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.     

New two-stage sampling designs based on neoteric ranked set sampling

Neoteric ranked set sampling (NRSS) is a recently developed sampling plan, derived from the well-known ranked set sampling (RSS) scheme. It has already been proved that NRSS provides more efficient estimators for population mean and variance compared to RSS and other sampling designs based on ranked sets. In this work, we propose and evaluate the performance of some two-stage sampling designs based on NRSS. Five different sampling schemes are proposed. Through an extensive Monte Carlo simulation study, we verified that all proposed sampling designs outperform RSS, NRSS, and the original double RSS design, producing estimators for the population mean with a lower mean square error. Furthermore, as with NRSS, two-stage NRSS estimators present some bias for asymmetric distributions. We complement the study with a discussion on the relative performance of the proposed estimators. Moreover, an additional simulation based on data of the diameter and height of pine trees is presented.     

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.     

Effects of a multimodal exercise program in pedal dexterity and balance: study with Portuguese older adults of different contexts

This study investigated the effects of a multimodal exercise program (MEP) on pedal dexterity and balance in two groups of older adult participants (65–92 years of age) from a psychiatric hospital center (HC), a residential care home (RCH), and a daily living center (DLC). The experimental group (EG) trained three times per week for 12 months, and the control group (CG) maintained their normal activities. The Mini-Mental State Examination and the Modified Baecke Questionnaire, as well as the Pedal Dexterity and the Tinetti tests, were applied to all subjects before and after the experimental protocol. Furthermore, the foot preference was controlled using the Lateral Preference Questionnaire proposed by Coren [10]. In the EG, the results from the Pedal Dexterity test showed that both males and females from the RCH and DLC improved their performances after the MEP. In the HC, the males slightly decreased their performance with both feet, contrarily to females. Both males and females from the CG decreased their pedal dexterity performance, namely, with the non-preferred foot. Concerning the Tinetti test, the EG of both males and females from the HC, the RCH (males were better than females regarding the gender factor), and the DLC improved their balance after the MEP. In the CG, no significant effects or interactions were found for any of the context groups.     

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.