Results 221 to 230 of about 268,187 (266)
Some of the next articles are maybe not open access.
Rate of Convergence to a Stable Law
SIAM Journal on Applied Mathematics, 2001The paper deals with the densities of normalized sums of i.i.d. random variables with stable limit law. Under various conditions when the tails are finally of Pareto type (or mixtures of them) the authors introduce uniform rates for the convergence of the densities to the stable densities.
Joseph B. Keller, Rachel Kuske
openaire +2 more sources
On the Rate of Convergence of the Core
International Economic Review, 1979The classical theorem of Debreu and Scarf [1963] asserts that the core of a replicated pure exchange economy tends to the set of its competitive allocations as the index k of the replication (the number of traders of each type) tends to infinity.
openaire +2 more sources
The Best Rate of Convergence of the Core
International Economic Review, 1983It has been demonstrated by \textit{R. J. Aumann} [Econometrica 43, 611-646 (1975; Zbl 0325.90082)] that the rate of convergence of the core can be arbitrarily slow unless some conditions are imposed on the limit economy. In the present paper an open set of limit economies is exhibited so that for each economy in the open set, a sequence of economies ...
openaire +1 more source
1990
In this chapter, the rate of convergence of the algorithm to its ODE and/or to the desired value θ* is described in more detail. The analysis is again asymptotic.
Albert Benveniste +2 more
openaire +1 more source
In this chapter, the rate of convergence of the algorithm to its ODE and/or to the desired value θ* is described in more detail. The analysis is again asymptotic.
Albert Benveniste +2 more
openaire +1 more source
1997
The traditional definition of rate of convergence refers to the asymptotic properties of normalized errors about the limit point \( \bar \theta \). If e n = e for the Robbins—Monro algorithm, it is concerned with the asymptotic properties of \( U_n^ \in = \left( {\theta _n^ \in - \bar \theta } \right)/\sqrt \in \) for large n and small ∈.
Harold J. Kushner, G. George Yin
openaire +1 more source
The traditional definition of rate of convergence refers to the asymptotic properties of normalized errors about the limit point \( \bar \theta \). If e n = e for the Robbins—Monro algorithm, it is concerned with the asymptotic properties of \( U_n^ \in = \left( {\theta _n^ \in - \bar \theta } \right)/\sqrt \in \) for large n and small ∈.
Harold J. Kushner, G. George Yin
openaire +1 more source
The convergence rate of the MDM algorithm
The 2012 International Joint Conference on Neural Networks (IJCNN), 2012In this paper we will describe a simple proof of a linear convergence rate for the MDM algorithm that solves the Minimum Norm Problem (MNP). Linear convergence rates have been shown for the SMO algorithm, but the proofs require specific assumptions and are rather involved. We will follow a different approach, with a more geometric flavor.
Jorge López Lázaro +1 more
openaire +1 more source
2015
In this chapter, we study the local rate of convergence of r n (x) to r(x). We obtain full information on the first asymptotic term of r n (x) − r(x), and are rewarded with (i) a central limit theorem for r n (x) − r(x), and (ii) a way of helping the user decide how to choose the weights v ni of the estimate.
Gérard Biau, Luc Devroye
openaire +1 more source
In this chapter, we study the local rate of convergence of r n (x) to r(x). We obtain full information on the first asymptotic term of r n (x) − r(x), and are rewarded with (i) a central limit theorem for r n (x) − r(x), and (ii) a way of helping the user decide how to choose the weights v ni of the estimate.
Gérard Biau, Luc Devroye
openaire +1 more source
1996
This chapter gives some results on rates of convergence of M-estimators, including maximum likelihood estimators and least-squares estimators. We first state an abstract result, which is a generalization of the theorem on rates of convergence in Chapter 3.2, and next discuss some methods to establish the maximal inequalities needed for the application ...
Aad W. van der Vaart, Jon A. Wellner
openaire +1 more source
This chapter gives some results on rates of convergence of M-estimators, including maximum likelihood estimators and least-squares estimators. We first state an abstract result, which is a generalization of the theorem on rates of convergence in Chapter 3.2, and next discuss some methods to establish the maximal inequalities needed for the application ...
Aad W. van der Vaart, Jon A. Wellner
openaire +1 more source
Convergence rate on periodic gossiping
Information Sciences, 2016zbMATH Open Web Interface contents unavailable due to conflicting licenses.
F. He, S. Mou, J. Liu, A.S. Morse
openaire +2 more sources
1978
In Section 7.1, rate of convergence is defined, and our approach to the rate problem discussed. The rates are developed (in Section 7.3) for three separate cases, two forms of the basic KW procedure and the basic RM procedure. These algorithms are discussed in Section 7.1 and are put into a form which will be useful in the subsequent development.
Harold J. Kushner, Dean S. Clark
openaire +1 more source
In Section 7.1, rate of convergence is defined, and our approach to the rate problem discussed. The rates are developed (in Section 7.3) for three separate cases, two forms of the basic KW procedure and the basic RM procedure. These algorithms are discussed in Section 7.1 and are put into a form which will be useful in the subsequent development.
Harold J. Kushner, Dean S. Clark
openaire +1 more source