Weibull分布在新产品市场渗透研究中的应用拓展

在应用Weibull模型研究新产品市场渗透时,"永不采用人群"、消费者个体之间的差异、消费者群体之间的差异是研究新产品采用时需要考虑的三种因素。本文基于这三种因素分别建立了三个拓展的Weibull模型,并利用面板数据进行了实证研究,发现三种拓展之后的模型在数据拟合和数据预测方面均有显著的提高.然后,本文将三种因素整合至一个模型之中形成了一种新的综合Weibull模型,实证分析结果显示新的模型有很好的新产品市场渗透数据拟合和预测能力.     

Nonparametric evaluation of the first passage time of degradation processes

This paper discusses a nonparametric method to approximate the first passage time (FPT) distribution of the degradation processes incorporating random effects if the process type is unknown. The FPT of a degradation process is unnecessarily observed since its density function can be approximated by inverting the empirical Laplace transform using the empirical saddlepoint method. The empirical Laplace transform is composed of the measured increments of the degradation processes. To evaluate the performance of the proposed method, the approximated FPT is compared with the theoretical FPT assuming a true underlying process. The nonparametric method discussed in this paper is shown to possess the comparatively small relative errors in the simulation study and performs well to capture the heterogeneity in the practical data analysis. To justify the fitting results, the goodness‐of‐fit tests including Kolmogorov‐Smirnov test and Cramér‐von Mises test are conducted, and subsequently, a bootstrap confidence interval is constructed in terms of the 90th percentile of the FPT distribution.     

中国碳排放与经济发展存在倒U型关系吗?——考虑时间相关效应和异质性的研究

随着我国经济进入高质量发展阶段,经济发展与碳排放之间的关系日益受到关注。本文基于1997~2016年省级面板数据,运用非参数广义加性混合模型研究了中国碳排放与经济发展的关系。文中,不可观测的时间相关效应和残差自相关结构被作为独立变量加入模型,收入效应和时间相关效应对碳排放的影响可能存在异质性也予以考虑。实证结果显示,东部和中部地区的最适模型中收入效应具有异质性,西部地区的最适模型中包含异质性时间相关效应;中国碳排放与经济发展之间不存在倒U型关系;在未来的节能减排工作中,应充分考虑中国经济发展阶段性、区域差异性及碳排放驱动因素的异质性。     

偏t分布假设下的空间效应模型及其应用

在非寿险索赔强度预测中,目前使用最为广泛的是广义线性模型。索赔强度的广义线性模型假设因变量服从伽马分布或逆高斯分布,且在预测项中仅能考虑协变量的线性效应。这些限制性条件都有可能影响索赔强度预测结果的准确性。本文对索赔强度的广义线性模型进行了推广:用偏T分布代替常用的伽马分布和逆高斯分布;在预测项中引入惩罚样条函数来描述连续型协变量的非线性效应;考虑索赔强度在不同地区的差异性和相邻地区的相依性。最后基于一组实际的车损险数据进行了实证研究,结果表明,本文的推广模型可以明显提高索赔强度预测模型的拟合优度。     

The influence curve approach in data envelopment analysis

The behavior of efficiency in the stochastic DEA model is examined here in terms of the influence curve approach which quantifies the influence of observed data on the empirical fit of the production frontier. Data influence is analyzed here through robustness. Two types of robustness and sensitivity issues are analyzed here in terms of (a) a new class of minimax measures, and (b) a set nonlinear efficiency measures and it is shown through empirical applications that in suitable cases these measures outperform the conventional ones.This is a revised version of an invited paper presented at the 13th International Symposium on Mathematical Programming held in tokyo, Japan, 29 August 2 September 1988. Sincere thanks are due to Professors A. Charnes and J. Sojka for their valuable comments and suggestions. Thanks are also due to the Guest Editor S. Shinoda and the two anonymous referees for their comments.     

Genetic algorithms in logic tree decision modeling

An important approach to decision modeling is the induction of knowledge structures—such as rules, trees, and graphs—from empirical data describing previous conditions and the resulting decisions. We examine here a specific knowledge structure, a logic tree, in which the conditions are leaves, the decision is the root, and the intermediate nodes are logical operators. We then use genetic algorithms (GAs) to construct logic trees that best represent the correspondence between conditions and decisions described by the empirical data. We also investigate an important characteristic of the GA search, the fitness distance correlation. Finally, we comment on the usefulness of GAs in knowledge modeling.     

A spatial filtering specification for the auto-Poisson model

The auto-Poisson model describes georeferenced data consisting of counts exhibiting spatial dependence. Its conventional specification is plagued by being restricted to only situations involving negative spatial autocorrelation, and an intractable normalizing constant. Work summarized here accounts for spatial autocorrelation in the mean response specification by incorporating latent map pattern components. Results are reported for seven empirical datasets available in the literature.     

