Aggressive Behaviors in Social Interaction and Developmental Adaptation: A Narrative Analysis of Interpersonal Conflicts During Early Adolescence

Using conflict narratives reported by adolescents in grade 7 (mean age = 13.4 years), this study investigated the interactional properties and developmental functions of four types of aggressive behaviors: social aggression, direct relational aggression, physical aggression, and verbal aggression. A total of 475 participants from the Carolina Longitudinal Study ( Cairns & Cairns, 1994 ) were included. Results showed that the majority of conflict interactions involved more than a dyad. The use of social aggression (e.g., concealed social attack) was associated with more individuals involved in the conflict. Social aggression was primarily reported as an initiating behavior for interpersonal conflicts, while direct relational aggression was a responding behavior. Medium to high levels of reciprocity were found for physical, verbal, and direct relational aggression, whereas a low level of reciprocity was reported for social aggression. School authorities were most likely to intervene in physical aggression. The use of social aggression was associated with higher network centrality among adolescents. Developmental maladjustment in late adolescence and early adulthood was primarily predicted by physical aggression.     

重庆直辖市优势工业选择研究

分别运用层次分析模型和最优动态组合预测模型对重庆市工业结构进行多指标静态分析与单指标动态预测,科学地得出重庆市优势工业选择的结论。     

基于路径的多元标准回归模型及其应用

在多元回归模型建立的过程中,由于各个变量的测量单位不相同,多元回归模型无法正确地评估出各个分量对预测值直接的和间接的影响.为此,引入路径系数,将多元回归模型转化为多元标准回归模型,通过变量间的相关系数和多元标准回归模型,分析预测变量对响应变量的直接和间接影响.     

中国数字经济发展综合评价与预测

文章结合数字经济的内涵和外延,分别从数字基础设施、数字产业化、产业数字化等方面构建了数字经济发展水平量化指标体系,基于熵值法测度了中国2010—2018年数字经济发展趋势,并建立灰色预测模型对2019—2028年的数字经济发展走向进行预测。研究结果表明:2010—2018年,中国数字经济发展水平增长了4.4倍,年均增速达到50%,2019—2028年,中国数字经济发展水平增速将不断提高,预计2028年数字经济发展水平将是2018年的5倍。因此,我国要进一步推动新型数字经济基础设施建设,加快传统产业的数字化转型,积极发展电信业等数字产业,以保障我国数字经济快速发展。     

Bayesian prediction for progressively censored data from the Burr model

Received: August 14, 2000; revised version: April 23, 2001     

Some results on bootstrap prediction intervals

We investigate the construction of a BC_a-type bootstrap procedure for setting approximate prediction intervals for an efficient estimator θ_m of a scalar parameter θ, based on a future sample of size m. The results are also extended to nonparametric situations, which can be used to form bootstrap prediction intervals for a large class of statistics. These intervals are transformation-respecting and range-preserving. The asymptotic performance of our procedure is assessed by allowing both the past and future sample sizes to tend to infinity. The resulting intervals are then shown to be second-order correct and second-order accurate. These second-order properties are established in terms of min(m, n), and not the past sample size n alone.     

基于行业细分的上市公司经营失败预警研究

依据文献本文构建了一套包括17个指标的经营失败预警指标体系,根据2001-2005年上市公司的财务报表数据,采用Logistic回归,在区分制造和非制造两类行业基础上建立企业经营失败的预警模型。研究显示,分行业预警模型不仅总体预测精度高于混合行业模型,而且犯一类错误的概率明显低于混合行业模型,大大降低预测风险,具有较高的推广价值。     

ANOMALIES, PROBINGS, INSIGHTS: KEN FOREMAN'S ROLE IN THE SAMPLING INFERENCE CONTROVERSY OF THE LATE 20TH CENTURY

This paper is based on the author's recollections of work done for and with E.K. Foreman, and later developments of that work. It describes most particularly the author's early intimations of the potential for using prediction models in survey sampling, the reasons why the use of such models, especially for inferential purposes, was so strongly resisted in the second half of the 20th century, the manner in which randomization and prediction inferences can be combined, and some advantages flowing from that combination.     

人才预测方法在高校人才预测中的应用

介绍了高校人才预测的方法和原则，并运用回归预测、灰色预测、马尔科夫预测等方法对某高校2005～2010年的专业人才数量和人才的学历结构进行了预测，预测结果符合人才发展规律，切实可行。     

Temporal Aggregation of Stationary and Non-stationary Continuous-Time Processes

Abstract.  We study the autocorrelation structure of aggregates from a continuous-time process. The underlying continuous-time process or some of its higher derivative is assumed to be a stationary continuous-time auto-regressive fractionally integrated moving-average (CARFIMA) process with Hurst parameter H . We derive closed-form expressions for the limiting autocorrelation function and the normalized spectral density of the aggregates, as the extent of aggregation increases to infinity. The limiting model of the aggregates, after appropriate number of differencing, is shown to be some functional of the standard fractional Brownian motion with the same Hurst parameter of the continuous-time process from which the aggregates are measured. These results are then used to assess the loss of forecasting efficiency due to aggregation.