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András Bárdossy IWS Universität Stuttgart

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Präsentation zum Thema: "András Bárdossy IWS Universität Stuttgart"—  Präsentation transkript:

1 András Bárdossy IWS Universität Stuttgart
Copulas (2) András Bárdossy IWS Universität Stuttgart

2 Sampling only at a number of locations What is between ?
Spatial problems Sampling only at a number of locations What is between ? Estimate Quality of estimation Simulate realizations Geostatistics (Krige, Matheron) Mining applications Hydro and Environmental sciences

3 Geostatistics Z(x) Random function – Realisation z(xi) Assumption – „uniform continuity“ No differences are known a-priori Independent of the location – depends only on h (Semi)Variogramm  Covariance function

4 Experimental Variogramm EC

5 Point kriging Unbiasedness

6 Estimation variance using the variogram

7 Kriging equations using variogram

8 Estimation variance is an index of spatial configuration
Problems Estimation variance is an index of spatial configuration Does not depend on the local values “Best” for Gaussian distribution Symmetrical (high and low values not distinguished) Variogram estimation difficult Squared differences – skewed distribution Dominated by high values Independence of the pairs not fulfilled Strongly influenced by the marginal distribution







15 Digital elevation models – water dominated regions Contaminations
Symmetry Digital elevation models – water dominated regions Maxima and minima Contaminations Source vs Background concentrations Known but unquantified deterministic processes lead to asymmetry and non-Gaussian dependence

16 Indicator Variables Indicator variables Indicator variogram

17 Indicator variables Interpretation as probability Interpolation of the indicators Result  pdf for each location Simulation restricted to the observed range Can copulas be used to overcome some of these problems?

18 How to find such copulas ?
Spatial copulas Assumption: Multivariate copula exists for any number of points The bi-variate marginal copulas corresponding to pairs separated by a vector h are translation invariant How to find such copulas ?

19 Empirical copulas Set of pdf pairs corresponding to points separated by the vector h Generalization of the variogram Empirical density using kernel smoothing



22 Empirical copula density chloride h=5000m

23 Empirical copula density chloride h=30000m

24 Empirical copula density nitrate h=5000m

25 Empirical copula density pH h=5000m

26 Cl Variogramm



29 pH Variogramm



32 Conditional Entropy: Nitrate 3000 m and 30000m

33 Copulas and natural processes
Natural processes influence high and low values differently Erosion at high elevations Pollution is spreading not the background Weather relates the high discharges Copulas of digital elevation models: Spain – eroded old landscape Ecuador – younger but erroded Mars – eroded and meteorites

34 Copula density of the pair C8 and C9

35 Copulas of daily rainfall
601 rainfall stations in the Rhein catchment Germany Size = km2 Days with important events with good spatial coverage were selected (400 days of the period ) Spatial copulas (densities) for different distances were calculated

36 Spatial dependence – 5 km Event 70

37 Spatial dependence – 5 km Event 347

38 Spatial dependence – 5 km Event 159

39 Radarniederschlag 29. Dezember 2001 11:20-13:20

40 Copula Radarniederschlag 29. Dezember 2001 11:20-13:20

41 Requirements for a spatial copula
Stability of the multivariate marginals: which means that any multivariate marginal copula corresponding to a selected set of points should not depend on the set of other selected points used to define the multivariate copula. Wide range of dependence: a geographically close set of points should have an arbitrarily strong dependence structure, while distant points should be independent. Flexible parametrization: the multivariate copula should have a parametrization such that the dependence structure reflects the geometric position of the corresponding set of points.

42 Definition of a copula from a multivariate distribution:

43 Multivariate normal copula Derived multivariate copulas
Possibilities Multivariate normal copula Simple but symmetrical Derived multivariate copulas If g monotonic – no change of the copula If g non monotonic one can get interesting copulas

44 Normal copula Correlation = 0.85

45 Chi square Non central chi-square distribution

46 Chi square Multivariate case

47 n-dimensional Chi-square copula

48 Chi-Square Copulas

49 Gauss – Chi-square


51 V-transformed copula Transformation function: Strong dependence of the extremes if shifted to one side and partly to the middle If k=1 then it is the chi square copula

52 K=2, m=1

53 Empirical copula density chloride h=5000m

54 Parameter estimation Non independent pairs – ML Fit the rank correlation function and the asymmetry Parametric form of the covariance of the original normal Further work needed

55 For n+1 points the joint distribution is known
Interpolation For n+1 points the joint distribution is known Calculate the conditional for the unobserved point Full conditional distribution known – thus confidence intervals can be calculated Example: 4 points – corner of a unit square A: two of them with F(x)=1 two with F(x)=0 B: all with F(x)=0.5

56 Example interpolation – conditional densities m=0, k=1

57 Example interpolation – conditional densities m=1, k=3

58 Example interpolation – conditional densities normal copula




62 Validation of the conditional densities
Are the conditional densities OK ? Cross validation Calculation of the frequencies of non exceedence for the observed values Comparison with the uniform V is much better then normal or Kriging

63 Thank you ! bardossy@iws.uni-stuttgart.de
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64 Nitrat und Phosphat

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