The Experts below are selected from a list of 3396 Experts worldwide ranked by ideXlab platform
Andrzej Okolewski - One of the best experts on this subject based on the ideXlab platform.
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on fatou type lemma for monotone moments of Weakly Convergent random variables
Statistics & Probability Letters, 2004Co-Authors: Marek Kaluszka, Andrzej OkolewskiAbstract:Sufficient conditions for convergence of monotone moments of Weakly Convergent random variables, concerning the rate of convergence, are given. They are often more convenient than the necessary and sufficient uniform integrability condition. Some asymptotic evaluations for inverse moments are presented.
Tao Wang - One of the best experts on this subject based on the ideXlab platform.
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generalized krasnoselskii mann type iteration for nonexpansive mappings in banach spaces
Journal of the Operations Research Society of China, 2021Co-Authors: Youcai Zhang, Ke Guo, Tao WangAbstract:The Krasnoselskii–Mann iteration plays an important role in the approximation of fixed points of nonexpansive mappings, and it is well known that the classic Krasnoselskii–Mann iteration is Weakly Convergent in Hilbert spaces. The weak convergence is also known even in Banach spaces. Recently, Kanzow and Shehu proposed a generalized Krasnoselskii–Mann-type iteration for nonexpansive mappings and established its convergence in Hilbert spaces. In this paper, we show that the generalized Krasnoselskii–Mann-type iteration proposed by Kanzow and Shehu also converges in Banach spaces. As applications, we proved the weak convergence of generalized proximal point algorithm in the uniformly convex Banach spaces.
P H Algoet - One of the best experts on this subject based on the ideXlab platform.
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Weakly Convergent nonparametric forecasting of stationary time series
arXiv: Statistics Theory, 2008Co-Authors: Gusztav Morvai, Sidney Yakowitz, P H AlgoetAbstract:The conditional distribution of the next outcome given the infinite past of a stationary process can be inferred from finite but growing segments of the past. Several schemes are known for constructing pointwise consistent estimates, but they all demand prohibitive amounts of input data. In this paper we consider real-valued time series and construct conditional distribution estimates that make much more efficient use of the input data. The estimates are consistent in a weak sense, and the question whether they are pointwise consistent is still open. For finite-alphabet processes one may rely on a universal data compression scheme like the Lempel-Ziv algorithm to construct conditional probability mass function estimates that are consistent in expected information divergence. Consistency in this strong sense cannot be attained in a universal sense for all stationary processes with values in an infinite alphabet, but weak consistency can. Some applications of the estimates to on-line forecasting, regression and classification are discussed.
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Weakly Convergent nonparametric forecasting of stationary time series
IEEE Transactions on Information Theory, 1997Co-Authors: Gusztav Morvai, Sidney Yakowitz, P H AlgoetAbstract:The conditional distribution of the next outcome given the infinite past of a stationary process can be inferred from finite but growing segments of the past. Several schemes are known for constructing pointwise consistent estimates, but they all demand prohibitive amounts of input data. We consider real-valued time series and construct conditional distribution estimates that make much more efficient use of the input data. The estimates are consistent in a weak sense, and the question whether they are pointwise-consistent is still open. For finite-alphabet processes one may rely on a universal data compression scheme like the Lempel-Ziv (1978) algorithm to construct conditional probability mass function estimates that are consistent in expected information divergence. Consistency in this strong sense cannot be attained in a universal sense for all stationary processes with values in an infinite alphabet, but weak consistency can. Some applications of the estimates to on-line forecasting, regression, and classification are discussed.
Marek Kaluszka - One of the best experts on this subject based on the ideXlab platform.
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on fatou type lemma for monotone moments of Weakly Convergent random variables
Statistics & Probability Letters, 2004Co-Authors: Marek Kaluszka, Andrzej OkolewskiAbstract:Sufficient conditions for convergence of monotone moments of Weakly Convergent random variables, concerning the rate of convergence, are given. They are often more convenient than the necessary and sufficient uniform integrability condition. Some asymptotic evaluations for inverse moments are presented.
Youcai Zhang - One of the best experts on this subject based on the ideXlab platform.
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generalized krasnoselskii mann type iteration for nonexpansive mappings in banach spaces
Journal of the Operations Research Society of China, 2021Co-Authors: Youcai Zhang, Ke Guo, Tao WangAbstract:The Krasnoselskii–Mann iteration plays an important role in the approximation of fixed points of nonexpansive mappings, and it is well known that the classic Krasnoselskii–Mann iteration is Weakly Convergent in Hilbert spaces. The weak convergence is also known even in Banach spaces. Recently, Kanzow and Shehu proposed a generalized Krasnoselskii–Mann-type iteration for nonexpansive mappings and established its convergence in Hilbert spaces. In this paper, we show that the generalized Krasnoselskii–Mann-type iteration proposed by Kanzow and Shehu also converges in Banach spaces. As applications, we proved the weak convergence of generalized proximal point algorithm in the uniformly convex Banach spaces.
