DEA方法在潜在GDP估算中的应用

文章总结了前人对潜在国内生产总值(GDP)测算方法研究的成果与不足,对潜在GDP的数据包络分析估算方法进行了探讨。文章深入分析了BCC模型的经济意义,应用数据包络方法的潜在GDP测算法,估计了中国1998～2007年间的宏观经济投资效率和潜在GDP。应用数据包络方法估算潜在GDP,有利于克服传统潜在GDP测算方法的缺陷与不足,从而使潜在GDP的估算更加科学与可靠。     

基于灰色反馈修正的长期预

对于总体趋势增加的原始数据,用灰色动态模型GM(1.1)进行长期预测时,预测值往往偏高,当时间趋向无穷时,预测值将趋向无限大.为了解决这个问题,我们在建模方法上作了改进,提出了一个灰色反馈长期预测模型,简记为LGM(1.1),用此模型对河北省某地粮食产量进行了实验性预测,预测精度较好.     

我国商业银行介入风险投资领域的创新模式设计

探索我国商业银行进入风险投资领域,有利于商业银行跳出传统的成长思维模式,进一步提升其成长和发展的能力.同时,通过商业银行的介入也有利于推动我国风险投资行业的快速发展.而目前我国商业银行进入风险投资领域的模式只处于初级阶段,文章通过创新模式的设计,为探索商业银行将来全面进入风险投资领域提供参考.     

基于波特模型的A物流集团竞争战略研究

文章应用波特五力模型对A物流集团发展的五种力量。即现有企业间竞争、潜在进入者的竞争、替代品的竞争、供应商的竞争和客户的议价能力进行了分析,在此基础上提出该公司可采取的竞争战略．并针对现阶段A物流集团如何提高自身物流竞争能力提出了几点建议。     

结构方程建模在管理学研究中的应用

文章通过分析近几年发表的133篇相关学术论文发现,我国学者已掌握了结构方程建模技术,对建模细节的说明也越来越规范,但在模型识别、等价模型等方面还存在一些问题。通过对结构方程使用规范和应用现状的分析与讨论,使学者能更规范的运用结构方程建模技术,并使其能在管理学研究领域发挥更大的作用。     

网络能力、知识租金获取及集群企业国际化成长的关系研究

文章通过构建网络能力、知识租金获取与集群企业国际化成长关系的理论模型,揭示集群企业如何运用网络能力激活蕴含在网络环境中的知识租金从而实现集群企业的国际化成长的微观机理。利用苏州外向型集群企业的问卷调查所获得数据,运用结构方程模型对该上述模型进行实证分析,结果表明网络能力对集群企业的国际化成长有显著的正向影响,网络能力还能通过影响知识租金的获取从而对集群企业的国际化成长产生正向影响。     

基于CDM模型的创新测度研究

  本文介绍创新调查分析领域比较流行的CDM模型的由来、结构及其在创新调查分析领域的应用，依据全国工业企业创新调查指标对CDM模型进行了改进，尝试建立一个适合进行中国企业创新的微观实证研究的模型框架。     

人才流动变化的随机模型

随机过程应用的不断发展与进步,使得其应用范围越来越广,特别是在工程技术、生物学与生命科学、管理、经济金融等领域都取得了较好的应用效果。本文试图对我国人才市场中人才流动的变化规律进行分析,并建立一个随机模型。运用该模型可对我国人才市场中人才流动的变化进行近期的     

基于FAHP的个人信用评分模型

在经济、金融快速发展,信用交易日益频繁的中国,个人信用评分的建设仍然是个薄弱环节.文章基于运用FAHP法构建了个人信用评分模型,并通过案例分析对模型进行了验证.结果显示,引入FAHP法简化了模型的构建过程,模型运行结果有效、合理,值得在个人信用评分领域推广.     

单变量时间序列模型的识别

时间序列分析就是通过研究时间序列中数值上的统计关系,来揭示系统的动态结构特征及其发展变化规律,是一种重要的现代统计分析方法,广泛地应用于自然领域、社会领域的科学研究和思维。在时间序列变量建模的过程中,一般分为模型识别、模型估计和诊断以及模型预测三个步骤,其中模     

The effects of individually varying times of observations on growth parameter estimations in piecewise growth model

When using latent growth modeling (LGM), researchers often restrict the factor loadings, while the multilevel modeling (MLM) treats time as a metric variable. However, when individually varying times of observations are concerned in the longitudinal studies, the use of specified loadings would lead to inaccurate estimation. Based on piecewise growth modeling (PGM), this simulation study showed that (i) individually varying times of observations with larger boundaries got worse estimates and model fits when LGM was used; (ii) estimating the PGM across all the simulation situations was robust within MLM, whereas LGM got identically equal estimation with MLM only in the case of time boundaries of ±1 month or shorter; (iii) larger change of slope in piecewise modeling indicated better estimation.     

