Reinhard Furrer's Publications and Proceedings

[ 2007 | 2006 | 2005 | 2004 | 2003 | 2002 | 2001 | 2000 | 1999 | 1998 ]

Furrer, R. and Sain, S. R. (2008). spam: A Sparse Matrix R Package with Emphasis on MCMC Methods for Gaussian Markov Random Fields. Submitted. [Abstract]

Abstract: spam is an R package for sparse matrix algebra with emphasis on a Cholesky factorization of sparse positive definite matrices. The implemantation of spam is based on the competing philosophical maxims to be competitively fast compared to existing tools and to be easy to use, modify and extend. The first is addressed by using fast Fortran routines and the second by assuring S4 and S3 compatibility. One of the features of spam is to exploit the algorithmic steps of the Cholesky factorization and hence to perform only a fraction of the workload when factorizing matrices with the same sparseness structure. Simulations show that exploiting this break-down of the factorization results in a speed-up of about a factor 10 and memory savings of about a factor 15 for large matrices and slightly smaller factors for huge matrices. The article is motivated with Markov chain Monte Carlo methods for Gaussian Markov random fields, but many other statistical applications are mentioned that profit from an efficient Cholesky factorization as well.

Keywords: Cholesky factorization, Compactly supported covariance function, Compressed sparse row format, Symmetric positive definite matrix, Stochastic modeling, S3/S4.

BibTeX:

@ARTICLE{Furr:Sain:08b,
    AUTHOR = {Furrer, R. and Sain, S. R.},
      YEAR = {2008},
     TITLE = {spam: {A} Sparse Matrix {R} Package with Emphasis on {MCMC} Methods for {G}aussian {M}arkov Random Fields},
   JOURNAL = {Comput. Statist. Data Anal.},
  FJOURNAL = {Computational Statistics and Data Analysis},
    VOLUME = {},
    NUMBER = {},
     PAGES = {},
}

Furrer, R. and Sain, S. R. (2008). Spatial Model Fitting for Large Datasets with Applications to Climate and Microarray Problems. Statistics and Computing, doi:10.1007/s11222-008-9075-x. [Abstract] [BibTeX]

Abstract: Many problems in the environmental and biological sciences involve the analysis of large quantities of data. Further, the data in these problems are often subject to various types of structure and, in particular, spatial dependence. Traditional model fitting often fails due to the size of the datasets since it is difficult to not only specify but also to compute with the full covariance matrix describing the spatial dependence. We propose a very general type of mixed model that has a random spatial component. Recognizing that spatial covariance matrices often exhibit a large number of zero or near-zero entries, covariance tapering is used to force near-zero entries to zero. Then, taking advantage of the sparse nature of such tapered covariance matrices, backfitting is used to estimate the fixed and random model parameters. The novelty of the paper is the combination of the two techniques, tapering and backfitting, to model and analyze spatial datasets several orders of magnitude larger than those datasets typically analyzed with conventional approaches. Results will be demonstrated with two datasets. The first consists of regional climate model output that is based on an experiment with two regional and two driver models arranged in a two-by-two layout. The second is microarray data used to build a profile of differentially expressed genes relating to cerebral vascular malformations, an important cause of hemorrhagic stroke and seizures.

Keywords: Mixed effects; Backfitting; Covariance Tapering; Sparse matrices.

BibTeX:

@ARTICLE{Furr:Sain:08a,
    AUTHOR = {Furrer, R. and Sain, S. R.},
      YEAR = {2008},
     TITLE = {Spatial Model Fitting for Large Datasets with Applications to Climate and Microarray Problems},
   JOURNAL = {Statist. Comput.},
  FJOURNAL = {Statistics and Computing},
       DOI = {10.1007/s11222-008-9075-x},
}

Mendez, P. F., Furrer, R., Ford, R. and Ordóñez, F. (2008). Scaling Laws as a Tool of Materials Informatics. JOM, 60(03), 60-66, doi:10.1007/s11837-008-0036-9 [Abstract] [BibTeX]

Abstract: This paper discusses the utility of scaling laws to materials informatics and presents the algorithm Scaling LAW (SLAW), useful to obtain scaling laws from statistical data. These laws can be used to extrapolate known materials property data to untested materials by using other more readily available information. This technique is independent of a characteristic length or time scale, so it is useful for a broad diversity of problems. In some cases, SLAW can reproduce the mathematical expression that would have been obtained through an analytical treatment of the problem. This technique was originally designed for mining statistical data in materials processing and materials behavior at a system level, and it shows promise for the study of the relationship between structure and properties in materials.

