新混沌系统的自适应控制与噪声中的同步研究

文章分析了新混沌系统的动力学行为,在参数未知时设计一连续的自适应控制器,通过调整控制增益提高控制速度,并证明所给控制器能使受控混沌系统全局渐近稳定。研究在有界噪声作用下两个新混沌系统的异同步控制器的控制效果,用Matlab软件进行数值仿真,检验了该控制器具有较强的鲁棒性和抗干扰能力。     

福建省城镇居民收入与经济增长同步性的实证分析及对策

居民收入与经济增长同步是福建省经济社会发展的主要目标,本文从改革开放以来福建省城镇居民收入和经济增长发展轨迹入手,深入剖析居民收入与经济增长的关系,揭示居民收入增长变化趋势,探求促进居民收入可持续增长的对策措施,为推动福建省跨越式发展以及实现居民收入增长与经济发展同步这一＂十二五＂规划的约束性目标,理清发展思路,提供参考依据。     

经济增长自适应预测模型的构建与应用

在内在和外在因素的综合作用下,经济增长率(GDP实际增长率)的时间序列中往往存在着常态增长和周期波动双重效应。文章采用逐步回归法构建的自适应经济增长预测模型,能有效揭示经济运行中常态增长和周期波动效应共同决定经济增长的数量规律,并能有效地应用于经济增长预测。     

上海城市居民家庭收入特点、问题与对策

2011年,上海城市居民收入水平保持平稳较快增长,但仍需关注居民收入实际增长与经济增长同步的不确定性、就业人员工资性收入正常增长机制缺失、人口老龄化影响民生政策的可持续性等问题。为促进上海城市居民收入持续稳步增长,建议深化收入分配制度改革、加快产业结构调整、拓宽城市居民收入渠道、完善社会保障体系等,努力实现居民收入增长和经济发展同步。     

中国经济增长中的环保投资贡献的实证分析

文章将环保投资作为对经济增长的影响要素纳入C--D生产函数,运用中国1990～2009年的时间序列数据,通过建立VAR模型和VECM模型,运用JJ协整检验和Granger因果关系检验,确定了环保投资与GDP之间存在双向长期Granger因果关系。通过线性回归分析,发现环保投资对我国经济增长有正的影响。此外,VAR模型的脉冲响应分析表明,经济建设投资、环保投资与劳动者工资同步协调增长,对我国的经济增长具有长远的意义。     

2011年长三角地区经济发展分析

2011年长三角经济增速呈现回落态势,但仍高于全国平均水平。工业、服务业发展较为稳定,投资、消费、出口增速基本保持同步,协调拉动经济增长。财政收入较快增长,物价水平稳中趋于回落。     

中国R&D投入与经济增长的系统动态仿真研究

文章采用系统动力学模型对R&D投入与经济增长进行了系统的分析和研究,从因果关系图入手,绘制出系统流图,进行了系统仿真,并调整政策参数进行两种方案的对比和预测.研究结果表明,我国R&D投入与经济增长是相辅相成,应抓住有利时机,利用目前我国经济增长的良好势头,不断加大政府政策的支持力度,提升科技进步对经济增长的贡献度.     

就业弹性与经济增长相关性分析

改革的深入和现代化进程的逐步深化,经济转轨、社会转型和经济全球化正在严重冲击中国的劳动力市场,使就业问题成为中国进入21世纪后所面临的最大挑战,就业弹性出现了明显的下降.就业弹性的逐年下降使劳动力的需求远远不能与经济保持同步扩张,劳动力过剩已经成为我国经济发展中一个日益突出的问题.通过研究得出经济增长与就业增长的相关性分析,为政府相关部门制定政策和决策提供依据,对经济发展战略、经济增长战略以及政策倾向方面提出相应的对策建议.     

中国金融发展与经济增长的因果关系分析

在国内外金融发展与经济增长关系的争论由来已久.从国内的研究现状来看,大多实证研究在方法上都选用了向量误差修正模型和格兰杰因果关系检验.文章运用超外生性方法实证分析了中国金融发展与经济增长之间的关系,得出中国金融发展与经济增长不存在正向的控制因果关系,但在长期内存在经济增长到金融发展的逆向控制因果关系的结论.结论表明:优先发展经济的政策有助于带动金融长期的发展.     

居民储蓄存款超增长的实证及思考

居民储蓄存款的超增长是近几年来我国社会经济生活的特有现象。这种现象主要是由于居民收入水平与消费水平的非同步变动和居民消费结构的变化以及公款消费、灰色收入等因素所引起的。如何解决居民储蓄存款超增长与银根紧缩政策这一矛盾，作者进行了初步探讨。     

Adaptive c-chart with estimated parameter

In the design of control charts, it is usually assumed that process parameters are known. However, in many practical applications the values of these parameters are unknown and should be estimated using historical in-control process observations. In this study, the performance of adaptive c-chart with estimated parameter is evaluated. It is demonstrated that by increasing the size and the number of samples in estimating the process parameter, the performance of the chart converges to that of the known parameter case. Finally the best phase I sampling scenarios are presented to make the chart with the estimated parameter perform as well as the chart with the known parameter.     