物流业公共交通基础设施动态趋势分析——基于中国2001—2012年省域面板数据

采用线性潜变量发展模型对中国物流业公共交通基础设施的动态发展趋势进行实证分析。研究表明:就整体而言,中国物流业公共交通基础设施初始平均水平较高,但不能为其后续发展提供足够的动力和支撑。就省域间比较而言,无论从空间地域上,还是时间维度上,均存在着显著的不均衡,且这种不均衡表现出逐年扩大的趋势。     

A functional data approach to model score difference process in professional basketball games

In this paper, we investigate the progress of score difference (between home and away teams) in professional basketball games employing functional data analysis (FDA). The observed score difference is viewed as the realization of the latent intensity process, which is assumed to be continuous. There are two major advantages of modeling the latent score difference intensity process using FDA: (1) it allows for arbitrary dependent structure among score change increments. This removes potential model mis-specifications and accommodates momentum which is often observed in sports games. (2) further statistical inferences using FDA estimates will not suffer from inconsistency due to the issue of having a continuous model yet discretely sampled data. Based on the FDA estimates, we define and numerically characterize momentum in basketball games and demonstrate its importance in predicting game outcomes.     

An example of a two-part latent growth curve model for semicontinuous outcomes in the health sciences

A new method of modeling coronary artery calcium (CAC) is needed in order to properly understand the probability of onset and growth of CAC. CAC remains a controversial indicator of cardiovascular disease (CVD) risk, but this may be due to ill-equipped methods of specifying CAC during the analysis phase of studies reporting an analysis where CAC is the primary outcome. The modern method of two-part latent growth modeling may represent a strong alternative to the myriad of existing methods for modeling CAC. We provide a brief overview of existing methods of analysis used for CAC before introducing the general latent growth curve model, how it extends into a two-part (semicontinuous) growth model, and how the ubiquitous problem of missing data can be effectively handled. We then present an example of how to model CAC using this framework. We demonstrate that utilizing this type of modeling strategy can result in traditional predictors of CAC (e.g. age, gender, and high-density lipoprotein cholesterol), exerting a different impact on the two different, yet simultaneous, operationalizations of CAC. This method of analyzing CAC could inform future analyses of CAC and inform subsequent discussions about the nature of its potential to inform long-term CVD risk and heart events.     

Markov chain Monte Carlo with the Integrated Nested Laplace Approximation

The Integrated Nested Laplace Approximation (INLA) has established itself as a widely used method for approximate inference on Bayesian hierarchical models which can be represented as a latent Gaussian model (LGM). INLA is based on producing an accurate approximation to the posterior marginal distributions of the parameters in the model and some other quantities of interest by using repeated approximations to intermediate distributions and integrals that appear in the computation of the posterior marginals. INLA focuses on models whose latent effects are a Gaussian Markov random field. For this reason, we have explored alternative ways of expanding the number of possible models that can be fitted using the INLA methodology. In this paper, we present a novel approach that combines INLA and Markov chain Monte Carlo (MCMC). The aim is to consider a wider range of models that can be fitted with INLA only when some of the parameters of the model have been fixed. We show how new values of these parameters can be drawn from their posterior by using conditional models fitted with INLA and standard MCMC algorithms, such as Metropolis–Hastings. Hence, this will extend the use of INLA to fit models that can be expressed as a conditional LGM. Also, this new approach can be used to build simpler MCMC samplers for complex models as it allows sampling only on a limited number of parameters in the model. We will demonstrate how our approach can extend the class of models that could benefit from INLA, and how the R-INLA package will ease its implementation. We will go through simple examples of this new approach before we discuss more advanced applications with datasets taken from the relevant literature. In particular, INLA within MCMC will be used to fit models with Laplace priors in a Bayesian Lasso model, imputation of missing covariates in linear models, fitting spatial econometrics models with complex nonlinear terms in the linear predictor and classification of data with mixture models. Furthermore, in some of the examples we could exploit INLA within MCMC to make joint inference on an ensemble of model parameters.     

Accelerated failure time models for the analysis of competing risks

Competing risks are common in clinical cancer research, as patients are subject to multiple potential failure outcomes, such as death from the cancer itself or from complications arising from the disease. In the analysis of competing risks, several regression methods are available for the evaluation of the relationship between covariates and cause-specific failures, many of which are based on Cox’s proportional hazards model. Although a great deal of research has been conducted on estimating competing risks, less attention has been devoted to linear regression modeling, which is often referred to as the accelerated failure time (AFT) model in survival literature. In this article, we address the use and interpretation of linear regression analysis with regard to the competing risks problem. We introduce two types of AFT modeling framework, where the influence of a covariate can be evaluated in relation to either a cause-specific hazard function, referred to as cause-specific AFT (CS-AFT) modeling in this study, or the cumulative incidence function of a particular failure type, referred to as crude-risk AFT (CR-AFT) modeling. Simulation studies illustrate that, as in hazard-based competing risks analysis, these two models can produce substantially different effects, depending on the relationship between the covariates and both the failure type of principal interest and competing failure types. We apply the AFT methods to data from non-Hodgkin lymphoma patients, where the dataset is characterized by two competing events, disease relapse and death without relapse, and non-proportionality. We demonstrate how the data can be analyzed and interpreted, using linear competing risks regression models.     

