Time-sequential statistical procedures: a preface

This preface outlines the Scope of the time-sequential procedures considered in this special issue and presents some of the basic terminologies used in the subsequent papers.     

Weighted-Average Least Squares Prediction

Prediction under model uncertainty is an important and difficult issue. Traditional prediction methods (such as pretesting) are based on model selection followed by prediction in the selected model, but the reported prediction and the reported prediction variance ignore the uncertainty from the selection procedure. This article proposes a weighted-average least squares (WALS) prediction procedure that is not conditional on the selected model. Taking both model and error uncertainty into account, we also propose an appropriate estimate of the variance of the WALS predictor. Correlations among the random errors are explicitly allowed. Compared to other prediction averaging methods, the WALS predictor has important advantages both theoretically and computationally. Simulation studies show that the WALS predictor generally produces lower mean squared prediction errors than its competitors, and that the proposed estimator for the prediction variance performs particularly well when model uncertainty increases.     

A Festschrift for Adrian Baddeley

This article introduces a special issue of the Australian and New Zealand Journal of Statistics, being a Festschrift for Adrian Baddeley on the occasion of his 65th birthday.     

Least absolute values estimation: an introduction

A brief review and bibliography of least absolute values (LAV) estimation is given. This paper serves to introduce the other articles in this special issue on the computational aspects of LAV estimation.     

Statistical consulting

The paper and the special issue focus on the activity of statistical consulting and its varieties. This includes academic consulting, consulting to and in industry as well as statistics in public media.     

Cointegrated systems II

The three invited papers in this special issue of Econometric Reviews on "Cointegrated Systems II" complement the previous special issue of the journal. The paper by Eric Zivot and Peter Phillips provides a comprehensive Bayesian analysis of trend determination in economic time series. Two interesting comments on some aspects of current research involving cointegration and the modelling of dynamic economic relationships are provided by Clive Granger and Denzil Fiebig.     

Developments and new dimensions in econometrics

Summary This special issue of the Journal of the German Statistical Society presents 14 papers with surveys on the development and new topics in econometrics. The articles aim to demonstrate how German econometricians see the discipline from their specific view. They briefly describe the main strands and emphasize some recent methods.     

A systematic review on model selection in high-dimensional regression

High dimensional models are getting much attention from diverse research fields involving very many parameters with a moderate size of data. Model selection is an important issue in such a high dimensional data analysis. Recent literature on theoretical understanding of high dimensional models covers a wide range of penalized methods including LASSO and SCAD. This paper presents a systematic overview of the recent development in high dimensional statistical models. We provide a brief review on the recent development of theory, methods, and guideline on applications of several penalized methods. The review includes appropriate settings to be implemented and limitations along with potential solution for each of the reviewed method. In particular, we provide a systematic review of statistical theory of the high dimensional methods by considering a unified high-dimensional modeling framework together with high level conditions. This framework includes (generalized) linear regression and quantile regression as its special cases. We hope our review helps researchers in this field to have a better understanding of the area and provides useful information to future study.     

Current and past roles of the statistician in space applications

The paper contains a brief and informal history of space science topics which led to statistical consulting. The past and current roles of the statistician in space science is discussed and this special issue is prefaced by describing generally the contents of the issue. Several statistical problems associated with the space scientists’ efforts to develop instrumentation and space vehicles for both manned and unmanned space missions are discussed as well as those problems associated with developing a space laboratory. The current tasks of developing remote sensors to make earth observations for monitoring earth resources and the associated statistical problems are also discussed. These latter activities are those that have motivated this special issue.     

Cointegrated systems II

The three invited papers in this special issue of Econometric Reviews on "Cointegrated Systems II" complement the previous special issue of the journal. The paper by Eric Zivot and Peter Phillips provides a comprehensive Bayesian analysis of trend determination in economic time series. Two interesting comments on some aspects of current research involving cointegration and the modelling of dynamic economic relationships are provided by Clive Granger and Denzil Fiebig.     

一种新的空间权重矩阵选择方法

空间权重矩阵选择问题一直是空间计量经济学中的一个难题,权重矩阵的选择正确与否关系到模型的最终估计结果。本文在空间滞后模型框架下,把空间权重矩阵选择问题转化为变量选择问题,然后利用CWB方法进行变量选择。中国城市服务业集聚机理实证研究显示,利用本文所提出的方法所选取的空间权重矩阵较为合理,进而可以减少因为空间权重矩阵误设问题而引起的模型估计偏误。在大样本情形下,该方法可以非常有效地降低计算成本。     

Preface

The appearance of this issue will mark almost two years since its inception by the late Editor, Professor Don B. Owen. Professor Malay Ghosh suggested a special issue on Pitman's Measure of Closeness (PMC) to Professor Owen in autumn of 1989. After a thorough review process the issue was finalized in June 1991. It is with remorse that we publish this special issue in memory of Professor Owen.

The completion of the issue coincided with a special conference, “Pitman's Measure of Cfoseness: Celebrating a Decade of Renaissance,” held on June 15, 1991 at the University of Texas at San Antonio (UTSA). The papers in this special issue, except those of Professor Kubokawa and Drs. Bertuzzi and Gandolfi, were presented and discussed. The conference participants included C.R. Rao (Pennsylvania State University), Colin Blyth (Queen's University), and H.T. David (Iowa State University). Further, P.K. Sen (University of North Carolina) and Malay Ghosh (University of Florida) gave keynote addresses that respectively set the themes for the morning and afternoon sessions.

