@article{GlebovKiselevTarkhanov2010,
  author    = {Glebov, Sergei and Kiselev, Oleg and Tarkhanov, Nikolai Nikolaevich},
  title     = {Weakly nonlinear dispersive waves under parametric resonance perturbation},
  issn      = {0022-2526},
  doi       = {10.1111/j.1467-9590.2009.00460.x},
  year      = {2010},
  abstract  = {We consider a solution of the nonlinear Klein-Gordon equation perturbed by a parametric driver. The frequency of parametric perturbation varies slowly and passes through a resonant value, which leads to a solution change. We obtain a new connection formula for the asymptotic solution before and after the resonance.},
  language  = {en}
}
@phdthesis{Hayn2010,
  author    = {Hayn, Michael},
  title     = {Wavelet analysis and spline modeling of geophysical data on the sphere},
  address   = {Potsdam},
  pages     = {95 S. : graph. Darst.},
  year      = {2010},
  language  = {en}
}
@unpublished{Roelly2010,
  author    = {Roelly, Sylvie},
  title     = {Unas propiedades basicas de procesos de ramificaci{\´o}n : Lectures held at ICIMAF La Habana, Cuba, 2009 and 2010},
  url       = {http://nbn-resolving.de/urn:nbn:de:kobv:517-opus-49620},
  year      = {2010},
  abstract  = {Aus dem Inhalt: 1. Unas propiedades de los procesos de Bienaym{\´e}-Galton-Watson de tiempo dis- creto (BGW) 2. Unas propiedades del proceso BGW de tiempo continuo 3. Limites de procesos de BGW cuando la poblaci{\´o}n es numerosa},
  language  = {mul}
}
@article{StepanenkoTarkhanov2010,
  author    = {Stepanenko, Victor and Tarkhanov, Nikolai Nikolaevich},
  title     = {The Cauchy problem for Chaplygin's system},
  issn      = {1747-6933},
  doi       = {10.1080/17476930903394978},
  year      = {2010},
  abstract  = {We discuss the Cauchy problem for the so-called Chaplygin system which often appears in gas, aero- and hydrodynamics. This system can be thought of as a nonlinear analogue of the Cauchy-Riemann system in the plane. We pose Cauchy data on a part of the boundary and apply variational approach to construct a solution to this ill-posed problem. The problem actually gives insight to fundamental questions related to instable problems for nonlinear equations.},
  language  = {en}
}
@article{BaerBessa2010,
  author    = {B{\"a}r, Christian and Bessa, C. Pacelli},
  title     = {Stochastic completeness and volume growth},
  issn      = {0002-9939},
  doi       = {10.1090/S0002-9939-10-10281-0},
  year      = {2010},
  abstract  = {It was suggested in 1999 that a certain volume growth condition for geodesically complete Riemannian manifolds might imply that the manifold is stochastically complete. This is motivated by a large class of examples and by a known analogous criterion for recurrence of Brownian motion. We show that the suggested implication is not true in general. We also give counterexamples to a converse implication.},
  language  = {en}
}
@unpublished{LaeuterRamadan2010,
  author    = {L{\"a}uter, Henning and Ramadan, Ayad},
  title     = {Statistical Scaling of Categorical Data},
  url       = {http://nbn-resolving.de/urn:nbn:de:kobv:517-opus-49566},
  year      = {2010},
  abstract  = {Estimation and testing of distributions in metric spaces are well known. R.A. Fisher, J. Neyman, W. Cochran and M. Bartlett achieved essential results on the statistical analysis of categorical data. In the last 40 years many other statisticians found important results in this field. Often data sets contain categorical data, e.g. levels of factors or names. There does not exist any ordering or any distance between these categories. At each level there are measured some metric or categorical values. We introduce a new method of scaling based on statistical decisions. For this we define empirical probabilities for the original observations and find a class of distributions in a metric space where these empirical probabilities can be found as approximations for equivalently defined probabilities. With this method we identify probabilities connected with the categorical data and probabilities in metric spaces. Here we get a mapping from the levels of factors or names into points of a metric space. This mapping yields the scale for the categorical data. From the statistical point of view we use multivariate statistical methods, we calculate maximum likelihood estimations and compare different approaches for scaling.},
  language  = {en}
}
@phdthesis{Ramadan2010,
  author    = {Ramadan, Ayad},
  title     = {Statistical model for categorical data},
  address   = {Potsdam},
  pages     = {iv, 100 S. : graph. Darst.},
  year      = {2010},
  language  = {en}
}
@phdthesis{Kammanee2010,
  author    = {Kammanee, Athassawat},
  title     = {Some inverse potential problems},
  address   = {Potsdam},
  pages     = {XIV, 128 S.},
  year      = {2010},
  language  = {en}
}
@phdthesis{Pornsawad2010,
  author    = {Pornsawad, Pornsarp},
  title     = {Solution of nonlinear inverse ill-posed problems via Runge-Kutta methods},
  address   = {Potsdam},
  pages     = {104 S.},
  year      = {2010},
  language  = {en}
}
@phdthesis{Chi2010,
  author    = {Chi, Nguyen Phuong},
  title     = {Research on improvement of contents and methods of teaching the elements of probability and statistics in teh Vietnamese upper-secondary school},
  address   = {Potsdam},
  pages     = {272 S. : graph. Darst.},
  year      = {2010},
  language  = {en}
}