A duopolistic model of dynamic competitive advertising

This paper proposes a differential game model of competitive advertising decisions for non-durable products by extending the Lanchester model and the Deal model of competitive advertising in the literature. The proposed model is compared empirically with the Lanchester model for model fitting and forecast accuracy. It is shown that the model is suitable for an actual market and out-performs the Lanchester model in forecast accuracy. The model provides a sensible modeling alternative to the Lanchester model for the study of dynamic competitive advertising decisions. Necessary and sufficient conditions for open-loop and closed-loop Nash equilibrium solutions to the model are discussed. A numerical algorithm for open-loop and closed-loop Nash strategies to the model is developed.     

第三方数据平台精准营销收费模式研究

第三方数据平台通过大数据分析技术为商家提供精准营销服务,本文研究了两个竞争商家通过同一个数据平台进行精准营销时,平台收费模式(费率佣金与按销量收费)对商家竞争以及平台与商家利润的影响。研究发现:1)费率佣金模式的绩效依赖于费率参数,在精准度高的情况下,它不能最大化平台与商家的利润。2)与费率佣金相比,按销量收费模式显著提高了商家的利润,并且平台与商家的利润都随费用参数的增加而增加。3)从收费模式选择的角度,精准度越高,按销量收费模式的优势越明显;费率佣金模式适用于垄断情况,而按销量收费模式更适用于竞争情况。4)费率佣金与按销量收费的结合可以实现平台与商家的共赢。研究结论为大数据驱动下的精准营销收费模式选择,以及“大数据平台”的盈利模式创新提供了理论参考。     

Iterative identification and control design using finite-signal-to-noise models

In this paper, a model is said to be validated for control design if using the model-based controller, the closed loop performance of the real plant satisfies a specified performance bound. To improve the model for control design, only closed loop response data is available to deduce a new model of the plant. Hence the procedure described herein involves three steps in each iteration: (i) closed loop identification; (ii) plant model extraction from the closed loop model; (iii) controller design. Thus our criteria for model validation involve both the control design procedure by which the closed loop system performance is evaluated, and the identification procedure by which a new model of the plant is deduced from the closed loop response data. This paper proposes new methods for both parts, and also proposes an iterative algorithm to connect the two parts. To facilitate both the identification and control tasks, the new finite-signal-to-noise (FSN) model of linear systems is utilized. The FSN model allows errors in variables whose noise covariances are proportional to signal covariances. Allowing the signal to noise ratios to be bounded but uncertain, a control theory to guarantee a variance upper bound is developed for the discrete version of this new FSN model. The identification of the closed loop system is accomplished by a new type of q-Markov Cover, adjusted to accommodate the assumed FSN structure of the model. The model of the plant is extracted from the closed loop identification model. This model is then used for control design and the process is repeated until the closed loop performance validates the model. If the iterations produce no such a controller, we say that this specific procedure cannot produce a model valid for control design and the level of the required performance must be reduced.     

基于遗传算法的附随扩散模型的构建及实证研究

论文研究了具有附随扩散关系的产品扩散特点,以Bass模型为理论基础,构建了附随扩散模型,然后以移动上网用户附随移动用户扩散的实例为研究对象,建立了移动上网附随扩散模型,并采用遗传算法估算模型参数,对今后几年移动上网用户的扩散规律进行了预测。最后,将附随扩散模型的研究结果与采用传统Bass模型、Logistic模聊的结果进行了对比,得出了附随扩散模型拟合和预测效果更好的结论。     

The asymptotic limit for a combustion model in regard to infinite reaction rate

The Zeldovich-von Neumann-Doring model and the Chapman-Jouguet model for a simplified combustion model-Majda＇s model is studied.The author proves a uniform maximum norm estimate,then proves that as the rate of chemical reaction tends to infinity the solutions to the Zeldovich-von Neumann-Doring model tend to that of the Chapman-Jouguet model.The type of combustion waves is studied.This result is compared with the result of the projection and finite difference method for the same model.     

A comparison of univariate methods for forecasting container throughput volumes

In this paper, six univariate forecasting models for the container throughput volumes in Taiwan’s three major ports are presented. The six univariate models include the classical decomposition model, the trigonometric regression model, the regression model with seasonal dummy variables, the grey model, the hybrid grey model, and the SARIMA model. The purpose of this paper is to search for a model that can provide the most accurate prediction of container throughput. By applying monthly data to these models and comparing the prediction results based on mean absolute error, mean absolute percent error and root mean squared error, we find that in general the classical decomposition model appears to be the best model for forecasting container throughput with seasonal variations. The result of this study may be helpful for predicting the short-term variation in demand for the container throughput of other international ports.     