A multiple group item response theory model with centered skew-normal latent trait distributions under a Bayesian framework

Very often, in psychometric research, as in educational assessment, it is necessary to analyze item response from clustered respondents. The multiple group item response theory (IRT) model proposed by Bock and Zimowski [12] provides a useful framework for analyzing such type of data. In this model, the selected groups of respondents are of specific interest such that group-specific population distributions need to be defined. The usual assumption for parameter estimation in this model, which is that the latent traits are random variables following different symmetric normal distributions, has been questioned in many works found in the IRT literature. Furthermore, when this assumption does not hold, misleading inference can result. In this paper, we consider that the latent traits for each group follow different skew-normal distributions, under the centered parameterization. We named it skew multiple group IRT model. This modeling extends the works of Azevedo et al. [4], Bazán et al. [11] and Bock and Zimowski [12] (concerning the latent trait distribution). Our approach ensures that the model is identifiable. We propose and compare, concerning convergence issues, two Monte Carlo Markov Chain (MCMC) algorithms for parameter estimation. A simulation study was performed in order to evaluate parameter recovery for the proposed model and the selected algorithm concerning convergence issues. Results reveal that the proposed algorithm recovers properly all model parameters. Furthermore, we analyzed a real data set which presents asymmetry concerning the latent traits distribution. The results obtained by using our approach confirmed the presence of negative asymmetry for some latent trait distributions.     

Modeling Longitudinal Obesity Data with Intermittent Missingness Using a New Latent Variable Model

We propose a latent variable model for informative missingness in longitudinal studies which is an extension of latent dropout class model. In our model, the value of the latent variable is affected by the missingness pattern and it is also used as a covariate in modeling the longitudinal response. So the latent variable links the longitudinal response and the missingness process. In our model, the latent variable is continuous instead of categorical and we assume that it is from a normal distribution. The EM algorithm is used to obtain the estimates of the parameter we are interested in and Gauss–Hermite quadrature is used to approximate the integration of the latent variable. The standard errors of the parameter estimates can be obtained from the bootstrap method or from the inverse of the Fisher information matrix of the final marginal likelihood. Comparisons are made to the mixed model and complete-case analysis in terms of a clinical trial dataset, which is Weight Gain Prevention among Women (WGPW) study. We use the generalized Pearson residuals to assess the fit of the proposed latent variable model.     

Semi-parametric survival analysis via Dirichlet process mixtures of the First Hitting Time model

Time-to-event data often violate the proportional hazards assumption inherent in the popular Cox regression model. Such violations are especially common in the sphere of biological and medical data where latent heterogeneity due to unmeasured covariates or time varying effects are common. A variety of parametric survival models have been proposed in the literature which make more appropriate assumptions on the hazard function, at least for certain applications. One such model is derived from the First Hitting Time (FHT) paradigm which assumes that a subject’s event time is determined by a latent stochastic process reaching a threshold value. Several random effects specifications of the FHT model have also been proposed which allow for better modeling of data with unmeasured covariates. While often appropriate, these methods often display limited flexibility due to their inability to model a wide range of heterogeneities. To address this issue, we propose a Bayesian model which loosens assumptions on the mixing distribution inherent in the random effects FHT models currently in use. We demonstrate via simulation study that the proposed model greatly improves both survival and parameter estimation in the presence of latent heterogeneity. We also apply the proposed methodology to data from a toxicology/carcinogenicity study which exhibits nonproportional hazards and contrast the results with both the Cox model and two popular FHT models.

     

A Bayesian phase I/II design to determine subgroup-specific optimal dose for immunotherapy sequentially combined with radiotherapy

Sequential administration of immunotherapy following radiotherapy (immunoRT) has attracted much attention in cancer research. Due to its unique feature that radiotherapy upregulates the expression of a predictive biomarker for immunotherapy, novel clinical trial designs are needed for immunoRT to identify patient subgroups and the optimal dose for each subgroup. In this article, we propose a Bayesian phase I/II design for immunotherapy administered after standard-dose radiotherapy for this purpose. We construct a latent subgroup membership variable and model it as a function of the baseline and pre-post radiotherapy change in the predictive biomarker measurements. Conditional on the latent subgroup membership of each patient, we jointly model the continuous immune response and the binary efficacy outcome using plateau models, and model toxicity using the equivalent toxicity score approach to account for toxicity grades. During the trial, based on accumulating data, we continuously update model estimates and adaptively randomize patients to admissible doses. Simulation studies and an illustrative trial application show that our design has good operating characteristics in terms of identifying both patient subgroups and the optimal dose for each subgroup.