Nenparametric Two-Group Classification: Concepts and a SAS-Based Software Package

This article introduces BestClass, a set of SAS macros, available in the mainframe and workstation environment, designed for solving two-group classification problems using a class of recently developed nonparametric classification methods. The criteria used to estimate the classification function are based on either minimizing a function of the absolute deviations from the surface which separates the groups, or directly minimizing a function of the number of misclassified entities in the training sample. The solution techniques used by BestClass to estimate the classification rule use the mathematical programming routines of the SAS/OR software. Recently, a number of research studies have reported that under certain data conditions this class of classification methods can provide more accurate classification results than existing methods, such as Fisher's linear discriminant function and logistic regression. However, these robust classification methods have not yet been implemented in the major statistical packages, and hence are beyond the reach of those statistical analysts who are unfamiliar with mathematical programming techniques. We use a limited simulation experiment and an example to compare and contrast properties of the methods included in Best-Class with existing parametric and nonparametric methods. We believe that BestClass contributes significantly to the field of nonparametric classification analysis, in that it provides the statistical community with convenient access to this recently developed class of methods. BestClass is available from the authors.     

On sufficient conditions for the strong consistency of least-squares estimates

The strong consistency of the least-squares estimates in regression models is obtained when the errors are i.i.d. with absolute moment of order r, 0<r? 2. The assumptions presented for the random error sequence will permit us to obtain improvements of the conditions on the regressors in order to obtain the strong consistency of the least-squares estimates in linear and nonlinear regression models.     

Strong consistency of least squares estimates with i.i.d. errors with mean values not necessarily defined

We establish strong consistency of the least squares estimates in multiple regression models discarding the usual assumption of the errors having null mean value. Thus, we required them to be i.i.d. with absolute moment of order r, 0<r<2, and null mean value when r>1. Only moderately restrictive conditions are imposed on the model matrix. In our treatment, we use an extension of the Marcinkiewicz–Zygmund strong law to overcome the errors mean value not being defined. In this way, we get a unified treatment for the case of i.i.d. errors extending the results of some previous papers.     

Geographically Weighted Negative Binomial Regression—incorporating overdispersion

Global regression assumes that a single model adequately describes all parts of a study region. However, the heterogeneity in the data may be sufficiently strong that relationships between variables can not be spatially constant. In addition, the factors involved are often sufficiently complex that it is difficult to identify them in the form of explanatory variables. As a result Geographically Weighted Regression (GWR) was introduced as a tool for the modeling of non-stationary spatial data. Using kernel functions, the GWR methodology allows the model parameters to vary spatially and produces non-parametric surfaces of their estimates. To model count data with overdispersion, it is more appropriate to use a negative binomial distribution instead of a Poisson distribution. Therefore, we propose the Geographically Weighted Negative Binomial Regression (GWNBR) method for the modeling of data with overdispersion. The results obtained using simulated and real data show the superiority of this method for the modeling of non-stationary count data with overdispersion compared with competing models, such as global regressions, e.g., Poisson and negative binomial and Geographically Weighted Poisson Regression (GWPR). Moreover, we illustrate that these competing models are special cases of the more robust model GWNBR.     

Validation of the Flourishing Scale and Scale of Positive and Negative Experience in Portugal

The Flourishing Scale (FS) and the Scale of Positive and Negative Experience (SPANE) created by Diener et al. (Soc Indic Res 97:143–156, 2010) are instruments that assess psychological flourishing and feelings (positive and negative, and the difference between the two). In this study, the psychometric properties of both scales were explored by using two Portuguese samples (I: n = 734; II: n = 194). Reliability analysis and a multi-group confirmatory factorial analysis (MCFA) of both scales were performed. To examine the validity of FS and SPANE we analyzed their correlations with other well-being and happiness measures. Results showed that the Portuguese versions of both scales have good psychometric properties, and they also showed convergent validity. Results also demonstrated the unidimensional structure of the FS and a two-factor solution for the SPANE. The multi-group CFA of both scales evidenced an invariant structure. Both Portuguese versions of the scales behave consistently with the original and may be used in future studies of well-being.     

