Bimodality based on the generalized skew-normal distribution

This paper focuses on the development of a new extension of the generalized skew-normal distribution introduced in Gómez et al. [Generalized skew-normal models: properties and inference. Statistics. 2006;40(6):495–505]. To produce the generalization a new parameter is introduced, the signal of which has the flexibility of yielding unimodal as well as bimodal distributions. We study its properties, derive a stochastic representation and state some expressions that facilitate moments derivation. Maximum likelihood is implemented via the EM algorithm which is based on the stochastic representation derived. We show that the Fisher information matrix is singular and discuss ways of getting round this problem. An illustration using real data reveals that the model can capture well special data features such as bimodality and asymmetry.     

Detection of outliers in simple circular regression models using the mean circular error statistic

The investigation on the identification of outliers in linear regression models can be extended to those for circular regression case. In this paper, we propose a new numerical statistic called mean circular error to identify possible outliers in circular regression models by using a row deletion approach. Through intensive simulation studies, the cut-off points of the statistic are obtained and its power of performance investigated. It is found that the performance improves as the concentration parameter of circular residuals becomes larger or the sample size becomes smaller. As an illustration, the statistic is applied to a wind direction data set.     

Maximum likelihood estimation in a Weibull regression model with type-1 censoring: a Monte Carlo study

Results of the Monte Carlo study of the performance of a maximum likelihood estimation in a Weibull parametric regression model with two explanatory variables are presented. One simulation run contained 1000 samples censored on the average by the amount of 0-30%. Each simulatedsample was generated in a form of two-factor two-level balanced experiment. The confidence intervals were computed using the large-sample normal approximation via the matrix of observed information. For small sample sizes the estimates of the scale parameter b of the loglifetime were significantly negatively biased, which resulted in a poor quality of confidence intervals for b and the low-level quantiles. All estimators improved their quality when the nominal value of b decreased. A moderate amount of censoring improved the quality of point and confidence estimation. The reparametrization b 7 produced rather accurate confidence intervals. Exact confidence intervals for b in case of non-censoring were obtained using the pivotal quantity b/b.     

The "quality " of early care and education settings: definitional and measurement issues

There is a widespread belief that high-quality early care and education can improve children's school readiness. However, debate continues about the essential elements of a high-quality experience, about whether quality means the same things across different types of care settings, about how to measure quality, and about the level of quality that might make a meaningful difference in outcomes for children. Are the aspects of the child care environment that researchers measure the ones that are most strongly related to children's development? This article argues that the ways in which researchers currently measure early care environments are flawed and that the conclusions drawn about the relationship between these measures and outcomes for children are frequently incorrect or overstated. The article addresses four questions: How is the quality of the child care environment commonly defined and measured? Do the most commonly used measures capture the child's experience? Do they work well across all settings? Are researchers drawing the correct conclusions from studies that relate the child care environment to child outcomes? Finally, the article discusses some possible directions for future research.     

SOME ECONOMIC CONSEQUENCES OF ANTICIPATING TECHNICAL ADVANCE

Using a simple model of a long run profit maximizing firm, we investigated the consequences of foreseeing future technical advance upon the adoption of new technology, scrapping of old, and for price and output of the firm's product. To simplify the analysis and highlight the conclusions, we assumed all technologies embodied in equipment and all equipment infinitely durable. It was shown that the often-used formulas for the unit cost of using capital over a finite (rkj/(l — a^T)) or infinite (rkj) life are appropriate only if the equipment does not become outmoded during its economic life and if there are no demand shifts in that time interval. Otherwise, the current cost of using capital (ex ante) must reflect future lesser or greater earning power of that capital due to outmodedness or demand shifts. Anticipation of technical advance tends to delay scrapping of old equipment and retard installation of new, with current output smaller and price higher than if technology is stagnant. Selection among currently competing technologies is also affected by the course future technical advance is expected to follow. The economic lifetime of capital equipment is independent of the elasticity of demand for the firm's output. On the other hand anticipation of demand expansion tends to partially or wholly offset the effect of anticipating future technical advance, while expected demand decline tends to reinforce it. Uncertainty about when improved technology will appear tends to retard adoption of current best practice technology, to retard scrapping of outmoded technology, restrict output and elevate price, in comparison with pptimal policy when the time of availability is believed known. The optimal policy is unaffected when it is the magnitude of the improvement rather than its timing which is unknown.     

Psychology in the Arab World: Past, Present, and Future

In this article, we present a broad-based view of psychology in the Arab countries. We begin by casting light on the relevant contributions by ancient Arab scholars in the Middle Ages. Given that systematic scientific investigation of this legacy has been minuscule, we argue that in the future, scholars seeking to provide a more balanced and comprehensive history of Arab psychology should reanalyze these works. We point out that in the early decades of the modern twentieth century, Western psychology was introduced in the Arab countries, especially in Egypt—the gateway through which such psychology was introduced and practiced. By the early 1960s, a number of universities had been established in some Arab countries, and Egyptian academicians, including psychologists, were invited to perform teaching, research, and administration tasks. Their legacy was a discipline bearing most of the strengths and weaknesses of Egyptian psychology, although in spite of such commonalties, there exist also some interesting differences characterizing psychology in the various Arab countries. The final section of the article summarizes some key aspects endemic to psychology in the Arab countries.     

A theory and verities of market place behaviour

This study questions traditional notions of competitive economic behaviour from a theoretical perspective of social psychology and systems theory. Inter alia these approaches suggest, firstly, that economic relationships are simply an aspect of broader social relationships and are likely to be characterised by cooperation rather than competitiveness and the likelihood of conflict. Secondly, it is suggested that the conditions required for market participants to he “fully informed” would not he evident because of the potential instability of such a communications network or system.These broad hypotheses are examined by measuring aspects of communications and business behaviour in a wholesale agricultural market. The data. which are analysed by network and correlational procedures, support the hypotheses.     

Personalized Neural Networks Underlie Individual Differences in Ethnic Identity Exploration and Resolution

This study examined how ethnic identity relates to large-scale brain networks implicated in social interactions, social cognition, self-definition, and cognitive control. Group Iterative Multiple Model Estimation (GIMME) was used to create sparse, person-specific networks among the default mode and frontoparietal resting-state networks in a diverse sample of 104 youths aged 17–21. Links between neural density (i.e., number of connections within and between these networks) and ethnic identity exploration and resolution were evaluated in the full sample. Ethnic identity resolution was positively related to frontoparietal network density, suggesting that having clarity about one’s ethnic group membership is associated with brain network organization reflecting cognitive control. These findings help fill a critical knowledge gap about the neural underpinnings of ethnic identity.