Non-linear model parameter estimation–estimating a feasible parameter set with respect to model use

This article deals with non-linear model parameter estimation from experimental data. As for non-linear models a rigorous identifiability analysis is difficult to perform, parameter estimation is performed in such a way that uncertainty in the estimated parameter values is represented by the range of model use results when the model is used for a certain purpose. Using this approach, the article presents a simulation study where the objective is to discover whether the estimation of model parameters can be improved, so that a small enough range of model use results is obtained. The results of the study indicate that from plant measurements available for the estimation of model parameters, it is possible to extract data that are important for the estimation of model parameters relative to a certain model use. If these data are improved by a proper measurement campaign (e.g. proper choice of measured variables, better accuracy, higher measurement frequency) it is to be expected that a valid model for a certain model use will be obtained. The simulation study is performed for an activated sludge model from wastewater treatment, while the estimation of model parameters is done by Monte Carlo simulation.     

Bayesian Inference via Filtering for a Class of Counting Processes: Application to the Micromovement of Asset Price

This paper highlights recent developments in a rich class of counting process models for the micromovement of asset price and in the Bayesian inference (estimation and model selection) via filtering for the class of models. A specific micromovement model built upon linear Brownian motion with jumping stochastic volatility is used to demonstrate the procedure to develop a micromovement model with specific tick-level sample characteristics. The model is further used to demonstrate the procedure to implement Bayes estimation via filtering, namely, to construct a recursive algorithm for computing the trade-by-trade Bayes parameter estimates, especially for the stochastic volatility. The consistency of the recursive algorithm model is proven. Simulation and real-data examples are provided as well as a brief example of Bayesian model selection via filtering.     

Multiset Model Selection

Model selection algorithms are required to efficiently traverse the space of models. In problems with high-dimensional and possibly correlated covariates, efficient exploration of the model space becomes a challenge. To overcome this, a multiset is placed on the model space to enable efficient exploration of multiple model modes with minimal tuning. The multiset model selection (MSMS) framework is based on independent priors for the parameters and model indicators on variables. Posterior model probabilities can be easily obtained from multiset averaged posterior model probabilities in MSMS. The effectiveness of MSMS is demonstrated for linear and generalized linear models. Supplementary material for this article is available online.     

CRM决策的序贯优化——以KDD98直邮决策为例

从多阶段、延迟回报的角度来看待CRM中的决策优化问题。以KDD98数据集为例,将邮寄序贯决策定义为一个部分可观察马尔可夫决策模型(POMDP)。提出了模型参数估计的EM算法并用MATLAB实现;用模型对数似然值、BIC统计量选择最佳模型;用向前一步预测对模型进行检验;用Incremental prune算法对模型求解。实证结果表明,POMDP模型可以很好的捕捉客户购买行为的动态变化,对客户的购买有很好的预测效果。在此基础上,说明了如何使用该模型以客户终生价值最大化为目标优化直邮策略。     

基于GAS模型的动态VaR预测效果分析

GAS模型是一种基于观测的动态模型,理论简单且应用灵活,可以直接估计VaR.将GAS模型和GARCH类模型应用于不同条件下生成的模拟数据和三个时间段的沪深300指数的日对数收益率数据,并比较模型关于VaR的预测效果。结果表明:在对称的条件分布下,GAS模型容易高估风险且不稳健,其表现不如GARCH类模型;但在条件分布为有偏的时,GAS模型与GARCH类模型的表现相当,部分情况下会优于GARCH类模型,尤其在实证分析中关于序列2和序列3的VaR的估计,GAS模型的预测效果较好。因此,实际应用中,对于具有较明显偏态分布或尖峰分布的数据可以考虑使用GAS模型预测动态VaR.     

Joint distributions of some actuarial random vectors for the Cox risk model

The main purpose of this paper was to investigate the joint distributions of some actuarial vectors that contain the ruin time for the Cox risk model. Joint distributions of some actuarial vectors such as those containing the ruin time, the maximum surplus before ruin, duration of the surplus being negative, and others are important for measuring the risk management level and the severity caused by ruin. In the past decade, great literatures have devoted to the study of these distributions for classical models, such as the compound Poisson model and the perturbed compound Poisson model etc. The main result of this paper provides the joint distributions of these actuarial vectors for the Cox risk model—a model with wide applications in risk theory. The main method of this paper is to apply the idea of ‘operational time scale’ to the Cox model, which enables us to solve our problem by intergrading some existing results for the compound Poisson risk model. To some extent, we can view our work as an extension of joint distributions of some actuarial vectors for the compound Poisson risk model to the ones for the Cox risk model. Copyright © 2011 John Wiley & Sons, Ltd.     

Estimation of Lyapunov spectrum and model selection for a chaotic time series

The estimation of the Lyapunov spectrum for a chaotic time series is discussed in this study. Three models: the local linear (LL) model; the local polynomial (LP) model and the global radial basis function (RBF) model, are compared for estimating the Lyapunov spectrum in this study. The number of neighbors for training the LL model and the LP model; the number of centers for building the RBF model, have been determined by the generalized degree of freedom for a chaotic time series. The above models have been applied to three artificial chaotic time series and two real-world time series, the numerical results show that the model-chosen LL model provides more accurate estimation than other models for clean data set while the RBF model behaves more robust to noise than other models for noisy data set.     