线性混合效应模型的正交经验似然推断

基于经验似然方法和QR分解技术, 对线性混合效应模型提出了一个基于正交经验似然的估计方法. 在一些正则条件下, 证明了所提出的经验对数似然比函数渐近服从卡方分布, 进而给出了模型固定效应的置信区间估计. 所提出估计过程不受模型随机效应的影响, 进而保证了所给出的估计是比较有效的. 一些数值模拟和实例分析进一步表明了所提出的估计方法是行之有效的.     

Feature-based elimination: Model and empirical comparison

Experimental and verbal protocol research suggest that consumers appear to use noncompensatory screening strategies to remove alternatives and simplify complex choice situations prior to making a choice. Existing multi-phased choice models assume that the consumer initially evaluates each alternative to determine whether it should pass the first-stage screen and enter the choice set. The feature-based elimination model proposed in this study allows the consumer to avoid processing information for each alternative when forming the choice set. The consumer is assumed to apply a sequence of noncompensatory screens, similar to the elimination-by-aspects strategy, to form the choice set. An empirical application of the model demonstrates that cross-sectional heterogeneity in screening strategies can also be accommodated. One finding from this application is that heterogeneity in screening strategies may be at least as prevalent as heterogeneity in preferences. A comprehensive empirical comparison of the proposed model with existing two-stage models for scanner panel data shows that the model performs at least as well as all existing models and substantially better than most. The empirical performance of the model, coupled with its theoretical appeal and consistency with actual accounts of decision making in complex situations, make the proposed model an appealing alternative to existing multi-phased choice models.     

Symmetry in data mining and analysis: A unifying view based on hierarchy

Data analysis and data mining are concerned with unsupervised pattern finding and structure determination in data sets. The data sets themselves are explicitly linked as a form of representation to an observational, or otherwise empirical, domain of interest. “Structure” has long been understood as symmetry which can take many forms with respect to any transformation, including point, translational, rotational, and many others. Symmetries directly point to invariants that pinpoint intrinsic properties of the data and of the background empirical domain of interest. As our data models change, so too do our perspectives on analyzing data. The structures in data surveyed here are based on hierarchy, represented as p-adic numbers or an ultrametric topology.     

Weighted Empirical Likelihood Ratio Confidence Intervals for the Mean with Censored Data

We propose a procedure to construct the empirical likelihood ratio confidence interval for the mean using a resampling method. This approach leads to the definition of a likelihood function for censored data, called weighted empirical likelihood function. With the second order expansion of the log likelihood ratio, a weighted empirical likelihood ratio confidence interval for the mean is proposed and shown by simulation studies to have comparable coverage accuracy to alternative methods, including the nonparametric bootstrap-t. The procedures proposed here apply in a unified way to different types of censored data, such as right censored data, doubly censored data and interval censored data, and computationally more efficient than the bootstrap-t method. An example of a set of doubly censored breast cancer data is presented with the application of our methods.     

非线性随机效应模型的异方差性检验

随机效应模型广泛应用于刻画重复测量数据的特征．在该模型中,随机误差的方差包括受试群体内部及受试群体之间两项方差．Ｚｈａｎｇ和 Ｗｅｉｓｓ ２０００年研究了线性随机效应模型的异方差检验,本文对非线性随机效应模型,分别讨论了群体内、群体间和多变量的异方差性的检验问题,得到了检验的ｓｃｏｒｅ统计量,并讨论了三种情形下,相应的ｓｃｏｒｅ函数之间的关系．最后给出一个数值例子说明上述方法的有用性．     

Modeling influenza progression within a continuous-attribute heterogeneous population

We consider three attributes of an individual that are critical in determining the temporal dynamics of pandemic influenza: social activity, proneness to infection, and proneness to shed virus and spread infection. These attributes differ by individual, resulting in a heterogeneous population. We develop discrete-time models that depict the evolution of the disease in the presence of such population heterogeneity. For every individual, the value for each of the three describing attributes is viewed as an experimental value of a continuous random variable. The methodology is simple yet general, extending more traditional discrete compartmental models that depict population heterogeneity. Illustrative numerical examples show how individuals who have much larger-than-average values for one or more of the attributes drive the influenza wave, especially in the early generations of the pandemic. This heterogeneity-driven pandemic physics carries important policy implications. We conclude by using contact data in four European countries to demonstrate empirical uses of our model.     

