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Search Results: 1 - 10 of 18603 matches for " Zuofeng Gao "
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Gao Zuofeng,

计算数学 , 2000,
Abstract: For a multilevel engineering system, considering reliabilities as decomposition parameters, construction costs as coordination parameters, its decomposition models for reliability optimization is constructed, and its corresponding coordination algorithm is also given.
The -Core of a -person Stochastic Cooperative Game
Zuofeng Gao,Hongxin Bai,Suting Zhang,Yongbo Yu
Modern Applied Science , 2009, DOI: 10.5539/mas.v2n2p71
Abstract: In this paper, based on the core of the stochastic cooperative game (Oviedo, 2000, pp. 519-524), We define the core of the stochastic cooperative game. Thus, we recuperate the defect in theory that the core is empty usual. And we introduce some characters and properties about the kind -core.
The Stable Set and Weak Stable Set For -person Repeated Fuzzy Cooperative Games
Zuofeng Gao,Suting Zhang,Hongxin Bai,Chunyan Han
Modern Applied Science , 2009, DOI: 10.5539/mas.v2n2p97
Abstract: In this paper,based on the fuzzy games,we define the imputation sequences of the -person repeated games,and the domination,weak domination for the imputation sequences.Further,based on this theory,we define the core ,the weak core,the stable set,and the weak stable set of the -person repeated fuzzy cooperative games.At last,some properties of the stable set and the weak stable set are given.
A Repeated Convex Fuzzy Cooperative Game
Zuofeng Gao,Yongbo Yu,Hongxin Bai,Chunyan Han
Modern Applied Science , 2009, DOI: 10.5539/mas.v2n3p54
Abstract: This paper introduces repeated theory on the base of fuzzy cooperative game by Aubin etal in 1974 and then constructs repeated fuzzy games theory. It gives the conception of repeated convex fuzzy cooperative games and studies the property of repeated convex fuzzy games.
The Shapley Value for Stochastic Cooperative Game
Ying Ma,Zuofeng Gao,Wei Li,Ning Jiang
Modern Applied Science , 2009, DOI: 10.5539/mas.v2n4p76
Abstract: In this paper we extend the notion of Shapley value to the stochastic cooperative games. We give the definition of marginal vector to the stochastic cooperative games and we define the Shapley value for this game. Furthermore, we discuss the axioms of the Shapley value and give the proofs of these axioms.
Two-Person Non Zero-Sum Bimatrix Game with Fuzzy Payoffs and Its Equilibrium Strategy
Chunyan Han,Zuofeng Gao,Yongbo Yu,Hua Zhang
Journal of Mathematics Research , 2009, DOI: 10.5539/jmr.v1n1p57
Abstract: In this paper, we consider fuzzy bimatrix games with fuzzy payoffs. Based on fuzzy max order, for such games, we define three kinds of concepts of minim ax equilibrium strategies. Some basic results obtained.

Bai Zhongzhi,Gao Zuofeng,Huang Tinzhu,

计算数学 , 1999,
Abstract: For a class of ideal models of parallel computers, we define some measuring parameters such as the speed-up, the efficiency, the redundancy of a linear and nonlinear parallel iteration method in both average and asymptotic senses, as well as the utilization ratio of the parallel computer. These parameters are reasonable and convenient for the theoretical studies of the parallel iteration methods.
Local and global asymptotic inference in smoothing spline models
Zuofeng Shang,Guang Cheng
Statistics , 2012, DOI: 10.1214/13-AOS1164
Abstract: This article studies local and global inference for smoothing spline estimation in a unified asymptotic framework. We first introduce a new technical tool called functional Bahadur representation, which significantly generalizes the traditional Bahadur representation in parametric models, that is, Bahadur [Ann. Inst. Statist. Math. 37 (1966) 577-580]. Equipped with this tool, we develop four interconnected procedures for inference: (i) pointwise confidence interval; (ii) local likelihood ratio testing; (iii) simultaneous confidence band; (iv) global likelihood ratio testing. In particular, our confidence intervals are proved to be asymptotically valid at any point in the support, and they are shorter on average than the Bayesian confidence intervals proposed by Wahba [J. R. Stat. Soc. Ser. B Stat. Methodol. 45 (1983) 133-150] and Nychka [J. Amer. Statist. Assoc. 83 (1988) 1134-1143]. We also discuss a version of the Wilks phenomenon arising from local/global likelihood ratio testing. It is also worth noting that our simultaneous confidence bands are the first ones applicable to general quasi-likelihood models. Furthermore, issues relating to optimality and efficiency are carefully addressed. As a by-product, we discover a surprising relationship between periodic and nonperiodic smoothing splines in terms of inference.
Bayesian Ultrahigh-Dimensional Screening Via MCMC
Zuofeng Shang,Ping Li
Statistics , 2013,
Abstract: We explore the theoretical and numerical property of a fully Bayesian model selection method in sparse ultrahigh-dimensional settings, i.e., $p\gg n$, where $p$ is the number of covariates and $n$ is the sample size. Our method consists of (1) a hierarchical Bayesian model with a novel prior placed over the model space which includes a hyperparameter $t_n$ controlling the model size, and (2) an efficient MCMC algorithm for automatic and stochastic search of the models. Our theory shows that, when specifying $t_n$ correctly, the proposed method yields selection consistency, i.e., the posterior probability of the true model asymptotically approaches one; when $t_n$ is misspecified, the selected model is still asymptotically nested in the true model. The theory also reveals insensitivity of the selection result with respect to the choice of $t_n$. In implementations, a reasonable prior is further assumed on $t_n$ which allows us to draw its samples stochastically. Our approach conducts selection, estimation and even inference in a unified framework. No additional prescreening or dimension reduction step is needed. Two novel $g$-priors are proposed to make our approach more flexible. A simulation study is given to display the numerical advantage of our method.
High-Dimensional Bayesian Inference in Nonparametric Additive Models
Zuofeng Shang,Ping Li
Statistics , 2013,
Abstract: A fully Bayesian approach is proposed for ultrahigh-dimensional nonparametric additive models in which the number of additive components may be larger than the sample size, though ideally the true model is believed to include only a small number of components. Bayesian approaches can conduct stochastic model search and fulfill flexible parameter estimation by stochastic draws. The theory shows that the proposed model selection method has satisfactory properties. For instance, when the hyperparameter associated with the model prior is correctly specified, the true model has posterior probability approaching one as the sample size goes to infinity; when this hyperparameter is incorrectly specified, the selected model is still acceptable since asymptotically it is proved to be nested in the true model. To enhance model flexibility, two new $g$-priors are proposed and their theoretical performance is examined. We also propose an efficient MCMC algorithm to handle the computational issues. Several simulation examples are provided to demonstrate the computational advantages of our method.
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