BibTeX:

@ARTICLE{Mend:etal:08,
    AUTHOR = {Mendez, P. F., Furrer, R., Ford, R. and Ord\'o\~nez F.},
      YEAR = {2008},
     TITLE = {Scaling Laws as a Tool of Materials Informatics},
   JOURNAL = {JOM},
  FJOURNAL = {JOM},
    VOLUME = {60},
    NUMBER = {3},
     PAGES = {60-66},
       DOI = {http://dx.doi.org/10.1007/s11837-008-0036-9},
}

Kupper, T., de Alencastro, L. F., Gatsigazi, R., Furrer, R., Grandjean D. and Tarradellas J. (2008). Concentrations and specific loads of brominated flame retardants in sewage sludge. Chemosphere, 71(6), 1173-1180, doi:10.1016/j.chemosphere.2007.10.019. [Abstract] [BibTeX]

Abstract: Many substances related to human activities end up in wastewater and accumulate in sewage sludge. The present study focuses on two classes of brominated flame retardants: polybrominated diphenyl ethers (BDE28, BDE47, BDE49, BDE66, BDE85, BDE99, BDE100, BDE119, BDE138, BDE153, BDE154, BDE183, BDE209) and hexabromocyclododecane (HBCD) detected in sewage sludge collected from a monitoring network in Switzerland. Mean concentrations (n = 16 wastewater treatment plants) were 310, 149, 95 and 17 mu g per kg dry matter for decaBDE, HBCD, penta- and octaBDE, respectively. These numbers correspond well with other studies from European countries. DecaBDE, HBCD, penta- and octaBDE showed average specific loads (load per connected inhabitant per year) in sludge of 6.1, 3.3, 2.0 and 0.3 mg cap^-1 yr^-1, respectively. This is in line with consumption and storage of the compounds in the environment and the anthroposphere. Discrepancies observed for octaBDE and HBCD can be explained by the release from materials where these compounds are incorporated in and/or their degradation during anaerobic sludge treatment. Loads from different types of monitoring sites showed that brominated flame retardants ending up in sewage sludge originate mainly from surface runoff, industrial and domestic wastewater.

Keywords: Sources; Wastewater treatment plant; Polybrominated diphenyl ethers; Hexabromocyclododecane; Flux

BibTeX:

@ARTICLE{Kupp:etal:08,
    AUTHOR = {Kupper, T. and  De Alencastro, L.F. and Gatsigazi, R. and Furrer, R. and Grandjean, D. and Tarradellas J.},
      YEAR = {2008},
     TITLE = {Concentrations and specific loads of brominated flame retardants in sewage sludge},
   JOURNAL = {Chemosphere},
  FJOURNAL = {Chemosphere},
    VOLUME = {71},
    NUMBER = {6},
     PAGES = {1173-1180},
       DOI = {10.1016/j.chemosphere.2007.10.019},
}

2007

Contributing author to Chapter 10 Global Climate Projections of the Working Group I contribution to the Intergovernmental Panel on Climate Change Fourth Assessment Report: Climate Change 2007: The Physical Science Basis, Cambridge University Press; ISBN 0521705967/0521880092.

Furrer, R, Sain, S. R., Nychka, D. and Meehl, G. A. (2007). Multivariate Bayesian Analysis of Atmosphere-Ocean General Circulation Models. Environmental and Ecological Statistics, 14(3), 249-266, doi:10.1007/s10651-007-0018-z. [Abstract] [BibTeX]

