Estimating the Size of a Criminal Population from Police Records Using the Truncated Poisson Regression Model

The truncated Poisson regression model is used to arrive at point and interval estimates of the size of two offender populations, i.e. drunk drivers and persons who illegally possess firearms. The dependent capture–recapture variables are constructed from Dutch police records and are counts of individual arrests for both violations. The population size estimates are derived assuming that each count is a realization of a Poisson distribution, and that the Poisson parameters are related to covariates through the truncated Poisson regression model. These assumptions are discussed in detail, and the tenability of the second assumption is assessed by evaluating the marginal residuals and performing tests on overdispersion. For the firearms example, the second assumption seems to hold well, but for the drunk drivers example there is some overdispersion. It is concluded that the method is useful, provided it is used with care.     

Multiple generation product life cycle predictions using a novel two-stage fuzzy piecewise regression analysis method

Product life cycle (PLC) prediction plays a crucial role in strategic planning and policy definition for high-technology products. Forecast methodologies which can predict PLCs accurately can help to achieve successful strategic decision-making, forecasting, and foresight activities in high-technology firms, research institutes, governments, and universities. Over the past few decades, even though analytic framework strategies have been proposed for production, marketing, R&D (research and development), and finance, aiming at each stage of PLCs, forecast methodologies with which to predict PLCs are few. The purpose of this research is to develop a novel forecast methodology to allow for predictions of product life time (PLT) and the annual shipment of products during the entire PLC of multiple generation products. A novel two-stage fuzzy piecewise regression analysis method is proposed in this paper. In the first stage, the product life-time of the specific generation to be analyzed will be predicted by the fuzzy piecewise regression line that is derived based upon the product life-time of earlier generations. In the second stage of the forecast methodology, the annual shipment of products of the specified generation will be predicted by deriving annual fuzzy regression lines for each generation, based upon the historical data on the earlier generations' products. An empirical study predicting the life-time and the annual shipment of the 16 Mb (Mega bit) DRAM (Dynamic Random Access Memory) PLC is illustrated to validate the analytical process. The results demonstrate that two-stage fuzzy piecewise regression analysis can predict multiple generation PLT and PLC precisely, thereby serving as a foundation for future strategic planning, policy definitions and foresights.     

军事物流中供应商管理对策研究

从分析在军事物中开展供应商管理的内涵与外延入手,探讨了供应商管理应处理好的几个关系,重点研究了实施供应商管理的程序和方法。     

现代物流产业概念内涵和外延的理论研究

以经济学的市场划分、新兴古典经济学分工演进理论和三次产业分类理论为基础,以联合国标准产业分类法和我国《国民经济行业分类》的行业定义、划分原则和规定为标准,同时兼顾现代产业发展趋势-产业融合的特点。依据物流产品特性界定我国现代物流产业狭义和广义概念。     

我国物流园区建设发展现状及存在问题的分析

物流园区（Logistic park)是指由分布相对集中的多个物流组织设施和不同的专业化物流企业所构成，具有产业组织、经济运行等功能规模化的物流组织区域，我国现代物流发展迅速，尤其是在物流园区的规划与建设方面，取得相当可观的进展和不少有益的经验。本文简要分析近年来我园各地区物流园区规划建设发展状况，并对在这一发展过程中不同程度存在的各种问题进行了分析。     

结构异质性对中国能源高质量发展的影响研究

基于结构异质性对能源高质量发展影响的理论分析,使用稀疏主成分分析方法测度中国能源高质量发展水平,构建中国和八大经济区域面板分位数回归模型,实证研究了结构异质性对中国能源高质量发展的影响。结果表明：2003—2020年中国能源高质量发展水平有所提升,但仍存在明显的区域发展不平衡问题,西部地区和北部沿海地区的能源高质量发展水平更高。产业结构与能源高质量发展水平呈负相关关系,能源消费结构、资本配置结构与能源高质量发展水平呈正相关关系。     

回归分析在土地估价中的应用

回归分析是一种统计学方法，在地价分析与测算中得到广泛应用。本文从统计学的角度阐明了回归分析的原理，并介绍了如何建立回归方程解决土地估价中出现的有关问题。     

探究“新零销售”模式下影响销售量的相关因素

在新零销售模式的推动下,客户越来越倾向于消费个性化的商品。为了精准预测客户的消费需求,论文以单款单色产品为研究对象,通过建立多元回归分析的数学模型,来探究影响商品销售量的相关因素。经过分析发现,除了一些定性因素外,定量因素对销售量也产生了一定的影响,其中实际花费总金额、实际销售单价、库存数等定量因素对销售量的影响较大,且各个变量之间具有相关性,所以电商平台应特别关注这三个变量的影响。     

Prediction of the Indian summer monsoon using a stacked autoencoder and ensemble regression model

The study of climatic variables that govern the Indian summer monsoon has been widely explored. In this work, we use a non-linear deep learning-based feature reduction scheme for the discovery of skilful predictors for monsoon rainfall with climatic variables from various regions of the globe. We use a stacked autoencoder network along with two advanced machine learning techniques to forecast the Indian summer monsoon. We show that the predictors such as the sea surface temperature and zonal wind can predict the Indian summer monsoon one month ahead, whereas the sea level pressure can predict ten months before the season. Further, we also show that the predictors derived from a combination of climatic variables can outperform the predictors derived from an individual variable. The stacked autoencoder model with combined predictors of sea surface temperature and sea level pressure can predict the monsoon (June-September) two months ahead with a 2.8% error. The accuracy of the identified predictors is found to be superior to the state-of-the-art predictions of the Indian monsoon.     

保险公司偿付能力恶化预测研究

文章在分析公司财务预测研究方法应用前提的基础上,运用MDA模型和Logistic线性回归模型预测方法对我国保险公司偿付能力恶化进行预测研究。结果表明两个主流模型均能在保险公司偿付能力恶化前1~2年较好地进行预测,但是由于两类错估率的不同,MDA模型要优于Logistic模型。