processes, non-Gaussian generalized fields, and so on), (b) structure of random fields with long-range dependence, and (c) non-Gaussian time series;

  1. Theoretical models and techniques of simulation of non-Gaussian random fields with prescribed statistical properties, for example, (a) known moments up to some order, (b) known tail behavior of multivariate probability density functions, and (c) known statistics of extremes;

  2. Extrema, sample path behavior, and geometry for non-Gaussian random processes and fields;

  3. Inference and analysis of point processes with applications to oceanographic data;

  4. Analysis of the Navier-Stokes system driven by Gaussian and non-Gaussian white noise;

  5. Analysis of random fields that appear as solutions of stochastic partial differential equations (of special interest are equations driven by non-Gaussian noise or noises over a product of time-space and location-space);

  6. Wavelet analysis of random fields with application to oceanographic problems; and

  7. Statistical problems for non-Gaussian data (see models of particular interest in 2. above): (a) modeling (model identification, parameter estimation, and so on), (b) data analysis of irregularly sampled points on a field, (c) quantile estimation from dependent stationary processes and fields, (d) estimation problems for random fields given the types of sampling or observational layouts that are typical in oceanography, and (e) estimation problems for samples from non-Gaussian random fields.



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