Abstract: Numerical experiments based on atmosphere-ocean general circulation models (AOGCMs) are one of the primary tools in deriving projections for future climate change. Although each AOGCM has the same underlying partial differential equations, modelling large scale effects, they have different small scale parameterisations and different discretisations to solve the equations, resulting in different climate projections. This motivates climate projections synthesized from results of several AOGCMs' output. We combine present day observations, present day and future climate projections in a single highdimensional hierarchical Bayes model. The challenging aspect is the modeling of the spatial processes on the sphere, the number of parameters and the amount of data involved. We pursue a Bayesian hierarchical model that separates the spatial response into a large scale climate change signal and an isotropic process representing small scale variability among AOGCMs. Samples from the posterior distributions are obtained with computer-intensive MCMC simulations. The novelty of our approach is that we use gridded, high resolution data covering the entire sphere within a spatial hierarchical framework. The primary data source is provided by the Coupled Model Intercomparison Project (CMIP) and consists of 9 AOGCMs on a 2.8 by 2.8 degree grid under several different emission scenarios. In this article we consider mean seasonal surface temperature and precipitation as climate variables. Extensions for our model are also discussed.

Keywords: Climate change; Spatial process; Spherical covariance, Hierarchical model; Large datasets; MCMC.

BibTeX:

@ARTICLE{Furr:Sain:Nych:Meeh:07,
  AUTHOR = {Furrer, R. and  Sain, S. R. and Nychka, D. W. and Meehl, G. A.},
   TITLE = {Multivariate {B}ayesian Analysis of Atmosphere-Ocean General Circulation Models},
    YEAR = {2007},
 JOURNAL = {Environ. Ecol. Stat.},
FJOURNAL = {Environmental and Ecological Statistics},
  VOLUME = {14},
  NUMBER = {3},
   PAGES = {249-266},
     DOI = {10.1007/s10651-007-0018-z},
}

Furrer, R., Knutti, R., Sain, S. R., Nychka, D. W. and Meehl, G. A. (2007). Spatial patterns of probabilistic temperature change projections from a multivariate Bayesian analysis. Geophysical Research Letters, 34, L06711, doi:10.1029/2006GL027754. [Abstract] [BibTeX] [Online supplement]

Abstract: We present probabilistic projections for spatial patterns of future temperature change using a multivariate Bayesian analysis. The methodology is applied to the output from 21 global coupled climate models used for the Fourth Assessment Report of the Intergovernmental Panel on Climate Change. The statistical technique is based on the assumption that spatial patterns of climate change can be separated into a large scale signal related to the true forced climate change and a small scale signal due to model bias and variability. The different scales are represented via dimension reduction techniques in a hierarchical Bayesian model. Posterior probabilities are obtained with a Markov chain Monte Carlo simulation. We show that with 66% (90%) probability 79% (48%) of the land areas warm by more than 2^oC by the end of the century for the SRES A1B scenario.

BibTeX:

@ARTICLE{Furr:Knut:Sain:Nych:Meeh:07,
    AUTHOR = {Furrer, R. and Knutti, R. and Sain, S. R. and Nychka, D. W. and Meehl, G. A.},
     TITLE = {Spatial patterns of probabilistic temperature change projections from a multivariate {B}ayesian analysis},
      YEAR = {2007},
   JOURNAL = {Geophys. Res. Lett.},
  FJOURNAL = {Geophysical Research Letters},
    VOLUME = {34},
     PAGES = {L06711},
       DOI = {10.1029/2006GL027754},
}

Brändli, R. C., Bucheli, T. D., Kupper, T., Furrer, R., Stahel, W. A., Stadelmann, F. X. and Tarradellas, J. (2007). Organic pollutants in compost and digestate. Part 1. Polychlorinated biphenyls, polycyclic aromatic hydrocarbons and markers. Journal of Environmental Moniting, 9, 456-464, doi:10.1039/b617101j. [Abstract] [BibTeX]

