Bootstrapping data with multiple levels of variation

The authors consider general estimators for the mean and variance parameters in the random effect model and in the transformation model for data with multiple levels of variation. They show that these estimators have different distributions under the two models unless all the variables have Gaussian distributions. They investigate the asymptotic properties of bootstrap procedures designed for the two models. They also report simulation results and illustrate the bootstraps using data on red spruce trees.     

PREDICTION OF THE FINITE POPULATION DISTRIBUTION FUNCTION UNDER GAUSSIAN SUPERPOPULATION MODELS

This article considers optimal prediction of the finite population distribution function under Gaussian superpopulation models, which allows auxiliary prior information to be incorporated into the estimation process. Large sample approximations for the variance of the optimal predictors are derived in some special important cases. A small scale Monte Carlo study illustrates comparisons between the optimal predictor and some others which are proposed in the literature. The conclusion is that the optimal predictor can be considerably more efficient in situations where the normal superpopulation model is adequate.     

Minimax estimation of a probability of success under LINEX loss

Minimax estimation of a binomial probability under LINEX loss function is considered. It is shown that no equalizer estimator is available in the statistical decision problem under consideration. It is pointed out that the problem can be solved by determining the Bayes estimator with respect to a least favorable distribution having finite support. In this situation, the optimal estimator and the least favorable distribution can be determined only by using numerical methods. Some properties of the minimax estimators and the corresponding least favorable prior distributions are provided depending on the parameters of the loss function. The properties presented are exploited in computing the minimax estimators and the least favorable distributions. The results obtained can be applied to determine minimax estimators of a cumulative distribution function and minimax estimators of a survival function.     

A proposed methodology for the prediction of mental workload, based on engineering system parameters

In the design of engineering systems, mental workload is one of the most important factors in the allocation of cognitive tasks. Current methods of task allocation have criteria that are defined in only general terms and are thus not very useful in aiding detailed decision-making in system design. Whilst there are many quantitative criteria available to determine the physical space in human-machine interaction, system designers really require an explicit model and specific criteria for the following identification of the mental workload imposed by the system; prediction of both human and system performance; evaluation of the alternatives of system design; and the design of system components. It is argued that the available methods of workload or performance are either too domain-dependent to apply to the design of other systems, or subject-dependent and thus do not reflect the objective workload imposed by the system. The presented research adopts a new approach to cognitive task analysis in dynamic decision-making systems. Based on the characteristics derived from task analysis, a general conceptual model of the prediction of mental workload in system design is proposed. In the new model, workload is represented by a set of system parameters—task arrival rate, task complexity, task uncertainty, and performance requirements—which are considered to be the main sources of workload. In this context, workload becomes an objective demand of engineering systems, independent of any subjective factors. Whether an individual or population is overloaded depends upon their workload threshold with respect to the specified task and environment. It is hoped that this new model, after both laboratory and industrial validation, could be used by system designers to predict the workload imposed on people by systems.     

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.