智能化仪表传感器非线性校正的实现

本文描述了传感器非线性特性预处理的方法．实践证明该方法适用于标准或非标准特殊传感器，处理结果使智能型仪表的测试精度有显著提高．     

增长性市场条件下垄断定价的实物期权方法

许多研究垄断厂商定价的文献忽视了垄断厂商拥有的调整价格的权利. 事实上,这种调 价的权利等价于厂商拥有的一个实物期权. 基于此,应用实物期权的方法确定了增长性市场条 件下调价权利(调价期权) 的价值,进而研究了垄断厂商的定价行为,给出了企业价值最大化的 条件. 结果表明,在增长性市场中,需求状况的好坏决定了垄断厂商的定价决策是否考虑调价 期权. 并且,当垄断厂商定价考虑其拥有的调价期权的价值时,除一些特殊情形外,边际收益等 于边际成本,不再适用垄断厂商的定价决策     

股指期货价格非线性均值回复特性实证研究

用ESTAR 模型对香港恒生指数期货933、9312、943 合约及S&P 500 指数期货933、9312、 943 合约价格进行了实证研究,发现恒生指数期货933、9312 合约实际价格呈现非线性均值回 复,而其他各合约实际价格呈现线性均值回复. 结论:由于股票现货没有卖空机制使套利成本 较大,抑制了套利行为,导致期货合约实际价格呈现非线性. 在股票现货没有卖空机制的市场 条件下,单向套利的机会要比成熟的市场更多.     

An access network design problem with end-to-end QoS constraints

In this paper, we present an access network design problem with end-to-end quality of service (QoS) requirement. The problem can be conceptualized as a two-level hierarchical location-allocation problem on the tree topology with nonlinear side constraints. The objective function of the nonlinear mixed integer programming model minimizes the total cost of switch and fiber cable, while satisfying demand within the prescribed level of QoS. By exploiting the inherent structure of the nonlinear QoS constraints, we develop linearization techniques for finding an optimal solution. Also, we devise an effective exact optimal algorithm within the context of disjunctive constraint generation. We present promising computational results that demonstrate the effectiveness of the proposed solution procedure.     

从“凯恩斯效应”到“阿米曲线”——政府规模与经济增长关系的非线性转向

政府规模与经济增长之间的关系问题是宏观经济研究中的核心问题之一.关于两者关系的传统研究往往以线性假定为基础,所以实证结果出现了巨大的差异.这种现象促使研究者从非线性角度重新审视政府规模与经济增长之间的关系.“巴罗法则”在内生增长框架内为政府规模与经济增长关系研究提供了一个标准的分析框架,揭示了两者之间的倒“U”关系.而“Armey曲线”不仅非常直观地表达了政府规模与经济增长之间的非线性关系,同时还指出这种非对称关系的非对称性质,具有很强的总结性和政策指导意义.     

绿色增长下的导向性技术创新选择研究

针对如何正确引导 GHG 排放的气候反馈经济损失下导向性技术创新，以实现长期的绿色增长这一问题，基于 DICE 模型，重构导向性技术创新不同的动力要素及其与气候变化之间的内生关系，建立了可体现绿色增长“均衡性”、“包容性”和“可持续性”的非线性最优控制模型。数值仿真结果显示: 气候反馈经济损失不容小觑; 偏于生产的绿色技术创新导向下社会生产力强劲但存在气候环境恶化风险，偏于减排的绿色技术创新导向下气候反馈经济损失小但存在增长动力匮乏风险，二者虽均可实现长期的绿色增长，但中性的绿色技术创新导向会更稳妥; 偏于生产的绿色技术创新导向下人均消费变化率在短期内会显著提升。     

Diagnostics for quasi-likelihood nonlinear models

Quasi-likelihood nonlinear models (QLNMs) are an extension of generalized linear model and include a widen class of models as special cases. This article investigates some diagnostic methods in QLNMs. An equivalency between a case-deletion model and a mean-shift outlier model in QLNM is established. Two simulation study and a real dataset are used to illustrate the proposed diagnostic methods.     

Corrected maximum likelihood estimators in heteroscedastic symmetric nonlinear models

In this article, we derive general matrix formulae for second-order biases of maximum likelihood estimators (MLEs) in a class of heteroscedastic symmetric nonlinear regression models, thus generalizing some results in the literature. This class of regression models includes all symmetric continuous distributions, and has a wide range of practical applications in various fields such as engineering, biology, medicine and economics, among others. The variety of distributions with different kurtosis coefficients than the normal may give more flexibility in the choice of an appropriate distribution, particularly to accommodate outlying and influential observations. We derive a joint iterative process for estimating the mean and dispersion parameters. We also present simulation studies for the biases of the MLEs.     

Robust Bayesian nonlinear mixed‐effects modeling of time to positivity in tuberculosis trials

Early phase 2 tuberculosis (TB) trials are conducted to characterize the early bactericidal activity (EBA) of anti‐TB drugs. The EBA of anti‐TB drugs has conventionally been calculated as the rate of decline in colony forming unit (CFU) count during the first 14 days of treatment. The measurement of CFU count, however, is expensive and prone to contamination. Alternatively to CFU count, time to positivity (TTP), which is a potential biomarker for long‐term efficacy of anti‐TB drugs, can be used to characterize EBA. The current Bayesian nonlinear mixed‐effects (NLME) regression model for TTP data, however, lacks robustness to gross outliers that often are present in the data. The conventional way of handling such outliers involves their identification by visual inspection and subsequent exclusion from the analysis. However, this process can be questioned because of its subjective nature. For this reason, we fitted robust versions of the Bayesian nonlinear mixed‐effects regression model to a wide range of TTP datasets. The performance of the explored models was assessed through model comparison statistics and a simulation study. We conclude that fitting a robust model to TTP data obviates the need for explicit identification and subsequent “deletion” of outliers but ensures that gross outliers exert no undue influence on model fits. We recommend that the current practice of fitting conventional normal theory models be abandoned in favor of fitting robust models to TTP data.     

Robust inference for estimating equations with nonignorably missing data based on SIR algorithm

Nonresponse is a very common phenomenon in survey sampling. Nonignorable nonresponse – that is, a response mechanism that depends on the values of the variable having nonresponse – is the most difficult type of nonresponse to handle. This article develops a robust estimation approach to estimating equations (EEs) by incorporating the modelling of nonignorably missing data, the generalized method of moments (GMM) method and the imputation of EEs via the observed data rather than the imputed missing values when some responses are subject to nonignorably missingness. Based on a particular semiparametric logistic model for nonignorable missing response, this paper proposes the modified EEs to calculate the conditional expectation under nonignorably missing data. We can apply the GMM to infer the parameters. The advantage of our method is that it replaces the non-parametric kernel-smoothing with a parametric sampling importance resampling (SIR) procedure to avoid nonparametric kernel-smoothing problems with high dimensional covariates. The proposed method is shown to be more robust than some current approaches by the simulations.