Abstract: In Europe, 9.3x10⁶t_{dry weight (dw)} of compost and digestate are produced per year. Most of this is applied to agricultural land, which can lead to considerable inputs of organic pollutants, such as polychlorinated biphenyls (PCB) and polycyclic aromatic hydrocarbons (PAH) to soil. This paper presents an inventory of the pollutant situation in source-separated composts, digestates and presswater in Switzerland by a detailed analysis of over 70 samples. PCB concentrations (sum PCB 28, 52, 101, 118, 138, 153, 180) were significantly higher in urban (median: 30 mg kg_dw^-1, n = 52) than in rural samples (median: 14 mg kg_dw^-1, n = 16). Together with low concentrations in general, this points to aerial deposition on compost input material as the major contamination pathway. Enantiomeric fractions of atropisometric PCB were close to racemic. Median PAH concentration was 3010 mg kg_dw^-1 (sum 15PAH, n = 69), and one quarter of the samples exhibited concentrations above the relevant Swiss guide value for compost (4000 mg kg_dw^-1). The levels were influenced by the treatment process (digestate > compost), the season of input material collection (spring-summer > winter > autumn), the particle size (coarse-grained > fine-grained), and maturity (mature > less mature). The main source of PAH in compost was pyrogenic, probably influenced mainly by liquid fossil fuel combustion and some asphalt abrasion, as suggested by multiple linear regression. This study, together with a companion paper reporting on other organic contaminates including emerging compound classes, provides a starting point for a better risk-benefit estimation of the application of compost and digestate to agricultural soil in Switzerland.

BibTeX:

@ARTICLE{Brae:Buch:Kupp:Furr:Stah:Stad:Tarr:07,
    AUTHOR = {Br\"andli, R. C. and Bucheli, T. D. and Kupper, T. and Furrer, R. and Stahel, W. A. and Stadelmann, F. X. and Tarradellas, J.},
     TITLE = {Organic pollutants in compost and digestate.  {P}art 1. {P}olychlorinated biphenyls, polycyclic aromatic hydrocarbons and markers},
      YEAR = {2007},
   JOURNAL = {J. Environ. Monit.},
  FJOURNAL = {Journal of Environmental Moniting},
    VOLUME = {9},
     PAGES = {456-464},
       DOI = {10.1039/b617101j},
}%"

Furrer, R. and Naveau, P. (2007). Probability Weighted Moments Properties for Small Samples. Statistics and Probability Letters, 77(2), 190-195, doi:10.1016/j.spl.2006.06.009. [Abstract] [BibTeX]

Abstract: Probability weighted moments (PWM) are classically used in hydrology. Here we study their properties for small samples. Links between PWMs and the hazard rate ordering are identified. We propose PWM tail equivalences and derive explicit variances for PWM unbiased estimators.

Keywords: Generalized Pareto distribution; U-statistics

BibTeX:

@ARTICLE {Furr:Nave:07
    AUTHOR = {Furrer, R. and Naveau, P.},
     TITLE = {Probability Weighted Moments Properties for Small Samples},
   JOURNAL = {Statist. Probab. Lett.},
  FJOURNAL = {Statistics and Probability Letters},
    VOLUME = {77},
      YEAR = {2007},
    NUMBER = {2},
     PAGES = {190-195},
}

Furrer, R. and Bengtsson, T. (2007). Estimation of High-dimensional Prior and Posteriori Covariance Matrices in Kalman Filter Variants. Jounal of Multivariate Analysis, 98(2), 227-255, doi:10.1016/j.jmva.2006.08.003. [Abstract] [BibTeX]

Abstract: This work studies the effects of sampling variability in Monte Carlo-based methods to estimate very highdimensional systems. Recent focus in the geosciences has been on representing the atmospheric state using a probability density function, and, for extremely high-dimensional systems, various sample-based Kalman filter techniques have been developed to address the problem of real-time assimilation of system information and observations. As the employed sample sizes are typically several orders of magnitude smaller than the system dimension, such sampling techniques inevitably induce considerable variability into the state estimate, primarily through prior and posterior sample covariance matrices. In this article, we quantify this variability with mean squared error measures for two Monte Carlo-based Kalman filter variants: the ensemble Kalman filter and the ensemble square-root Kalman filter. Expressions of the error measures are derived under weak assumptions and show that sample sizes need to grow proportionally to the square of the system dimension for bounded error growth. To reduce necessary ensemble size requirements and to address rank-deficient sample covariances, covariance-shrinking (tapering) based on the Schur product of the prior sample covariance and a positive definite function is demonstrated to be a simple, computationally feasible, and very effective technique. Rules for obtaining optimal taper functions for both stationary as well as non-stationary covariances are given, and optimal taper lengths are given in terms of the ensemble size and practical range of the forecast covariance. Results are also presented for optimal covariance inflation. The theory is verified and illustrated with extensive simulations.

