Linear scalar-on-surface random effects regression models

Many research fields increasingly involve analyzing data of a complex structure. Models investigating the dependence of a response on a predictor have moved beyond the ordinary scalar-on-vector regression. We propose a regression model for a scalar response and a surface (or a bivariate function) predictor. The predictor has a random component and the regression model falls in the framework of linear random effects models. We estimate the model parameters via maximizing the log-likelihood with the ECME (Expectation/Conditional Maximization Either) algorithm. We use the approach to analyze a data set where the response is the neuroticism score and the predictor is the resting-state brain function image. In the simulations we tried, the approach has better performance than two other approaches, a functional principal component regression approach and a smooth scalar-on-image regression approach.     

A Shiny Update to an Old Experiment Game

ABSTRACT

Games can be a powerful tool for learning about statistical methodology. Effective game design involves a fine balance between caricature and realism, to simultaneously illustrate salient concepts in a controlled setting and serve as a testament to real-world applicability. Striking that balance is particularly challenging in response surface and design domains, where real-world scenarios often play out over long time scales, during which theories are revised, model and inferential techniques are improved, and knowledge is updated. Here, I present a game, borrowing liberally from one first played over 40 years ago, which attempts to achieve that balance while reinforcing a cascade of topics in modern nonparametric response surfaces, sequential design, and optimization. The game embeds a blackbox simulation within a shiny app whose interface is designed to simulate a realistic information–availability setting, while offering a stimulating, competitive environment wherein students can try out new methodology, and ultimately appreciate its power and limitations. Interface, rules, timing with course material, and evaluation are described, along with a “case study” involving a cohort of students at Virginia Tech. Supplementary materials for this article are available online.     

Testing for parameter constancy in the time series direction in panel data models

We propose tests for parameter constancy in the time series direction in panel data models. We construct a locally best invariant test based on Tanaka [Time series analysis: nonstationary and noninvertible distribution theory. New York: Wiley; 1996] and an asymptotically point optimal test based on Elliott and Müller [Efficient tests for general persistent time variation in regression coefficients. Rev Econ Stud. 2006;73:907–940]. We derive the limiting distributions of the test statistics as T→∞ while N is fixed, and calculate the critical values by applying numerical integration and response surface regression. Simulation results show that the proposed tests perform well if we apply them appropriately.     

Three-group ROC predictive analysis for ordinal outcomes

Measuring the accuracy of diagnostic tests is crucial in many application areas including medicine, machine learning, and credit scoring. The receiver operating characteristic (ROC) surface is a useful tool to assess the ability of a diagnostic test to discriminate among three-ordered classes or groups. In this article, nonparametric predictive inference (NPI) for three-group ROC analysis for ordinal outcomes is presented. NPI is a frequentist statistical method that is explicitly aimed at using few modeling assumptions, enabled through the use of lower and upper probabilities to quantify uncertainty. This article also includes results on the volumes under the ROC surfaces and consideration of the choice of decision thresholds for the diagnosis. Two examples are provided to illustrate our method.     

A Bayesian hierarchical approach to dual response surface modelling

In modern quality engineering, dual response surface methodology is a powerful tool to model an industrial process by using both the mean and the standard deviation of the measurements as the responses. The least squares method in regression is often used to estimate the coefficients in the mean and standard deviation models, and various decision criteria are proposed by researchers to find the optimal conditions. Based on the inherent hierarchical structure of the dual response problems, we propose a Bayesian hierarchical approach to model dual response surfaces. Such an approach is compared with two frequentist least squares methods by using two real data sets and simulated data.     

Joint modified block replacement and production/inventory control policy for a failure-prone manufacturing cell

This paper considers a joint preventive maintenance (PM) and production/inventory control policy of an unreliable single machine, mono-product manufacturing cell with stochastic non-negligible corrective and preventive delays. The production/inventory control policy, which is based on the hedging point policy (HPP), consists in building and maintaining a safety stock of finished products in order to respond to demand and to avoid shortages during maintenance actions. Without considering the impact of preventive and corrective actions on the overall performance of the production system, most authors working in the reliability and maintainability domains confirm that the age-based preventive maintenance policy (ARP) outperforms the classical block-replacement policy (BRP). In order to reduce wastage incurred by the classical BRP, we consider a modified block replacement policy (MBRP), which consists in canceling a preventive maintenance action if the time elapsed since the last maintenance action exceeds a specified time threshold. The main objective of this paper is to determine the joint optimal policy that minimizes the overall cost, which is composed of corrective and preventive maintenance costs as well as inventory holding and backlog costs. A simulation model mimicking the dynamic and stochastic behavior of the manufacturing cell, based on more realistic considerations of the real behavior of industrial manufacturing cells, is proposed. Based on simulation results, the joint optimal MBRP/HPP parameters are obtained through a numerical approach that combines design of experiment, analysis of variance and response surface methodologies. The joint optimal MBRP/HPP policy is compared to classical joint ARP/HPP and BRP/HPP optimal policies, and the results show that the proposed MBRP/HPP outperforms the latter. Sensitivity analyses are also carried out in order to confirm the superiority of the proposed MBRP/HPP, and it is observed that for practitioners, the proposed joint MBRP/HPP offers not only cost savings, but is also easy to manage, as compared to the ARP/HPP policy.     

外汇市场冲击与国内资本市场波动的关联机理解析

运用三元VAR-GARCH(1,1)-Asymmetric-BEKK模型,解析了汇市、股市和债市间的关联机制。结果显示,汇市对股市和债市有显著的均值与波动溢出,而股市和债市对汇市仅有波动溢出,且汇市对股市的溢出效应大于对债市的溢出。基于新闻冲击曲面的分析揭示了资本市场对汇市冲击呈明显的非对称性。为此,政府应将金融市场开放度的扩大与资本流动的监管相结合,构建针对资本市场风险的多层次测度与监控体系,并完善资本市场的制度架构,增强信息披露的有效性。     

辐射计观测模拟系统的设计与研究

基于物理参数反演和卫星数据同化系统的发展需要,设计了一个结合陆面、大气和传感器特征,基于辐射传输模型的辐射计观测数据模拟系统。综合考虑地形、地表异质性、大气等影响,可以直接应用到数据同化系统,进而为校正数据同化模式的静态参数提供基础,同时提高对辐射机理的理解和认识,促进地球物理参数反演。利用该模拟系统,模拟了先进微波扫描辐射计(AMSR-E)的全球观测,并与卫星实测数据进行了对比,对粗糙度进行敏感性分析,其影响可达38 K。     

考虑油膜结合面的机床动态特性分析

考虑油膜结合面特性的机床主运动板的动态特性分析,对油膜的刚度分布情况作了3种假设,将假设得到的机床动态特性与不考虑油膜的特性进行比较,并用实验测试数据进行验证正确性。结果表明考虑油膜即结合面的特性对模态分析结果产生不可忽略的影响,其影响效果从第5阶模态振型开始表现明显;油膜刚度成分布幂函数最接近实验测试结果,因此可以看做油膜工作状态下分布的简化函数。     

英语语法教学中显性意义的基础性地位

近年来,在英语语法教学中,语法形式在不同语境中的隐性意义被高度关注,这弥补了以往语法教学只注重形式和其显性意义的不足。但隐性意义的推理和理解离不开显性意义的掌握,因为隐性意义的获得,依赖于显性意义和语境的结合。简要介绍语法形式显性意义和隐性意义的概念,并通过隐性意义推理过程中基本因素的分析,揭示显性意义的基础性地位,强...