Problems with Traditional Science Publishing and Finding a Wider Niche for Post-Publication Peer Review

Science affects multiple basic sectors of society. Therefore, the findings made in science impact what takes place at a commercial level. More specifically, errors in the literature, incorrect findings, fraudulent data, poorly written scientific reports, or studies that cannot be reproduced not only serve as a burden on tax-payers’ money, but they also serve to diminish public trust in science and its findings. Therefore, there is every need to fortify the validity of data that exists in the science literature, not only to build trust among peers, and to sustain that trust, but to reestablish trust in the public and private academic sectors that are witnessing a veritable battle-ground in the world of science publishing, in some ways spurred by the rapid evolution of the open access (OA) movement. Even though many science journals, traditional and OA, claim to be peer reviewed, the truth is that different levels of peer review occur, and in some cases no, insufficient, or pseudo-peer review takes place. This ultimately leads to the erosion of quality and importance of science, allowing essentially anything to become published, provided that an outlet can be found. In some cases, predatory OA journals serve this purpose, allowing papers to be published, often without any peer review or quality control. In the light of an explosion of such cases in predatory OA publishing, and in severe inefficiencies and possible bias in the peer review of even respectable science journals, as evidenced by the increasing attention given to retractions, there is an urgent need to reform the way in which authors, editors, and publishers conduct the first line of quality control, the peer review. One way to address the problem is through post-publication peer review (PPPR), an efficient complement to traditional peer-review that allows for the continuous improvement and strengthening of the quality of science publishing. PPPR may also serve as a way to renew trust in scientific findings by correcting the literature. This article explores what is broadly being said about PPPR in the literature, so as to establish awareness and a possible first-tier prototype for the sciences for which such a system is undeveloped or weak.     

Reviews

Book reviewed in this article: Bidge's Mob: Written and illustrated by Gaye Dell Creative Family Therapy Techniques: Play, Art, and Expressive Activities to Engage Children in Family Sessions: Editor: Liana Lowenstein Essential Skills in Family Therapy, from the first interview to termination: JoEllen Patterson, Lee Williams, Todd M. Edwards, Larry Chamow, Claudia Grauf‐Grounds Multi‐Family Therapy: Concepts and Techniques: Eia Asen, Michael Scholz     

The Educated Russian's Curse: Returns to Education in the Russian Federation during the 1990s

Abstract. This paper uses a representative sample of the Russian Federation, the Russian Longitudinal Monitoring Survey, to estimate the returns to education in this ex‐communist country. We tackle this classic issue in labor economics with the realistic expectation of obtaining results for Russia comparable in quality and reliability to those available in developed countries and other economies in transition. Using standard regression techniques we find that the returns to education in Russia are quite low compared with those reported in the literature on countries throughout the world, in almost no specification reaching higher than 5 per cent. Moreover, there is virtually no improvement in returns to education in the 1992–99 period, a result somewhat at odds with other studies using Russian data from similar time periods. When we instrument our main regressor using policy experiments from the 1960s, we find comparable results. We also perform a selectivity correction and discover even lower returns to education for men, although they become slightly higher for women. Additionally, we find extremely low returns to tenure, which can even become negative in certain specifications.     

我国丝绸出口贸易的经济分析

我国丝绸出口贸易的 "显示"比较优势指数呈逐年下降的趋势,但其数值一直保持在17以上,说明我国的丝绸出口与其他出口商品相比仍具有很大的竞争优势;我国丝绸出口的相对价格弹性系数为-0.3825,属于低弹性的商品.降低丝绸出口价格,不但无益于丝绸出口量的增加,反而会引起出口创汇额的明显下降,引起丝绸"出口贫困"现象的产生.稳定我国丝绸出口价格,是我国蚕丝业和丝绸贸易稳定发展的正确选择.     

论我国货币政策传导机制中的障碍

通过对近年来我国出现的通货紧缩现象的分析 ,可以看到中央银行所采取的稳健的货币政策的内容及其执行状况。经济的不景气不是由于货币供应量不足而是在于货币政策的传导机制的不畅。我们应找出传导机制不畅的原因 ,并提出相应的对策