Sample Size, Parameter Rates and Contiguity - The i.n.n.i.d. Case
Page 1 Next
G. G. Roussas, M. G. Akritas (1978)
Δελτίο της Ελληνικής Μαθηματικής Εταιρίας
Fredrik Johansson Viklund, Alan Sola, Amanda Turner (2012)
Annales de l'I.H.P. Probabilités et statistiques
We consider a variation of the standard Hastings–Levitov model HL(0), in which growth is anisotropic. Two natural scaling limits are established and we give precise descriptions of the effects of the anisotropy. We show that the limit shapes can be realised as Loewner hulls and that the evolution of harmonic measure on the cluster boundary can be described by the solution to a deterministic ordinary differential equation related to the Loewner equation. We also characterise the stochastic fluctuations...
Chen, You-You, Zhang, Li-Xin (2010)
Journal of Inequalities and Applications [electronic only]
Juan Antonio Cuesta Albertos, Carlos Matrán Bea (1983)
Trabajos de Estadística e Investigación Operativa
In a probability space (Ω,σ,P), for α ⊂ σ a sub-σ field, in general the best approximation in L∞ by elements of L∞(α) has not a unique solution. For the election between these, we prove the convergence P-almost surely of the conditional r-means, when r → ∞, to one solution, which we call conditional mid-range. This is characterized for each ω ∈ Ω by the mid-range, of one regular conditional distribution Q(ω, ·).
Noam Berger (2012)
Journal of the European Mathematical Society
We consider models of random walk in uniformly elliptic i.i.d. random environment in dimension greater than or equal to 4, satisfying a condition slightly weaker than the ballisticity condition . We show that for every and large enough, the annealed probability of linear slowdown is bounded from above by . This bound almost matches the known lower bound of , and significantly improves previously known upper bounds. As a corollary we provide almost sharp estimates for the quenched probability...
Frank Aurzada, Thomas Simon (2007)
ESAIM: Probability and Statistics
We investigate the small deviations under various norms for stable processes defined by the convolution of a smooth function with a real SαS Lévy process. We show that the small ball exponent is uniquely determined by the norm and by the behaviour of f at zero, which extends the results of Lifshits and Simon, Ann. Inst. H. Poincaré Probab. Statist.41 (2005) 725–752 where this was proved for f being a power function (Riemann-Liouville processes). In the Gaussian case, the same generality as...
Andrei Frolov (2013)
Open Mathematics
We derive logarithmic asymptotics of probabilities of small deviations for iterated processes in the space of trajectories. We find conditions under which these asymptotics coincide with those of processes generating iterated processes. When these conditions fail the asymptotics are quite different.
Jegaraj, Terence (2009)
Electronic Communications in Probability [electronic only]
Haroon Mohamed Barakat, Mouhamed A. El-Shandidy (1990)
Commentationes Mathematicae Universitatis Carolinae
L. Egghe (1980)
Annales de l'I.H.P. Probabilités et statistiques
Jan Rataj (1987)
Časopis pro pěstování matematiky
Li, Deli, Wang, Xiangchen, Rao, M.Bhaskara (1992)
International Journal of Mathematics and Mathematical Sciences
Didier Dacunha-Castelle (1975)
Séminaire de probabilités de Strasbourg
Bose, Arup, Sen, Arnab (2007)
Electronic Communications in Probability [electronic only]
T. Downarowicz, Y. Lacroix, D. Léandri (2010)
ESAIM: Probability and Statistics
In a stationary ergodic process, clustering is defined as the tendency of events to appear in series of increased frequency separated by longer breaks. Such behavior, contradicting the theoretical “unbiased behavior” with exponential distribution of the gaps between appearances, is commonly observed in experimental processes and often difficult to explain. In the last section we relate one such empirical example of clustering, in the area of marine technology. In the theoretical part of the paper...
Sanjoy Ghosal (2013)
Applications of Mathematics
In this paper the ideas of three types of statistical convergence of a sequence of random variables, namely, statistical convergence in probability, statistical convergence in mean of order and statistical convergence in distribution are introduced and the interrelation among them is investigated. Also their certain basic properties are studied.
K. Messaoudi, G. Michaille (1994)
ESAIM: Mathematical Modelling and Numerical Analysis - Modélisation Mathématique et Analyse Numérique
Roger Fischler (1970)
Annales scientifiques de l'Université de Clermont. Mathématiques
Mario Volpato, Alberto Bressan (1978)
Rendiconti del Seminario Matematico della Università di Padova
J. C. Lootgieter (1977)
Annales de l'I.H.P. Probabilités et statistiques
Page 1 Next