Methods for Generating Longitudinally Correlated Binary Data

The analysis of longitudinally correlated binary data has attracted considerable attention of late. Since the estimation of parameters in models for such data is based on asymptotic theory, it is necessary to investigate the small‐sample properties of estimators by simulation. In this paper, we review the mechanisms that have been proposed for generating longitudinally correlated binary data. We compare and contrast these models with regard to various features, including computational efficiency, flexibility and the range restrictions that they impose on the longitudinal association parameters. Some extensions to the data generation mechanism originally suggested by Kanter (1975) are proposed.     

WEB‐BASED AND MAIL SURVEYS: A COMPARISON OF RESPONSE,DATA, AND COST

The mail survey is the most common data collection technique used by logistics researchers today. Many researchers have noted that response rates to mail surveys have been declining. The Internet offers logistics researchers several potential advantages over traditional mail surveys. This research compares these two methods across response rates, speed of response, consistency of results, and cost. The research also provides unique insight regarding the ability of electronic methods to gain interest and subsequent participation among potential respondents thereby helping in theory testing.     

On the efficiency of national innovation systems

This paper presents a non-parametric approach to the disentanglement of the related effects of efficiency and productivity of a country's technological effort. The ultimate purpose is to identify the extent to which the alleged decrease in the productivity growth of many countries can be explained by differences in efficiency and by differences in its components, namely scale and congestion. The role of moderators in explaining productivity fluctuations if also assessed. The database consists of the countries included in the World Competitiveness Report.     

Using machine learning algorithms to predict the risk of small Unmanned Aircraft System violations in the National Airspace System

The increasing number of small Unmanned Aircraft System (sUAS) encounters with manned aircraft or airports increases the risk of collision in the National Airspace System. The purpose of this research is to develop and test predictive models for sUAS violation incidents in NAS using machine learning. This research uses machine learning algorithms to predict the risk of sUAS violation incidents using the FAA's UAS sighting data with a sample size of 2088. Three sUAS violation types are identified: flying above 400 feet, flying with 5 miles from an airport, and flying in restricted airspace. Seven machine learning algorithms were used, including classification regression, decision tree, neural network, gradient boosting, random forest, Bayesian networks, and Memory-Based Reasoning. The results show that Gradient boosting produces the best predictive model. This model can predict the sUAS violation incidents with an accuracy of 95.7 percent. Location, distance to the airport, state, sUAs altitude, airport type, and aircraft type are the most influential predictors to the sUAS violation incidents.     

Enhancing Data Science Ethics Through Statistical Education and Practice

As sources of data become more plentiful and massive datasets are easier to acquire, new ethical issues arise involving data quality and privacy, and the analysis, interpretation and dissemination of data‐driven decisions. There are numerous anecdotes involving abuses of complex data analyses and algorithms, and the impact they have had on society. In this paper, we discuss what statisticians can do to help enhance data science ethics in practice and what statistics educators can do to instil sound ethical behaviour in our students. We have opportunities to practice and teach ethical conduct relevant to all stages of the data life cycle. This paper discusses issues impacting ethical data science, with a focus on how statisticians can help raise awareness and encourage implementation of ethical best practices.     

用频谱分析方法实现多音并行HF MODEM

探讨了多音并行ＨＦ ＭＯＤＥＭ的原理,采用频谱分析的方法实现全数字化多音并行ＨＦ ＭＯＤＥＭ,其中解调、同步和多普勒频移补偿都是通过ＦＦＴ来完成。本方案已经在计算机上模拟实现。     

基于PC机EPP接口的并行遥测数据采集技术

在遥测系统中 ,解调后并行数据流的采集一般是通过PC机插卡方式 ,该方式实现比较复杂。为此 ,提出了一种基于EPP接口的并行遥测数据采集软硬件方案 ,该方案简化了设计 ,实现比较容易。     

Size really doesn't matter: In search of a national scale effect

I search for a “scale” effect in countries. I use a panel data set that includes 200 countries over forty years and link the population of a country to a host of economic and social phenomena. Using both graphical and statistical techniques, I search for an impact of size on the level of income, inflation, material well-being, health, education, the quality of a country's institutions, heterogeneity, and a number of different international indices and rankings. I have little success; small countries are more open to international trade than large countries, but are not systematically different otherwise. J. Japanese Int. Economies 20 (4) (2006) 482–507.     

财政支出对区域经济增长的影响效应分析--基于Panel Data模型的实证研究

我国各地区经济发展不平衡,可否通过财政支出来实现区域经济的协调发展?本文采用面板数据固定效应模型,对我国29个省市财政支出的影响进行了实证研究,并在此基础上划分东、中、西部进行了比较分析。结果表明:总体上财政支出可以促进区域经济的增长。这说明,政府可以通过财政支出来干预经济,实现区域经济的协调发展。