通风机性能的微机全自动测试与数据处理系统

介绍一种通风机性能微机全自动测试与数据处理系统,阐述了该系统的组成和功能,传感器和主要设备的选用,系统软件的设计及提高系统测试精度所采用的方法。工业应用证实该系统性能良好。     

Bivariate frailty model for the analysis of multivariate survival time

Because of limitations of the univariate frailty model in analysis of multivariate survival data, a bivariate frailty model is introduced for the analysis of bivariate survival data. This provides tremendous flexibility especially in allowing negative associations between subjects within the same cluster. The approach involves incorporating into the model two possibly correlated frailties for each cluster. The bivariate lognormal distribution is used as the frailty distribution. The model is then generalized to multivariate survival data with two distinguished groups and also to alternating process data. A modified EM algorithm is developed with no requirement of specification of the baseline hazards. The estimators are generalized maximum likelihood estimators with subject-specific interpretation. The model is applied to a mental health study on evaluation of health policy effects for inpatient psychiatric care.     

Improvements over the duplicate performance method for outlier detection in categorical multivariate surveys

Summary The detection of errors and outliers is an important step in data processing, especially those errors arising from data entry operations because they are of the entire responsability of the data processing staff. The duplicate performance method, is commonly used as an attempt to detect such type of errors. It implies typically typing twice the same data without any special precedence. If the errors are uniformly distributed among individuals, retyping a fraction of the total will also remove typically the same fraction of the errors. A new method is presented, which is able to improve that procedure by sorting the records putting first the most unlikely ones. The ability of the present methodology has been tested by a Monte Carlo simulation, using an existing database of categorical answers of housing characteristics in Uruguay. At first, it has been randomly contaiminated, and after that, the proposed procedure applied. The results show that if a partial retyping is done following the proposed order about 50 % of the errors can be removed while keeping the retyping effort between 4 and 14% of the dataset, while to attain a similar result with the standard methodology 50% (on, average) of the database should be processed. The new ordering is based upon the unrotated Principal Component Analysis (PCA) transformation of the previously coded data. No special shape of the multivariate distribution function is assumed or required.     

我国和西欧地区机床工业企业管理的抽样比较研究

本文通过抽样数据分析对我国和西欧地区的机床工业企业的生产管理进行评价比较。文中首先应用多集合的数据包络分析(DEA)方法,选用3个投入变量和2个产出变量对之进行相对有效性的综合评价。然后,应用t检验和Kolmogorov-Smirnov检验对生产管理的诸方面作显著性差异的检验。指出差距所在,并提出了一些改进管理的参考意见。     

基于机器学习LSTM&US模型的消费者信心指数预测研究

消费者信心指数等宏观经济指标具有时间上的滞后效应和动态变化的多维性,不易精确预测。本文基于机器学习长短时间记忆(Long Short-Term Memory,LSTM)神经网络模型,结合大数据技术挖掘消费者信心指数相关网络搜索数据(User Search,US),进而构建一种LSTM&US预测模型,并将其应用于对我国消费者信心指数的长期、中期与短期的预测研究,同时引入多个基准预测模型进行了对比分析。结果发现:引入网络搜索数据能够提高LSTM神经网络模型的预测性能与预测精度;LSTM&US预测模型具有较好的泛化能力,对不同期限的预测效果均较稳定,其预测性能与预测精度均优于其他六种基准预测模型(LSTM、SVR&US、RFR&US、BP&US、XGB&US和LGB&US);预测结果显示本文提出的LSTM&US预测模型具有一定的实用价值,该预测方法为消费者信心指数的预测与预判提供了一种新的研究思路,丰富了机器学习方法在宏观经济指标预测领域中的理论研究。     

A State Space Approach to Extracting the Signal From Uncertain Data

Most macroeconomic data are uncertain—they are estimates rather than perfect measures of underlying economic variables. One symptom of that uncertainty is the propensity of statistical agencies to revise their estimates in the light of new information or methodological advances. This paper sets out an approach for extracting the signal from uncertain data. It describes a two-step estimation procedure in which the history of past revisions is first used to estimate the parameters of a measurement equation describing the official published estimates. These parameters are then imposed in a maximum likelihood estimation of a state space model for the macroeconomic variable.     

Why don’t we care more about carelessness? Understanding the causes and consequences of careless participants

ABSTRACT

Although careless respondents have wreaked havoc on research for decades, the prevalence and implications of these participants has likely increased due to many new methodological techniques currently in use. Across three studies, we examined the prevalence of careless responding in participants, several means of predicting careless respondents, and the implications of careless respondents on data quality and recruitment attempts. At the same time, we sought to examine the geographic differences of careless responding and provide psychometric evidence for using embedded questions to detect these participants. In Study 1, we found over 1/3^rd of participants showed some evidence of careless responding and that careless respondents exhibited certain personality and demographic characteristics. In particular, carelessness seemed more prevalent in Asian samples than in North American samples. In Study 2, we found nearly 1/4^th of participants showed evidence of careless responding and that conclusions based on data including versus excluding these participants differed in significant yet unpredictable ways. Finally, in Study 3, we found that nearly 2/3^rd of participants who signed up for the study did not meet advertised study requirements for participation and including these participants changed the structure of the data obtained.     

Exploring the efficiency of Mexican universities: Integrating Data Envelopment Analysis and Multidimensional Scaling

National policy initiatives require the expenditure of large amounts of resources over several years. It is common for these initiatives to generate large amounts of data that are needed in order to assess their success. Educational policies are an obvious example. Here we concentrate on Mexico׳s “Educational Modernisation Programme” and try to see how this plan has affected efficiency in teaching and research at Mexico׳s universities. We use a combined approach that includes traditional ratios together with Data Envelopment Analysis models. This mixture allows us to assess changes in efficiency at each individual university and explore if these changes are related to teaching, to research, or to both. Using official statistics for 55 universities over a six year period (2007–2012), we have generated 12 ratios and estimated 21 DEA models under different definitions of efficiency. In order to make the results of the analysis accessible to the non-specialist we use models that visualise the main characteristics of the data, in particular scaling models of multivariate statistical analysis. Scaling models highlight the important aspects of the information contained in the data. Because the data is three-way (variables, universities, and years) we have chosen the Individual Differences Scaling model of Carroll and Chang. We complete the paper with a discussion of efficiency evolution in three universities.     

Network visualization and problem-solving support: A cognitive fit study

This study examines the relative effectiveness of four different social network representations for improving human problem-solving accuracy and speed: node-link diagrams, adjacency matrices, tables, and text. Results suggest that visual network representations improve problem-solving accuracy and speed, compared with text. Among the visual representations, tables produced superior problem-solving outcomes for symbolic tasks and link-node diagrams produced superior problem-solving outcomes for spatial tasks. These results partially support a cognitive fit model of problem-solving support. There is not “one best way” to represent network data. Instead, it is important to match network representations and problem-solving tasks.