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Journal Publications 2017

  1. Biscio, C.A.N. & Lavancier, F. (2017): Contrast estimation for parametric stationary determinantal point processes. Scand. J. Stat.  44, 204–229.
  2. Coeurjolly, J.-F., Møller, J. & Waagepetersen, R.P. (2017): Palm distributions for log Gaussian Cox processes. Scand. J. Stat. 44, 192–203.
  3. Hooghoudt, J., Barroso, M. & Waagepetersen, R. (2017): Towards Bayesian inference of the spatial distribution of proteins from three-cube Förster resonance energy transfer data. Ann. Appl. Stat. 11, 1711-1737.
  4. Hug, D., Kiderlen, K. & Svane, A.M. (2017): Voronoi-based estimation of Minkowski tensors from finite point samples. Discrete Comput. Geom. 57, 545-570.
  5. Jacobsen, R.D. & Møller, J. (2017): Frequentist and Bayesian inference for Gaussian–log-Gaussian wavelet trees and statistical signal processing applications. STAT 6, 248–256.
  6. Khanmohammadi, M., Darkner, S., Nava, N., Nyengaard, J.R., Wegener, G., Popoli, M. & Sporring, J. (2017): 3D analysis of synaptic vesicle density and distribution after acute foot-shock stress by using serial section transmission electron microscopy. J. Microsc. 265, 101–110.
  7. Kiderlen, M. & Dorph-Petersen, K.-A. (2017): The Cavalieri estimator with unequal section spacing revisited. Image Anal. Stereol. 36, 135-141.
  8. Kiderlen, M. & Jensen, E.B.V. (2017): Rotation invariant valuations. In Tensor Valuations and their Applications in Stochastic Geometry and Imaging (eds. M. Kiderlen and E.B.V. Jensen), Lecture Notes in Mathematics 2177, Springer, pp. 185-212.
  9. Kiderlen, M. & Jensen, E.B.V. (2017, eds.): Tensor Valuations and their Applications in Stochastic Geometry and Imaging. Lecture Notes in Mathematics 2177. Springer.
  10. Kousholt, A. (2017): Reconstruction of n-dimensional convex bodies from surface tensors. Adv. Appl. Math. 83, 115-144.
  11. Kousholt, A., Ziegel, J.F., Kiderlen, M. & Jensen, E.B.V. (2017): Stereological estimation of mean particle volume tensors in $\mathbb{R}^3$ from vertical sections. In Tensor Valuations and their Applications in Stochastic Geometry and Imaging (eds. M. Kiderlen and E.B.V. Jensen), Lecture Notes in Mathematics 2177, Springer, pp. 423-434.
  12. Kühnel, L. & Sommer, S.H. (2017): Stochastic development regression on non-linear manifolds. In Proceedings of Information Processing in Medical Imaging: 25th International Conference (IPMI) 2017. Lecture Notes in Computer Science 10265, pp. 53-64. Springer.
  13. Kühnel, L., Sommer, S., Pai, A. & Raket, L.L. (2017): Most likely separation of intensity and warping effects in image registration. Siam J. Imaging Sci. 10, 578-601.
  14. Myllymäki, M., Mrkvička, T., Grabarnik, P., Seijo, H. & Hahn, U. (2017): Global envelope tests for spatial processes. J. Roy. Stat. Soc. B 79, 381-404.
  15. Mrkvička, T.; Myllymäki, M. & Hahn, U. (2017): Multiple Monte Carlo testing, with applications in spatial point processes. Stat. Comput. 27, 1239-1255.
  16. Møller, J. & Waagepetersen, R. (2017): Some recent developments in statistics for spatial point patterns. Ann. Rev. Stat. Appl. 4, 317-342.
  17. Prokešová, M., Dvořák, J. & Jensen, E.B.V. (2017): Two-step estimation procedures for inhomogeneous shot-noise Cox processes. Ann. Inst. Statist. Math. 69, 513-542.
  18. Rønn-Nielsen, A. & Jensen, E.B.V. (2017): Excursion sets of infinitely divisible random fields with convolution equivalent Lévy measure. J. Appl. Prob. 54, 833-851.
  19. Rønn-Nielsen, A., Sporring, J. & Jensen, E.B.V. (2017): Estimation of sample spacing in stochastic processes. Image Anal. Stereol. 36, 43-49.
  20. Sommer, S.H. & Svane, A.M. (2017): Modelling anisotropic covariance using stochastic development and sub-Riemannian frame bundle geometry. J. Geom. Mech. 9, 391-410.
  21. Svane, A.M. (2017): Valuations in image analysis. In Tensor Valuations and their Applications in Stochastic Geometry and Imaging (eds. M. Kiderlen and E.B.V. Jensen), Lecture Notes in Mathematics 2177, Springer, pp. 435-454.
  22. Svane, A.M. & Jensen, E.B.V. (2017): Rotational Crofton formulae for Minkowski tensors and some affine counterparts. Adv. Appl. Math. 91, 44-75.
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Revised 03.11.2017