基于GM-ES-GASVR组合模型的丽江国内旅游需求预测

构建适合于预测丽江国内旅游需求的预测模型,对推动丽江旅游业的发展具有重要意义.研究发现灰色GM(1,1)模型、三次指数平滑模型与GA-SVR模型都适用于预测丽江国内旅游需求,且GA-SVR模型为这三个单项模型中的最优模型.在此基础上,利用变权方法建立GM-ES-GASVR组合预测模型.通过对拟合与测试结果的对比分析,表明GM-ES-GASVR变权组合预测模型比单一模型的拟合与测试效果都有较大改善.     

预算模式平稳过渡的目标规划模型分析

依据财政预算资金拨付体系平稳过渡的要求,本文提出了三种平稳过渡策略,建立了预算模式由计划预算模式平稳过渡到支出预算模式的多目标优化模型并给出了相应的算法.数值结果表明,模型可以对不同策略的效果进行评估,并且可以找到可行的解决方案。该模型应用于某单位过渡方案的制定,得到了很好的结果。该项研究无论对政府计划部门,还是企事业单位的预算制度改革都有很重要的作用。     

Stability preserving NSFD scheme for a delayed viral infection model with cell-to-cell transmission and general nonlinear incidence

In this paper, we designed and analysed a discrete model to solve a delayed within-host viral infection model by using non-standard finite difference scheme. The original model that we considered was a delayed viral infection model with cell-to-cell transmission, cell-mediated immune response and general nonlinear incidence. We show that the discrete model has equilibria which are exactly the same as those of the original continuous model and the conditions for those equilibria to be globally asymptotically stable are consistent with the original continuous model with no restriction on the time step size. The results imply that the discretization scheme can efficiently preserves the qualitative properties of solutions for corresponding continuous model.     

Multi-Model Communication and Data Assimilation for Mitigating Model Error and Improving Forecasts

Models for weather and climate prediction are complex, and each model typi-cally has at least a small number of phenomena that are poorly represented, such as perhaps the Madden-Julian Oscillation (MJO for short) or El Ni\~{n}o-Southern Oscillation (ENSO for short) or sea ice. Furthermore, it is often a very challenging task to modify and improve a complex model without creating new deficiencies. On the other hand, it is sometimes possible to design a low-dimensional model for a particular phenomenon, such as the MJO or ENSO, with significant skill, although the model may not represent the dynamics of the full weather-climate system. Here a strategy is proposed to mitigate these model errors by taking advantage of each model''s strengths. The strategy involves inter-model data assimilation, during a forecast simulation, whereby models can exchange information in order to obtain more faithful representations of the full weather-climate system. As an initial investigation, the method is examined here using a simplified scenario of linear models, involving a system of stochastic partial differential equations (SPDEs for short) as an imperfect tropical climate model and stochastic differential equations (SDEs for short) as a low-dimensional model for the MJO. It is shown that the MJO prediction skill of the imperfect climate model can be enhanced to equal the predictive skill of the low-dimensional model. Such an approach could provide a route to improving global model forecasts in a minimally invasive way, with modifications to the prediction system but without modifying the complex global physical model itself.     

非零截距线性和非线性回归模型的T-型稳健估计算法

首先给出非零截距线性模型T-型估计的模型与EM算法,其次给出非线性回归模型参数的T-型估计,利用泰勒级数对模型线性化,得到参数估计的迭代算法,最后用数值模拟实验验证了该算法的正确性和证实了T-型估计的稳健性.     

ESTIMATION OF THE MIXED AR AND HIDDEN PERIODIC MODEL

ThisresearchissupportedbytheNationalNaturalScienceFoundationofChina.1.IntroductionGeneralizedhiddenperiodicmodelhasthefollowingformwhereacisthesetofallpositiveintegers,('~{((t);tEac}isastationarysequencewithzeromeanandcontinuousspectraldensity,i=n,qisanonnegativeinteger,'f=0,X=(Al,Az,',A,)isarealvectorwith--T相似文献   

Surface-dependent growth kinematics in euclidean 3-space

In this paper, we investigate the surface-dependent growth model in Euclidean 3-space. The surface-dependent model is developed for modeling the kinematics of surface growth for objects that can be generated by the curves on the surface, such as parasites and plants. This paper includes two main purposes for this model. The first is to parameterize this model using quaternions and homothetic motions, while expressing matrix representations of the surface-dependent growth model. The second one is to construct the surface-dependent growth model by using the growth velocity components related to the Darboux frame at each point of the generating curve. Moreover, to support the theory studied in the paper, various examples are illustrated.