土壤信息系统的形成发展与建立

土壤信息系统是近年来国内外土壤学研究的热点和前沿学科之一 ,在国民经济和解决全球问题中发挥着重要作用。系统结构、土壤 -土地一体化数据库和精确农业应用研究是当前中国土壤信息系统研究的热点 ,在研究中应注意解决数据采集、更新、编码和应用的开发等问题     

救赎：寻求民族文化的出路——解读莫里森的《柏油孩子》

高速铁路具有高度集成、高精度的技术特点,运营过程中经受列车质量、速度、密度等多种因素影响,地理位置因素往往是影响铁路设备状态演变的决定性因素。采用网格化管理技术可将空间上连续分布的管理对象划分成较小的单元网格,有利于从空间位置角度研究管理对象状态的变化规律。随着信息系统技术、大数据技术的迅猛发展,基于位置而不是基于专业更符合高速铁路的管理需求,网格化管理技术给高速铁路管理带来了新的视角。     

FDI与出口贸易品技术含量关系的实证分析

以FDI与我国出口贸易品技术含量的关系作为切入点,选择通信设备、计算机制造业、医药制造业作为高新技术产业的代表,通用设备制造业、专用设备制造业作为传统制造业的代表。根据这4个行业1997—2011年利用外资与出口贸易品技术含量的数据,基于面板数据模型对高新技术产业和传统制造业FDI与出口贸易品技术含量之间的长期均衡关系及相关性进行实证分析。结果表明:高新技术产业利用外资与出口贸易品技术含量存在着长期均衡稳定关系,出口贸易品技术含量随着高新技术产业利用外资的增加而提高,并有明显的促进作用,而传统制造业利用外资对出口贸易品技术含量的影响有限。     

A unified approach to regression analysis under double-sampling designs

We propose a unified approach to the estimation of regression parameters under double-sampling designs, in which a primary sample consisting of data on the rough or proxy measures for the response and/or explanatory variables as well as a validation subsample consisting of data on the exact measurements are available. We assume that the validation sample is a simple random subsample from the primary sample. Our proposal utilizes a specific parametric model to extract the partial information contained in the primary sample. The resulting estimator is consistent even if such a model is misspecified, and it achieves higher asymptotic efficiency than the estimator based only on the validation data. Specific cases are discussed to illustrate the application of the estimator proposed.     

Clustering transformed compositional data using K-means,with applications in gene expression and bicycle sharing system data

Although there is no shortage of clustering algorithms proposed in the literature, the question of the most relevant strategy for clustering compositional data (i.e. data whose rows belong to the simplex) remains largely unexplored in cases where the observed value is equal or close to zero for one or more samples. This work is motivated by the analysis of two applications, both focused on the categorization of compositional profiles: (1) identifying groups of co-expressed genes from high-throughput RNA sequencing data, in which a given gene may be completely silent in one or more experimental conditions; and (2) finding patterns in the usage of stations over the course of one week in the Velib' bicycle sharing system in Paris, France. For both of these applications, we make use of appropriately chosen data transformations, including the Centered Log Ratio and a novel extension called the Log Centered Log Ratio, in conjunction with the K-means algorithm. We use a non-asymptotic penalized criterion, whose penalty is calibrated with the slope heuristics, to select the number of clusters. Finally, we illustrate the performance of this clustering strategy, which is implemented in the Bioconductor package coseq, on both the gene expression and bicycle sharing system data.     

Probabilistic frontier regression models for binary type output data

This paper proposes a probabilistic frontier regression model for binary type output data in a production process setup. We consider one of the two categories of outputs as ‘selected’ category and the reduction in probability of falling in this category is attributed to the reduction in technical efficiency (TE) of the decision-making unit. An efficiency measure is proposed to determine the deviations of individual units from the probabilistic frontier. Simulation results show that the average estimated TE component is close to its true value. An application of the proposed method to the data related to the Indian public sector banking system is provided where the output variable is the indicator of level of non-performing assets. Individual TE is obtained for each of the banks under consideration. Among the public sector banks, Andhra bank is found to be the most efficient, whereas the United Bank of India is the least.     

Bayesian analysis of human movement curves

Summary.  We consider the Bayesian analysis of human movement data, where the subjects perform various reaching tasks. A set of markers is placed on each subject and a system of cameras records the three-dimensional Cartesian co-ordinates of the markers during the reaching movement. It is of interest to describe the mean and variability of the curves that are traced by the markers during one reaching movement, and to identify any differences due to covariates. We propose a methodology based on a hierarchical Bayesian model for the curves. An important part of the method is to obtain identifiable features of the movement so that different curves can be compared after temporal warping. We consider four landmarks and a set of equally spaced pseudolandmarks are located in between. We demonstrate that the algorithm works well in locating the landmarks, and shape analysis techniques are used to describe the posterior distribution of the mean curve. A feature of this type of data is that some parts of the movement data may be missing—the Bayesian methodology is easily adapted to cope with this situation.     

伸展构造背景下倾角资料的构造解释

系统总结了在伸展构造背景下,导致反映构造特征的地层倾角模式复杂化的多种因素。沉积相的不同、岩性变化、井眼状况和测井作业时间等多种因素都可能使地层倾角资料复杂化。进一步指出在拉张盆地中,进行倾角资料的构造解释,必须了解其构造样式的多样性和复杂性,解释存在的多解性。重点解剖了铲式断层在横剖面情况下,对于不同构造位置下的倾角模式。详细分析了一个倾角模式对应多种构造样式的倾角构造解释实例。倾角资料的构造解释应是综合解释,既要综合井筒的相关地质资料,又要综合测井与地质知识,还必须考虑综合地震资料。     

大数据在重大行政决策中的应用及其法治化

大数据在重大行政决策领域的应用,属于技术理性与决策规则的有机融合。凭借信息来源与技术革新,大数据可赋予传统决策向数字化转型的新动能。大数据的应用贯穿于重大行政决策始终,在市场监管、环境保护等多类事项中所发挥的实践优势较为明显,能够推动决策的高效化、提升决策的精准度、增强决策机关之间的联动。然而,大数据应用可能会引发决策安全的隐忧、个人数据存在的空间被挤压以及决策失误的责任承担等难题。对前述问题提出对策的过程,实乃数据治理向治理数据的转变,此阶段不能欠缺法律法规的引导,应尝试从决策数据安全体系的法治保障、个人数据的法律保护、决策责任追究机制的科学建构等层面去推动大数据应用的法治化。     

浅论企业统计数据质量的弱化

统计数据质量是统计的生命 ,而统计数据质量的弱化一直是人们难以解决的一大难题。统计数据质量弱化的原因是多方面的 ,应采取依法统计、建立经常性数据质量检查制度、引进先进的统计调查方法及规范计算方法等对策 ,认真加以解决。