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  • 1. Agram, Nacira
    et al.
    Haadem, S.
    Øksendal, B.
    Proske, F.
    Optimal Stopping, Randomized Stopping, and Singular Control with General Information Flow2022In: Theory of Probability and its Applications, ISSN 0040-585X, E-ISSN 1095-7219, Vol. 66, no 4, p. 601-612Article in journal (Refereed)
  • 2.
    Soshnikov, A.
    et al.
    Univ Calif Davis, Davis, CA 95616 USA..
    Xu, Yuanyuan
    KTH, School of Engineering Sciences (SCI), Mathematics (Dept.), Mathematical Statistics.
    Gaussian approximation of the distribution of strongly repelling particles on the unit circle2021In: Theory of Probability and its Applications, ISSN 0040-585X, E-ISSN 1095-7219, Vol. 65, no 4, p. 588-615Article in journal (Refereed)
    Abstract [en]

    In this paper, we consider a strongly repelling model of n ordered particles {e(i theta j)}(j=0)(n-1 )with the density p(theta(0), ..., theta(n-1)) = 1/Z(n )exp { - beta/2 Sigma(j not equal k )sin(-2 )(theta(j) - theta(k)/2)}, beta > 0. Let theta(j )= 2 pi j/n + x(j)/n(2) + const such that Sigma(n-1 )(j=0)x(j )= 0. Define zeta(n) (2 pi j/n) = x(j)/root n, and extend zeta(n) piecewise linearly to [0, 2 pi]. We prove the functional convergence of zeta(n)(t) to zeta(t) = root 2/beta Re (Sigma(infinity )(k=1)1/k e(ikt) Z(k)), where Z(k ) are independent identically distributed complex standard Gaussian random variables.

  • 3. Stepanova, N.
    et al.
    Pavlenko, Tatjana
    KTH, School of Engineering Sciences (SCI), Mathematics (Dept.), Mathematical Statistics.
    Goodness-of-fit tests based on sup-functionals of weighted empirical processes2018In: Theory of Probability and its Applications, ISSN 0040-585X, E-ISSN 1095-7219, Vol. 63, no 2, p. 292-317Article in journal (Refereed)
    Abstract [en]

    A large class of goodness-of-fit test statistics based on sup-functionals of weighted empirical processes is proposed and studied. The weight functions employed are the Erdős–Feller– Kolmogorov–Petrovski upper-class functions of a Brownian bridge. Based on the result of M. Csörgő, S. Csörgő, L. Horváth, and D. Mason on this type of test statistics, we provide the asymptotic null distribution theory for the class of tests and present an algorithm for tabulating the limit distribution functions under the null hypothesis. A new family of nonparametric confidence bands is constructed for the true distribution function and is found to perform very well. The results obtained, involving a new result on the convergence in distribution of the higher criticism statistic, as introduced by D. Donoho and J. Jin, demonstrate the advantage of our approach over a common approach that utilizes a family of regularly varying weight functions. Furthermore, we show that, in various subtle problems of detecting sparse heterogeneous mixtures, the proposed test statistics achieve the detection boundary found by Yu. I. Ingster and, when distinguishing between the null and alternative hypotheses, perform optimally adaptively to unknown sparsity and size of the non-null effects. 

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