Keywords: Ensemble Kalman filter; Square-root filter; Matrix expansions; Shrinking; Tapering; Covariance boosting

BibTeX:

@ARTICLE{Furr:Beng:05,
    AUTHOR = {Furrer, R. and Bengtsson, T.},
      YEAR = {2007},
     TITLE = {Estimation of High-dimensional Prior and Posteriori Covariance Matrices in Kalman Filter Variants},
   JOURNAL = {J. Multivariate Anal.},
  FJOURNAL = {Journal of Multivariate Analysis},
    VOLUME = {98},
    NUMBER = {2},
     PAGES = {227-255},
       DOI = {10.1016/j.jmva.2006.08.003},
}

2006

Furrer, R., Genton, M. G. and Nychka, D. (2006). Covariance Tapering for Interpolation of Large Spatial Datasets. Journal of Computational and Graphical Statistics 15(3), 502-523. [Abstract] [BibTeX] [precipitation dataset, "read me"]

Abstract: Interpolation of a spatially correlated random process is used in many scientific areas. The best unbiased linear predictor, often called a kriging predictor in geostatistical science, requires the solution of a (possibly large) linear system based on the covariance matrix of the observations. In this article, we show that tapering the correct covariance matrix with an appropriate compactly supported positive definite function reduces the computational burden significantly and still leads to an asymptotically optimal mean squared error. The effect of tapering is to create a sparse approximate linear system that can then be solved using sparse matrix algorithms. Monte Carlo simulations support the theoretical results. An application to a large climatological precipitation dataset is presented as a concrete and practical illustration.

Keywords: asymptotic optimality, compactly supported covariance, kriging, large linear systems, sparse matrix.

BibTeX:

 @ARTICLE{Furr:Gent:Nych:06,
    AUTHOR = {R. Furrer and M. G. Genton and D. Nychka},
     TITLE = {Covariance Tapering for Interpolation of Large Spatial Datasets},
   JOURNAL = {2006},
    VOLUME = {15},
    NUMBER = {3},
     PAGES = {502-523},
}

Feingold, G., Furrer, R., Pilewskie, P., Remer L. A., Min, Q. and Jonsson H. (2006). Aerosol Indirect Effect Studies at Southern Great Plains during the May 2003 Intensive Operation Period. Journal of Geophysical Research. 111, D05S14, doi:10.1029/2004JD005648. [Abstract] [BibTeX]

Abstract: During May 2003 the Department of Energy's Atmospheric Radiation Measurement Program conducted an Intensive Operations Period (IOP) to measure the radiative effects of aerosol and clouds. A suite of both in situ and remote sensing measurements were available to measure aerosol and cloud parameters. This paper has three main goals: First, it focuses on comparison between in situ retrievals of the radiatively important drop effective radius r_e and various satellite, airborne, and surface remote sensing retrievals of the same parameter. On 17 May 2003, there was a fortuitous, near-simultaneous sampling of a stratus cloud by five different methods. The retrievals of r_e agree with one another to within ~20%, which is approximately the error estimate for most methods. Second, a methodology for deriving a best estimate of r_e from these different instruments, with their different physical properties and sampling volumes, is proposed and applied to the 17 May event. Third, the paper examines the response of r_e to changes in aerosol on 3 days during the experiment and examines the consistency of remote sensing and in situ measurements of the effect of aerosol on r_e. It is shown that in spite of the generally good agreement in derived r_e, the magnitude of the response of r_e to changes in aerosol is quite sensitive to the method of retrieving r_e and to the aerosol proxy for cloud condensation nuclei. Nonphysical responses are sometimes noted, and it is suggested that further work needs to be done to refine these techniques.

