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Semi-decentralized federated learning with client pairing for efficient mutual knowledge transfer. [PDF]
Sci RepYang D, Lee J, Choi SG.
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Moment convergence in mixed-rates Sparse-Bridge estimation
, 2014 H. Masuda, Yusuke Shimizu
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Machine learning unlocks robust convergence for chemical process simulations
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Verification of Reduced Convergence Rates
Computing, 2004zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Hu, Hsin-Yun, Li, Zi-Cai
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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
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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
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Convergence Rates of SNP Density Estimators
Econometrica, 1996The seminonparametric (SNP) density estimator, proposed by \textit{A. R. Gallant} and \textit{D. W. Nychka} [ibid. 55, 363-390 (1987; Zbl 0631.62110)], has been used for structural, reduced form, and efficient method of moments estimation in economics, finance, and the health sciences.
Fenton, Victor M, Gallant, A Ronald
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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
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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
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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
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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
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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
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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
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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
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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
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