On adaptive progressively Type-II censored competing risks data

In this article, a competing risks model based on exponential distributions is considered under the adaptive Type-II progressively censoring scheme introduced by Ng et al. [2009, Naval Research Logistics 56:687-698], for life testing or reliability experiment. Moreover, we assumed that some causes of failures are unknown. The maximum likelihood estimators (MLEs) of unknown parameters are established. The exact conditional and the asymptotic distributions of the obtained estimators are derived to construct the confidence intervals as well as the two different bootstraps of different unknown parameters. Under suitable priors on the unknown parameters, Bayes estimates and the corresponding two sides of Bayesian probability intervals are obtained. Also, for the purpose of evaluating the average bias and mean square error of the MLEs, and comparing the confidence intervals based on all mentioned methods, a simulation study was carried out. Finally, we present one real dataset to conduct the proposed methods.     

An adaptive rule for stopping a discovery process under time censorship

Consider a finite population of large but unknown size of hidden objects. Consider searching for these objects for a period of time, at a certain cost, and receiving a reward depending on the sizes of the objects found. Suppose that the size and discovery time of the objects both have unknown distributions, but the conditional distribution of time given size is exponential with an unknown non-negative and non-decreasing function of the size as failure rate. The goal is to find an optimal way to stop the discovery process. Assuming that the above parameters are known, an optimal stopping time is derived and its asymptotic properties are studied. Then, an adaptive rule based on order restricted estimates of the distributions from truncated data is presented. This adaptive rule is shown to perform nearly as well as the optimal stopping time for large population size.     

Estimation of generalized exponential distribution based on an adaptive progressively type-II censored sample

In this paper, based on an adaptive Type-II progressively censored sample from the generalized exponential distribution, the maximum likelihood and Bayesian estimators are derived for the unknown parameters as well as the reliability and hazard functions. Also, the approximate confidence intervals of the unknown parameters, and the reliability and hazard functions are calculated. Markov chain Monte Carlo method is applied to carry out a Bayesian estimation procedure and in turn calculate the credible intervals. Moreover, results from simulation studies assessing the performance of our proposed method are included. Finally, an illustrative example using real data set is presented for illustrating all the inferential procedures developed here.     

On the convergence of some adaptive estimation procedures

In the paper we consider some adaptive procedures for estimating the unknown parameters of autoregressive and moving average processes. In case of AK(p) and MA(1) processes sequences of estimators converging with probability one and In mean square are given     

On adaptive procedures controlling the familywise error rate

The idea of modifying, and potentially improving, classical multiple testing methods controlling the familywise error rate (FWER) via an estimate of the unknown number of true null hypotheses has been around for a long time without a formal answer to the question whether or not such adaptive methods ultimately maintain the strong control of FWER, until Finner and Gontscharuk (2009) and Guo (2009) have offered some answers. A class of adaptive Bonferroni and S?idàk methods larger than considered in those papers is introduced, with the FWER control now proved under a weaker distributional setup. Numerical results show that there are versions of adaptive Bonferroni and S?idàk methods that can perform better under certain positive dependence situations than those previously considered. A different adaptive Holm method and its stepup analog, referred to as an adaptive Hochberg method, are also introduced, and their FWER control is proved asymptotically, as in those papers. These adaptive Holm and Hochberg methods are numerically seen to often outperform the previously considered adaptive Holm method.     

Adaptively combining independent jonckheere statistics in a randomized block design with unequal scales

Jonckheere (1954) proposed a test statistic which is commonly used in testing for ordered alternatives in block designs.- We consider the application of Jonckheere's test statistic in block designs which have unequal scale parameters for the blocks. Estimates of the unknown scale parameters ar-fcrmed and are used to construct a modification of Jonckheere's test statistic using adaptive ideas. A Monte Carlo study shows that the modified Jonckheere is significantly more powerful than the original Jonckheere in many unequal scale situations.     

Fixed-width interval estimation of the minimum point of a regression function based on the Kiefer-Wolfowitz procedure

A version of the central limit theorem for the Kiefer-Wolfowitz procedure is stated. One constructs an asymptotically consistent fixed-width confidence interval for the minimum of a regression function. It is shown that the first moment of the corresponding stopping rule is finite. Both the construction and properties of the estimates of unknown parameters and an adaptive version of the procedure are presented.     

Empirical likelihood for generalized linear models with fixed and adaptive designs

Empirical likelihood inference for generalized linear models with fixed and adaptive designs is considered. It is shown that the empirical log-likelihood ratio at the true parameters converges to the standard chi-square distribution. Furthermore, we obtain the maximum empirical likelihood estimate of the unknown parameter and the resulting estimator is shown to be asymptotically normal. Some simulations are conducted to illustrate the proposed method.     

On Fan's adaptive Neyman tests for comparing two spectral densities

In this paper, we consider tests for assessing whether two stationary and independent time series have the same spectral densities (or same autocovariance functions). Both frequency domain and time domain test statistics for this purpose are reviewed. The adaptive Neyman tests are then introduced and their performances are investigated. Our tests are adaptive, that is, they are constructed completely by the data and do not involve any unknown smoothing parameters. Simulation studies show that our proposed tests are at least comparable to the current tests in most cases. Furthermore, our tests are much more powerful in some cases, such as against the long orders of autoregressive moving average (ARMA) models such as seasonal ARMA series.