghosh, S. (Eds.) A Changing World: Challenges for Landscape Research. Springer Verlag, Netherlands.
• Ghosh, S. Normality testing for a long-memory sequence using the empirical moment generating function. Manuscript [Includes analysis of global temperature series (source: University of East Anglia]
• Beran, J., Feng, Y., Ghosh, S., Kulik, R. (In preparation) Text Book on Long Memory Time Series Analysis.

• Large scale multivariate and spatial data modeling & analysis:

Some publications:

• Ghosh, S., Graf, U., Ecker, K., Wildi, O., Küchler, H., Feldmeyer-Christie, E., Küchler, M. Dimension reduction and data sharpening in Swiss mires. Manuscript. [Includes multivariate analysis & nonparametric density estimation of species data from Swiss mires]
• Ghosh, S. (2009) The unseen species number revisited. Sankhya, The Indian Journal of Statistics 71-B, 2: 137-150. [Includes analysis of vascular plant species data; source: BDM]
• Beran, J., Ghosh, S., Schell, D. (2009) Least square estimation for stationary lattice processes with long-memory. Journal of Multivariate Analysis, 100: 2178-2194. [Includes analysis of global total column ozone amounts; source: NASA's Ozone Processing Team]
• Ghosh, S., Wildi, O. (2007) Statistical analysis of landscape data: space-for-time, probability surfaces and discovering species. In Kienast, F., Wildi, O., Ghosh, S. (Eds.) A Changing World: Challenges for Landscape Research. Springer Verlag, Netherlands.
• Ghosh, S., Landmann, G., Pierrat, J.C., Müler-Edzards C. (1997) Spatio-temporal variation in defoliation. In: 10 years forest condition monitoring in Europe. Studies on temporal development, spatial distribution, and impacts of natural and anthropogenic stress factors. Geneva and Brussels, United Nations Economic Commission for Europe / European Commission. eds. C. Müler-Edzards, W. De Vries and J. Willem Erisman. pp. 35-50.