The conference banquet held in the Regent's Room at the University of Texas at San Antonio, featured stimulating addresses by C.R. Rao and Colin Blyth on some of the major controversies of PMC such as intransitiveness and Berkson's conjecture. We are grateful to the University of Texas at San Antonio for making this conference a reality. In particular, we thank Professor Shair Ahmad, Director of the Division of Mathematics, Computer Science, and Statistics at UTSA for funding travel expenses not only for the invited speakers but also for many of the younger researchers. We also acknowledge University President Samuel Kirkpatrick who made the KIVA room available for the technical sessions and the Regent's Room for the Banquet. We also acknowledge the financial support of Bell Helicopter Textron, Inc. as a cosponsor of the conference.     

低碳经济背景下碳排放的库兹涅茨曲线研究

 经济增长与环境质量之间的关系是环境经济学研究的热点问题。环境库兹涅茨曲线则是研究该问题较为普遍的方法。传统的环境库兹涅茨曲线的研究模式是事先假定曲线存在,然后利用固定模式的参数模型进行拟合估计,但这种做法的不足之处在于我们并不能够确定各国各地区的经济增长与环境质量存在这种关系。本文在低碳经济背景下,采用非参数模型,研究二氧化碳作为环境代理指标的碳排放的环境库兹涅茨曲线。研究发现：我国的经济增长与环境质量之间不存在倒“U”型曲线关系,FDI与城市化对我国的环境质量的影响统计意义显著,但经济意义不显著,而贸易、能源强度则对我国的环境具有恶化作用。     

Performance of information criteria for spatial models

Model choice is one of the most crucial aspect in any statistical data analysis. It is well known that most models are just an approximation to the true data-generating process but among such model approximations, it is our goal to select the ‘best’ one. Researchers typically consider a finite number of plausible models in statistical applications, and the related statistical inference depends on the chosen model. Hence, model comparison is required to identify the ‘best’ model among several such candidate models. This article considers the problem of model selection for spatial data. The issue of model selection for spatial models has been addressed in the literature by the use of traditional information criteria-based methods, even though such criteria have been developed based on the assumption of independent observations. We evaluate the performance of some of the popular model selection critera via Monte Carlo simulation experiments using small to moderate samples. In particular, we compare the performance of some of the most popular information criteria such as Akaike information criterion (AIC), Bayesian information criterion, and corrected AIC in selecting the true model. The ability of these criteria to select the correct model is evaluated under several scenarios. This comparison is made using various spatial covariance models ranging from stationary isotropic to nonstationary models.     

Design and analysis of computer experiments

The design and analysis of computer experiments as a relatively young research field is not only of high importance for many industrial areas but also presents new challenges and open questions for statisticians. This editorial introduces a special issue devoted to the topic. The included papers present an interesting mixture of recent developments in the field as they cover fundamental research on the design of experiments, models and analysis methods as well as more applied research connected to real-life applications.     

中国区域经济协调发展实证研究

 由于我国经济发展的区域不均衡性,区域经济的协调发展问题不仅是我国一项长期重要国策,也使其成为社会经济研究的热点问题。本文将空间计量模型与面板分析方法相结合,以我国省域经济发展作为研究对象和数据来源,从而对我国省域经济增长的协调发展和所受影响因素进行研究。结果显示,我国区域经济发展存在着显著的空间相关性,在经济发展的不同阶段影响经济增长的因素不同,对经济发展有持续显著正影响的因素有：人力资本、人口资本、市场化进程和财政支出等。据此本文提出若干政策建议,以促进我国经济协调增长。     

Special issue on statistical analysis of networks

Statistical Methods & Applications - The special issue on Statistical Analysis of Networks aspires to convey the breadth and depth of statistical learning with networks, ranging from networks...     

Joint detection for functional polynomial regression with autoregressive errors

In this article, we are concerned with detecting the true structure of a functional polynomial regression with autoregressive (AR) errors. The first issue is to detect which orders of the polynomial are significant in functional polynomial regression. The second issue is to detect which orders of the AR process in the AR errors are significant. We propose a shrinkage method to deal with the two problems: polynomial order selection and autoregressive order selection. Simulation studies demonstrate that the new method can identify the true structure. One empirical example is also presented to illustrate the usefulness of our method.     

巨灾、巨灾保险与中国模式

灾害是伴随人类社会发展而不断发展的异常现象,巨灾更以其巨大的破坏力及惨烈后果而给人类社会带来严重的威胁。巨灾不可避免,巨灾损失也不可避免,因此,必须正视巨灾发生及其发展规律,对巨灾损失除建立正常的补偿机制,还有必要建立专门的巨灾保险制度。中国的巨灾保险制度应当根据巨灾种类及相关国情要素体现出自己的特点。     

缺失数据下的逆概率多重加权分位回归估计及其应用

数据缺失问题普遍存在于应用研究中。在随机缺失机制假定下,本文从模型推断角度出发,针对线性缺失分位回归模型,提出一种新的有效估计方法——逆概率多重加权（IPMW）估计。该方法是在逆概率加权（IPW）估计的基础上,结合倾向得分匹配及模型平均思想,经过多次估计,加权确定最终参数估计结果。该方法适用于响应变量是独立同分布或独立非同分布的情形,并适用于绝大多数缺失场景。经过理论推导及模拟研究发现,IPMW估计量在继承IPW估计量的优势上具有更稳健的性质。最后,将该方法应用于含有缺失数据的微观调查数据中,研究了经济较发达的准一线城市中等收入群体消费水平的影响因素,对比两种估计方法的估计结果及置信带,发现逆概率多重加权估计量的标准偏差更小,估计结果更稳健。