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268
oeFields and Probability
, 1989
"... This article contains definitions and theorems concerning basic properties of following objects:  a field of subsets of given nonempty set;  a sequence of subsets of given nonempty set;  a oefield of subsets of given nonempty set and events from this oefield;  a probability i.e. oeadditive no ..."
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This article contains definitions and theorems concerning basic properties of following objects:  a field of subsets of given nonempty set;  a sequence of subsets of given nonempty set;  a oefield of subsets of given nonempty set and events from this oefield;  a probability i.e. oe
Line Search Algorithms With Guaranteed Sufficient Decrease
 ACM Trans. Math. Software
, 1992
"... The problem of finding a point that satisfies the sufficient decrease and curvature condition is formulated in terms of finding a point in a set T (). We describe a search algorithms for this problem that produces a sequence of iterates that converge to a point in T () and that, except for pathologi ..."
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Cited by 121 (0 self)
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The problem of finding a point that satisfies the sufficient decrease and curvature condition is formulated in terms of finding a point in a set T (). We describe a search algorithms for this problem that produces a sequence of iterates that converge to a point in T () and that, except
Frames and Stable Bases for ShiftInvariant Subspaces of . . .
, 1994
"... Let X be a countable fundamental set in a Hilbert space H, and let T be the operator T : ` 2 (X) ! H : c 7! X x2X c(x)x: Whenever T is welldefined and bounded, X is said to be a Bessel sequence. If, in addition, ran T is closed, then X is a frame. Finally, a frame whose corresponding T is inje ..."
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Cited by 133 (29 self)
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Let X be a countable fundamental set in a Hilbert space H, and let T be the operator T : ` 2 (X) ! H : c 7! X x2X c(x)x: Whenever T is welldefined and bounded, X is said to be a Bessel sequence. If, in addition, ran T is closed, then X is a frame. Finally, a frame whose corresponding
Approximate Medians and other Quantiles in One Pass and with Limited Memory
, 1998
"... We present new algorithms for computing approximate quantiles of large datasets in a single pass. The approximation guarantees are explicit, and apply without regard to the value distribution or the arrival distributions of the dataset. The main memory requirements are smaller than those reported ea ..."
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Cited by 124 (2 self)
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parameter. We present the algorithms, their theoretical analysis and simulation results. 1 Introduction This article studies the problem of computing order statistics of large sequences of online or diskresident data using as little main memory as possible. We focus on computing quantiles, which
Empirical process of the squared residuals of an ARCH sequence
 The Annals of Statistics
, 2001
"... this paper we consider the ARCH(p) model defined by the equations y t = oe t " t # oe ..."
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Cited by 20 (5 self)
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this paper we consider the ARCH(p) model defined by the equations y t = oe t " t # oe
The Probability of Unique Solutions of Sequencing by Hybridization
, 1996
"... We determine the asymptotic limiting probability as m !1 that a random string of length m over some alphabet \Sigma can be determined uniquely by its substrings of length `. This is an abstraction of a problem faced when trying to sequence DNA clones by SBH. Research done while visiting Carnegie M ..."
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Cited by 31 (1 self)
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We determine the asymptotic limiting probability as m !1 that a random string of length m over some alphabet \Sigma can be determined uniquely by its substrings of length `. This is an abstraction of a problem faced when trying to sequence DNA clones by SBH. Research done while visiting Carnegie
Unrestricted Aggregate Signatures
, 2006
"... 1 Introduction Aggregate signatures. An aggregate signature (AS) scheme [7] is a digital signature scheme with theadditional property that a sequence oe1,..., oen of individual signatureshere oei is the signature, underthe underlying base signature scheme, of some message ..."
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1 Introduction Aggregate signatures. An aggregate signature (AS) scheme [7] is a digital signature scheme with theadditional property that a sequence oe1,..., oen of individual signatureshere oei is the signature, underthe underlying base signature scheme, of some message
A (4k+1)Competitive Algorithm for the KServer With Fixed Cost Excursion Problem
, 1996
"... Let M be a metric space on which we have k servers. A request is a point oe 2 M and is serviced either by moving a server to oe, with cost equal to the distance the server moved, or by conducting an excursion, with cost fi (smaller than this distance). Our aim is to service (online) any sequence o ..."
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Let M be a metric space on which we have k servers. A request is a point oe 2 M and is serviced either by moving a server to oe, with cost equal to the distance the server moved, or by conducting an excursion, with cost fi (smaller than this distance). Our aim is to service (online) any sequence
A NOTE ON THE CONVERGENCE OE MOMENTS AND THE MARIINGALE CENTR,AL LIMIT THEOREM
"... Abstract. We study the convergence of moments of squareintegrable martingales, when the martingales converge to a (mixed) normal limit. 1. Introduction and basic facts 1.1. Let Mo, D) L, be a sequence of squareintegrable local martingales defined on afiltered space (Q",F",F",P" ..."
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Abstract. We study the convergence of moments of squareintegrable martingales, when the martingales converge to a (mixed) normal limit. 1. Introduction and basic facts 1.1. Let Mo, D) L, be a sequence of squareintegrable local martingales defined on afiltered space (Q
Nonstationary Matrix Cascade Algorithms
, 1998
"... This paper gives results on weak and strong convergence in L²(IR s ) r of the cascade sequence (OE k;n ) generated by the nonstationary matrix cascade algorithm OE k;n = jM j P j h k+1 (j)OE k+1;n\Gamma1 (M \Delta \Gammaj ); where for each k = 1; 2; : : : ; h k is a finite sequence of r \Thet ..."
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This paper gives results on weak and strong convergence in L²(IR s ) r of the cascade sequence (OE k;n ) generated by the nonstationary matrix cascade algorithm OE k;n = jM j P j h k+1 (j)OE k+1;n\Gamma1 (M \Delta \Gammaj ); where for each k = 1; 2; : : : ; h k is a finite sequence of r
Results 1  10
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268