BibTeX:

@ARTICLE{Fein:Furr:Pile:Reme:Min:Jons:07,
    AUTHOR = {Feingold, G. and Furrer, R. and Pilewskie, P. and Remer L. A. and Min, Q. and Jonsson H.},
      YEAR = {2006},
     TITLE = {Aerosol Indirect Effect Studies at Southern Great Plains during the May 2003 Intensive Operation Period},
   JOURNAL = {J. Geophys. Res.},
  FJOURNAL = {Journal of Geophysical Research},
    VOLUME = {111},
     PAGES = {D05S14},
       DOI = {10.1029/2004JD005648},
}

Plagellat, C., Kupper, T., Furrer, R., de Alencastroa, L. F., Grandjean, D. and Tarradellas, J. (2006). Concentrations and Specific Loads of UV Filters in Sewage Sludge Originating from a Monitoring Network in Switzerland. Chemosphere, 62, 915-925, doi:10.1016/j.chemosphere.2005.05.024. [Abstract]

Abstract: Many substances related to human activities end up in wastewater and accumulate in sewage sludge. The present study focuses on the analysis of widely used UV filters 3-(4-methylbenzylidene) camphor (4-MBC), octyl-methoxycinnamate (OMC), octocrylene (OC) and octyl-triazone (OT) in sewage sludge originating from a monitoring network in Switzerland. Mean concentrations in stabilised sludge from 14 wastewater treatment plants were 1780, 110, 4840 and 5510 lg/kg dry matter for 4-MBC, OMC, OC and OT, respectively. Specific loads in sewage sludge show that UV filters originate mainly from private households, but surface runoff and industries may be considered as additional sources. This indicates that besides use for sunscreens and cosmetics UV filters might occur in plastics and other materials and be released to the environment by volatilization or leaching. Differences between the modeled per capita loads of UV filters in sewage sludge and the observed specific loads in sewage sludge are probably due to erroneous figures of production volumes, degradation and sorption during wastewater treatment as well as degradation processes during transport in the sewer or sludge treatment. Thus, further research is needed to elucidate the fate of UV filters after application and release into the environment. Other compounds used as UV filters should be included in future studies.

Keywords: Chemical analysis; Sources; Wastewater treatment plant; Sunscreen agents; UV screens; Personal care products

BibTeX:

2005

Fournier, B. and Furrer, R. (2005). Automatic Mapping in the Presence of Substitutive Errors: A Robust Kriging Approach. Extended Abstract in Automatic mapping algorithms for routine and emergency monitoring data. Report on the Spatial Interpolation Comparison (SIC2004) exercise, Dubois, G. ed. Office for Official Publications of the European Communities, Luxembourg, EUR 21595 EN, ISBN: 92-894-9400-X.

Fournier, B. and Furrer, R. (2005). Automatic Mapping in the Presence of Substitutive Errors: A Robust Kriging Approach. Applied GIS 1(2), doi:10.2104/ag050012.

Brändli, R., Kupper, T., Bucheli, T. D., Furrer, R., Stadelmann, F. X. and Tarradellas, J. (2005). Persistent Organic Pollutants in Compost and its Input Materials - A Review of Field Studies. Journal of Environmental Quality 34, 3 735-760, doi:10.2134/jeq2004.0333.

Furrer, R. (2005). Covariance Estimation under Spatial Dependence. Jounal of Multivariate Analysis, 94, 2, 366-381, doi:10.1016/j.jmva.2004.05.009. [Abstract] [BibTeX]

Abstract:

BibTeX:

@ARTICLE{Furr:05,
    AUTHOR = {Furrer, R.},
      YEAR = {2005},
     TITLE = {Covariance Estimation under Spatial Dependence},
   JOURNAL = {J. Multivariate Anal.},
  FJOURNAL = {Journal of Multivariate Analysis},
    VOLUME = {94},
    NUMBER = {2},
     PAGES = {366-381},
}

2004

Sain, S. R. and Furrer, R. (2004). Fitting Large-Scale Spatial Models with Applications to Microarray Data Analysis. Proceedings of Interface 2004: Computational Biology and Bioinformatics, Baltimore, Maryland. [Abstract] [BibTeX]

Abstract:

BibTeX:

@INPROCEEDINGS{Sain:Furr:04,
     AUTHOR = {S. R. Sain and R. Furrer},
      title = {Fitting Large-Scale Spatial Models with Applications to Microarray Data Analysis},
  BOOKTITLE = {Proceedings Interface 2004},
       YEAR = {2004},
 }

Kupper, T., Berset, J. D., Etter-Holzer, R., Furrer, R. and Tarradellas, J. (2004). Concentrations and Specific Loads of Polycyclic Musks in Sewage Sludge Originating from a Monitoring Network in Switzerland. Chemosphere, 54, 8, 1111-1120, doi:10.1016/j.chemosphere.2003.09.023. [Abstract]

Abstract:

BibTeX:

2003

Fournier, B., Furrer, R., Gsponer, T. and Restle, E.-M. (2003), Editors. Proceedings of the 13th European Young Statisticans Meeting, September 21-26, Ovronnaz, Switzerland, Stämpfli AG, ISBN 3-908152-17-8.

