Binary discrimination methods for high-dimensional data with a geometric representation

Four binary discrimination methods are studied in the context of high-dimension, low sample size data with an asymptotic geometric representation, when the dimension increases while the sample sizes of the classes are fixed. We show that the methods support vector machine, mean difference, distance-weighted discrimination, and maximal data piling have the same asymptotic behavior as the dimension increases. We study the consistent, inconsistent, and strongly inconsistent cases in terms of angles between the normal vectors of the separating hyperplanes of the methods and the optimal direction for classification. A simulation study is done to assess the theoretical results.     

On the Use of GLS Demeaning in Panel Unit Root Testing

One of the most well-known facts about unit root testing in time series is that the Dickey–Fuller (DF) test based on ordinary least squares (OLS) demeaned data suffers from low power, and that the use of generalized least squares (GLS) demeaning can lead to substantial power gains. Of course, this development has not gone unnoticed in the panel unit root literature. However, while the potential of using GLS demeaning is widely recognized, oddly enough, there are still no theoretical results available to facilitate a formal analysis of such demeaning in the panel data context. The present article can be seen as a reaction to this. The purpose is to evaluate the effect of GLS demeaning when used in conjuncture with the pooled OLS t-test for a unit root, resulting in a panel analog of the time series DF–GLS test. A key finding is that the success of GLS depend critically on the order in which the dependent variable is demeaned and first-differenced. If the variable is demeaned prior to taking first-differences, power is maximized by using GLS demeaning, whereas if the differencing is done first, then OLS demeaning is preferred. Furthermore, even if the former demeaning approach is used, such that GLS is preferred, the asymptotic distribution of the resulting test is independent of the tuning parameters that characterize the local alternative under which the demeaning performed. Hence, the demeaning can just as well be performed under the unit root null hypothesis. In this sense, GLS demeaning under the local alternative is redundant.     

Pseudo Panel Data Models With Cohort Interactive Effects

When genuine panel data samples are not available, repeated cross-sectional surveys can be used to form so-called pseudo panels. In this article, we investigate the properties of linear pseudo panel data estimators with fixed number of cohorts and time observations. We extend standard linear pseudo panel data setup to models with factor residuals by adapting the quasi-differencing approach developed for genuine panels. In a Monte Carlo study, we find that the proposed procedure has good finite sample properties in situations with endogeneity, cohort interactive effects, and near nonidentification. Finally, as an illustration the proposed method is applied to data from Ecuador to study labor supply elasticity. Supplementary materials for this article are available online.     

Variable selection and weighted composite quantile estimation of regression parameters with left-truncated data

In this paper, we consider the weighted composite quantile regression for linear model with left-truncated data. The adaptive penalized procedure for variable selection is proposed. The asymptotic normality and oracle property of the resulting estimators are also established. Simulation studies are conducted to illustrate the finite sample performance of the proposed methods.     

The estimation for Lévy processes in high frequency data

In this article, a generalized Lévy model is proposed and its parameters are estimated in high-frequency data settings. An infinitesimal generator of Lévy processes is used to study the asymptotic properties of the drift and volatility estimators. They are consistent asymptotically and are independent of other parameters making them better than those in Chen et al. (2010 Chen, S. X., Delaigle, A., Hall, P. (2010). Nonparametric estimation for a class of Lévy processes. Journal of Econometrics 157:257–271.[Crossref], [Web of Science ®] , [Google Scholar]). The estimators proposed here also have fast convergence rates and are simple to implement.     

Bayesian methods for dealing with missing data problems

Missing data, a common but challenging issue in most studies, may lead to biased and inefficient inferences if handled inappropriately. As a natural and powerful way for dealing with missing data, Bayesian approach has received much attention in the literature. This paper reviews the recent developments and applications of Bayesian methods for dealing with ignorable and non-ignorable missing data. We firstly introduce missing data mechanisms and Bayesian framework for dealing with missing data, and then introduce missing data models under ignorable and non-ignorable missing data circumstances based on the literature. After that, important issues of Bayesian inference, including prior construction, posterior computation, model comparison and sensitivity analysis, are discussed. Finally, several future issues that deserve further research are summarized and concluded.     

A bivariate distribution with Lomax and geometric margins

We develop a stochastic model describing the joint distribution of $(X,N)$, where $N$ has a geometric distribution while $X$ is the sum of $N$ dependent, heavy-tail Pareto components. Models of this form arise in many applications, ranging from hydro-climatology to finance and insurance. We present fundamental properties of this vector, including marginal and conditional distributions, moments, representations, and parameter estimation. We also include an example from finance, illustrating modeling potential of this new bivariate distribution.     

基于Airbnb网络文本数据的旅游者民宿体验感知研究

民宿作为分享经济在旅游领域发展的新业态,为旅游者提供了一种全新的休闲方式。为了深入剖析影响旅游者民宿体验感知的关键因素,利用网络爬虫技术采集Airbnb用户评论数据作为研究样本,依循扎根理论方法的开放式编码、主轴式编码及选择式编码的三重过程,构建了旅游者民宿体验感知模型;借助ROST CM6.0软件从旅游者的正面评价及负面评价中提取高频特征词,分别对旅游者积极感知和消极感知的影响因素进行分析,并将城市民宿与乡村民宿进行比较。结果表明:旅游者对民宿体验的感知贯穿于旅游前期待体验—旅游中感受体验—旅游回味体验这3个阶段,由体验期望、体验前准备、民宿周围环境、民宿核心场景体验、体验后评价等5个维度构成;旅游者对城市民宿及乡村民宿的积极感知因素具有一致性,主要表现在地理位置、自然环境、文化环境、民宿特色和主客互动等方面;而消极感知因素具有差异性,配套设施和卫生条件是旅游者对城市民宿体验消极感知的主要影响因素,旅游者对乡村民宿的消极感知受到地理位置、基础设施及配套设施等多种因素的影响。     

智能网联汽车体系结构与关键技术

智能网联汽车（Intelligent connected vehicles,ICV）是无人驾驶技术的核心载体,产业链长,市场巨大,吸引了国内外众多汽车厂商和信息企业都纷纷投向ICV的研发与测试,部分ICV车型已进入量产阶段,新的概念车型也陆续提出。针对新型ICV的发展现状与关键技术,从ICV智能化和网联化两个维度对ICV中涉及到的相关技术进行分析,进一步从信息处理的感知层、决策层、控制层3个层次构建了ICV发展过程中的关键技术体系结构。研究表明,车辆环境感知和通信技术技术涵盖自车状态和外部环境,短程通信技术（DSRC）、机器视觉和激光雷达技术是实现车辆环境信息采集功能完善的关键;车路协同系统涉及车—车通信、车—路通信、车—云通信,协同智能交通系统、云平台和大数据技术是车路协同技术的关键;驾驶辅助技术包括自适应巡航控制系统、车道偏离/避免系统、碰撞预警/避免系统等8个构成,这是辅助驾驶、部分自动驾驶和有条件的自动驾驶3个智能化阶段中的关键;具有自主学习、自我决策的人工智能技术和信息安全技术也是智能网联汽车的关键技术,在此基础上,还应该实现多种技术间的相互支撑与融合,克服关键技术协同障碍,才能实现ICV的市场推广与普及应用。