基于面板数据的中国医药制造行业资本结构影响因素实证分析

本文以1998—2002年在上海、深圳证券交易所上市的医药制造行业公司为样本,运用面板数据模型,对决定我国医药制造行业上市公司资本结构的主要因素,进行实证分析。实证结果表明:非观测的个体异质性因素显著;在行业销售模式发生重大变革的前提下,债务特别是短期债务,为销售费用的融资提供了有力的资本支持;具有高增长期权的制约公司债务筹集能力较强;非债务税盾不能构成利息税盾的替代;成长性、规模、资产担保价值和产生内部资源的能力,对中国医药制造行业上市公司资本结构的影响不大。     

Determining the effects of observed and unobserved heterogeneity on consumer brand choice

This paper investigates the effects of heterogeneity in consumer choice behaviour. Omitted consumer heterogeneity may lead to badly biased results, and wrong inferences concerning marketing strategies to follow. In this research we study the extent and the cause of this bias. We distinguish between observed and unobserved heterogeneity, by partialing out the effects of unmeasured heterogeneity and modelling it explicitly. The following questions will be addressed: What is unobserved heterogeneity and how much of it can be explained? How should heterogeneity be incorporated in consumer choice models? A hazard model is used for the analysis. The hazard model will yield patterns of switching among brands, as well as, the effect of marketing mix variables on brand choice and purchase timing. Differences between switchers and repeat purchasers are studied and the extent to which brand choice can be explained. Our model is estimated using scanner panel data. We find that it is important to include both observed and unobserved heterogeneity in order to obtain a better fit of the model. Our results show that it may be sufficient to only include observed heterogeneity to obtain unbiased parameter estimates. Including observed heterogeneity also reduces the aggregation or heterogeneity bias in the hazard function. © 1998 John Wiley & Sons, Ltd.     

Some applications of the strong approximation of the integrated empirical copula processes

The purpose of the present paper is to provide a strong invariance principle for the integrated empirical copula process [introduced in a series of papers by Henze and Nikitin in the univariate setting] with the rate of the approximation for multivariate empirical processes. The applications discussed here are change-point detection in multivariate copula models and the integrated empirical copula process with estimated parameter. Finally, a general notion of bootstrapped integrated empirical copula process, constructed by exchangeably weighting sample, is presented.     

The Efficiency Distribution Approach in Data Envelopment Analysis: An Application

A method is developed here for characterizing the empirical distribution of the efficient units in data envelopment analysis. Two empirical applications illustrate the various uses of the distribution approach. One involves the cost frontier which exhibits increasing returns to scale and the other involves a dynamic production frontier, where technological change causes a shift of the production frontier over time.     

A combined approach for segment-specific market basket analysis

Market baskets arise from consumers’ shopping trips and include items from multiple categories that are frequently chosen interdependently from each other. Explanatory models of multicategory choice behavior explicitly allow for such category purchase dependencies. They typically estimate own and across-category effects of marketing-mix variables on purchase incidences for a predefined set of product categories. Because of analytical restrictions, however, multicategory choice models can only handle a small number of categories. Hence, for large retail assortments, the issue emerges of how to determine the composition of shopping baskets with a meaningful selection of categories. Traditionally, this is resolved by managerial intuition. In this article, we combine multicategory choice models with a data-driven approach for basket selection. The proposed procedure also accounts for customer heterogeneity and thus can serve as a viable tool for designing target marketing programs. A data compression step first derives a set of basket prototypes which are representative for classes of market baskets with internally more distinctive (complementary) cross-category interdependencies and are responsible for the segmentation of households. In a second step, segment-specific cross-category effects are estimated for suitably selected categories using a multivariate logistic modeling framework. In an empirical illustration, significant differences in cross-effects and price elasticities can be shown both across segments and compared to the aggregate model.     

Estimation for incomplete manpower data

Much attention has been paid to both non-parametric and parametric estimation for survival data with right censoring, particularly in the medical literature. In manpower planning the completed length of service until leaving is of great interest, and here also the data are right censored since people are still in service when data collection ends. However, it often occurs that the data are also left truncated since people are already in service at the beginning of data collection. These people have often been neglected both in estimation of the empirical distribution function and also in fitting particular parametric distributions. However, it is important to include them so as to use all the data, particularly when data are only present for a short period. The methods developed were applied to data for the completed length of service of both skilled and unskilled workers where the data were collected over a period of years. Using modified Kaplan-Meier estimation, applied to these data sets, empirical distribution functions were obtained. A number of parametric distributions were also fitted. The goodness of fit of these distributions as predictors of leavers and stayers over a given period was then tested using a chi-squared test.     

CEO权力影响公司股价特质波动吗？——基于公司治理和风险承担双重视角

影响股价特质波动的因素是当前的热点研究问题。本文认为CEO权力通过降低公司风险承担水平、弱化公司治理机制有效性两条途径降低股价特质波动。为验证该基本假设,本文以2011~2017年中国A 股 上市公司为样本进行实证检验,发现在控制相关影响因素后,CEO权力对股价特质波动呈现出显著的负向影响,在非国有上市公司和小型企业中,这种关系更加显著。本研究丰富了有关CEO权力经济后果及股价特质波动影响因素的研究,对CEO权力的合理配置和公司风险管理均具有一定启示。