RNDr. Michaela Prokešová, Ph.D.

Všechny informace a materiály ke kurzu Pravděpodobnost a matematická statistika i Statistické metody jsou v e-learningu.

Všechny informace a materiály ke kurzu Informační technologie a statistika 1 i 2 jsou v e-learningu.

Web ke kurzu Náhodné procesy https://www.karlin.mff.cuni.cz/~prokesov/TUL/TUL_NP.html

 

1. PROKEŠOVÁ, M., DVOŘÁK, J. a VEDEL JENSEN, E., 2017. Two-step estimation procedures for inhomogeneous shot-noise Cox processes. ANNALS OF THE INSTITUTE OF STATISTICAL MATHEMATICS. 69(3), 513-542. ISSN 0020-3157.
2. DVOŘÁK, J. a PROKEŠOVÁ, M., 2016. Asymptotic properties of the minimum contrast estimators for projections of inhomogeneous space-time shot-noise Cox processes. Applications of Mathematics. 61(4), 387-411. ISSN 0862-7940.
3. DVOŘÁK, J. a PROKEŠOVÁ, M., 2016. Parameter Estimation for Inhomogeneous Space-Time Shot-Noise Cox Point Processes. SCANDINAVIAN JOURNAL OF STATISTICS. 43(4), 939-961. ISSN 0303-6898.
4. BENEŠ, V., PROKEŠOVÁ, M., HELISOVÁ, K. a ZIKMUNDOVÁ, M., 2015. Space-Time Models in Stochastic Geometry. STOCHASTIC GEOMETRY, SPATIAL STATISTICS AND RANDOM FIELDS: MODELS AND ALGORITHMS. 2120, 204-231. ISSN 0075-8434.
5. PROKEŠOVÁ, M. a DVOŘÁK, J., 2014. Statistics for Inhomogeneous Space-Time Shot-Noise Cox Processes. METHODOLOGY AND COMPUTING IN APPLIED PROBABILITY. 16(2), 433-449. ISSN 1387-5841.
6. PROKEŠOVÁ, M. a VEDEL JENSEN, E., 2013. Asymptotic Palm likelihood theory for stationary point processes. ANNALS OF THE INSTITUTE OF STATISTICAL MATHEMATICS. 65(2), 387-412. ISSN 0020-3157.
7. DVOŘÁK, J. a PROKEŠOVÁ, M., 2012. MOMENT ESTIMATION METHODS FOR STATIONARY SPATIAL COX PROCESSES - A COMPARISON. KYBERNETIKA. 48(5), 1007-1026. ISSN 0023-5954.
8. PROKEŠOVÁ, M., 2011. ESTIMATORS OF THE ASYMPTOTIC VARIANCE OF STATIONARY POINT PROCESSES - A COMPARISON. KYBERNETIKA. 47(5), 678-695. ISSN 0023-5954.
9. PAWLAS, Z., KLEBANOV, L., BENEŠ, V., PROKEŠOVÁ, M., POPELÁŘ, J. a LÁNSKÝ, P., 2010. First-Spike Latency in the Presence of Spontaneous Activity. NEURAL COMPUTATION. 22(7), 1675-1697. ISSN 0899-7667.
10. PROKEŠOVÁ, M., 2010. INHOMOGENEITY IN SPATIAL COX POINT PROCESSES - LOCATION DEPENDENT THINNING IS NOT THE ONLY OPTION. IMAGE ANALYSIS & STEREOLOGY. 29(3), 133-141. ISSN 1580-3139.
11. HEINRICH, L. a PROKEŠOVÁ, M., 2010. On Estimating the Asymptotic Variance of Stationary Point Processes. METHODOLOGY AND COMPUTING IN APPLIED PROBABILITY. 12(3), 451-471. ISSN 1387-5841.
12. HELLMUND, G., PROKEŠOVÁ, M. a VEDEL JENSEN, E., 2008. LEVY-BASED COX POINT PROCESSES. ADVANCES IN APPLIED PROBABILITY. 40(3), 603-629. ISSN 0001-8678.
13. PENTTINEN, A., PROKEŠOVÁ, M., HELLMUND, G., BADDELEY, A. a VEDEL JENSEN, E., 2007. Discussion of ‘Modern statistics for spatial point processes‘. SCANDINAVIAN JOURNAL OF STATISTICS. 34(4), 685-711. ISSN 0303-6898.
14. PROKEŠOVÁ, M. a BENEŠ, V., 2006. Nonlinear filtering in spatio-temporal doubly stochastic point processes driven by OU processes. KYBERNETIKA. 42(5), 539-556. ISSN 0023-5954.
15. PROKEŠOVÁ, M., 2003. Bayesian MCMC estimation of the rose of directions. KYBERNETIKA. 39(6), 703-717. ISSN 0023-5954.

1. PROKEŠOVÁ, M., 2009. INHOMOGENEITY IN SPATIAL POINT PROCESSES - GEOMETRY VERSUS TRACTABLE ESTIMATION. Bologna: ESCULAPIO Pub. Co..
2. PROKEŠOVÁ, M., 2005. LOCALLY SCALED MODELS OF POINT PROCESSES. Bratislava: Slovak Univ Technol.
3. BENEŠ, V. a PROKEŠOVÁ, M., 2005. NONLINEAR FILTRATION IN DOUBLY STOCHASTIC POINT PROCESSES. Bratislava: Slovak Univ Technol.

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