Naveau, P., Furrer, R. and Keckhut, P. (2003). The spatio-temporal influence of the vortex on Artic Total Column Ozone variability, in The ISI International Conference on Environmental Statistics and Health, Mateu, J., Holland, D. González-Manteiga, W. (eds), 131-140. [Abstract]

Genton, M. G. and Furrer, R. (2003). Analysis of Rainfall Data by Simple Good Sense: is Spatial Statistics Worth the Trouble?, in Mapping radioactivity in the environment - Spatial Interpolation Comparison 97, Dubois, G., Malczewski, J., and De Cort M. (eds), 45-50. [Abstract]

Genton, M. G. and Furrer, R. (2003). Analysis of Rainfall Data by Robust Spatial Statistics using S+SpatialStats, in Mapping radioactivity in the environment - Spatial Interpolation Comparison 97, G. Dubois, J. Malczewski, M. De Cort (eds), 118-129. [Abstract]

2002

Furrer, R. (2002). M-Estimation for Dependent Random Variables. Statistics and Probabability Letters, 57(4), 337-341, doi:10.1016/S0167-7152(02)00084-6. [Abstract]

Furrer, R. (2002). Aspects of Modern Geostatistics: Nonstationarity, Covariance Estimation and State-Space Decompositions. Doctoral thesis under the supervision of Prof. Stephan Morgenthaler. [Abstract] [Kurzfassung] [Résumé] [Riassunto] [BibTeX]

Abstract:

BibTeX:

@PHDTHESIS{Furr:02,
  AUTHOR = {R. Furrer},
   TITLE = {Aspects of Modern Geostatistics: Nonstationarity, Covariance Estimation and State-Space Decompositions},
  SCHOOL = {Swiss Federal Insitute of Technology},
    YEAR = {2002},
}

2001

Furrer, R. (2001). Observation-State Representation of Non Stationary Spatial Processes. Proceedings of the 12th European Young Statisticians Meeting, Jánska Dolina, Slovakia, 15.

Furrer, R. (2001). Non Parametric Estimation within Decomposed Spatial Processes. Proceedings of the International Conference of the Royal Statistical Society (RSS2001), University of Glasgow, Scotland, 51.

2000

Furrer, R. (2000). On the Implementation of the Decomposition of Spatial Processes in Matlab. Computing Science and Statistics. Vol. 32, 64-77. [Abstract]

1999

Furrer, R. (1999). Covariance Estimation under Spatial Dependence. Proceedings in Spatial Temporal Modelling and its Applictions. Edited by Mardia, K. V., Aykroyd, R. G. and Dryden, I. L. Leeds University Press, Leeds, 137-140. [Abstract]

Furrer, R. and Genton, M. G. (1999). Robust Spatial Data Analysis of Lake Geneva Sediments with S+SpatialStats. Systems Research and Information Science, Special Issue on Spatial Data: Neural Nets/Statistics, 8(4), 257-272. [Abstract]

1998

Furrer, R. (1998). Principal Component Analysis of Lake Geneva Sediments. Proceedings of the Fourth Annual Conference of the International Association for Mathematical Geology . Edited by, Buccianti, A., Nardi, G. and Potenza, R., De Frede Editore, Napoli, 421-426. [Abstract]

Genton, M. G. and Furrer, R. (1998). Analysis of Rainfall Data by Robust Spatial Statistics using S+SpatialStats. Journal of Geographic Information and Decision Analysis, Vol. 2, No. 2, 126-136. [Abstract]

Genton, M. G. and Furrer, R. (1998). Analysis of Rainfall Data by Simple Good Sense: is Spatial Statistics Worth the Trouble ?. Journal of Geographic Information and Decision Analysis, Vol. 2, No. 2, 11-17. [Abstract]

Last modified Jun 17 2008 by rfurrer@mines.edu