Correlated random effects regression analysis for a log-normally distributed variable

In regression analysis, it is assumed that the response (or dependent variable) distribution is Normal, and errors are homoscedastic and uncorrelated. However, in practice, these assumptions are rarely satisfied by a real data set. To stabilize the heteroscedastic response variance, generally, log-transformation is suggested. Consequently, the response variable distribution approaches nearer to the Normal distribution. As a result, the model fit of the data is improved. Practically, a proper (seems to be suitable) transformation may not always stabilize the variance, and the response distribution may not reduce to Normal distribution. The present article assumes that the response distribution is log-normal with compound autocorrelated errors. Under these situations, estimation and testing of hypotheses regarding regression parameters have been derived. From a set of reduced data, we have derived the best linear unbiased estimators of all the regression coefficients, except the intercept which is often unimportant in practice. Unknown correlation parameters have been estimated. In this connection, we have derived a test rule for testing any set of linear hypotheses of the unknown regression coefficients. In addition, we have developed the confidence ellipsoids of a set of estimable functions of regression coefficients. For the fitted regression equation, an index of fit has been proposed. A simulated study illustrates the results derived in this report.     

Emotional Climate in Families Experiencing Homelessness: Associations with Child Affect and Socioemotional Adjustment in School

This study examined associations among family‐level risks, emotional climate, and child adjustment in families experiencing homelessness. Emotional climate, an indirect aspect of emotion socialization, was indexed by parents’ expressed emotion while describing their children. Sociodemographic risk and parent internalizing distress were hypothesized to predict more negativity and less warmth in the emotional climate. Emotional climate was expected to predict observer‐rated child affect and teacher‐reported socioemotional adjustment, mediating effects of risk. Participants were 138 homeless parents (64 percent African‐American) and their four‐ to six‐year‐old children (43.5 percent male). During semi‐structured interviews, parents reported demographic risks and internalizing distress and completed a Five Minute Speech Sample about their child, later rated for warmth and negativity. Children's positive and negative affect were coded from videotapes of structured parent‐child interaction tasks. Socioemotional adjustment (externalizing behavior, peer acceptance, and prosocial behavior) was reported by teachers a few months later. Hypotheses were partially supported. Parent internalizing distress was associated with higher parent negativity, which was linked to more negative affect in children, and parent warmth was associated with children's positive affect. Neither emotional climate nor child affect predicted teacher‐reported externalizing behavior or peer acceptance, but parental negativity and male sex predicted lower prosocial behavior in the classroom. Future research directions and clinical implications are discussed.     

Bayesian Classifiers Applied to the Tennessee Eastman Process

Fault diagnosis includes the main task of classification. Bayesian networks (BNs) present several advantages in the classification task, and previous works have suggested their use as classifiers. Because a classifier is often only one part of a larger decision process, this article proposes, for industrial process diagnosis, the use of a Bayesian method called dynamic Markov blanket classifier that has as its main goal the induction of accurate Bayesian classifiers having dependable probability estimates and revealing actual relationships among the most relevant variables. In addition, a new method, named variable ordering multiple offspring sampling capable of inducing a BN to be used as a classifier, is presented. The performance of these methods is assessed on the data of a benchmark problem known as the Tennessee Eastman process. The obtained results are compared with naive Bayes and tree augmented network classifiers, and confirm that both proposed algorithms can provide good classification accuracies as well as knowledge about relevant variables.     

Servant Leadership in the Public Sector: Employee Perspective

Public Organization Review - The purpose of this study is to explore how servant leadership affects public sector employee engagement, organisational ethical climate, and public sector reform, of...     

A global count of the extreme poor in 2012: data issues,methodology and initial results

The 2014 release of a new set of purchasing power parity (PPP) conversion factors (PPPs) for 2011 has prompted a revision of the World Bank’s international poverty line. In revising the line, we have sought to minimize changes to the real purchasing power of the earlier $1.25 line (in 2005 PPPs), so as to preserve the integrity of the goalposts for international targets such as the Sustainable Development Goals (SDGs) and the World Bank’s twin goals – which were set with respect to that line. In particular, the new line was obtained by inflating the same fifteen national poverty lines – originally used by Ravallion et al. (World Bank Econ. Rev. 23(2): 163–184 2009) to construct the $1.25 line – to 2011 prices in local currency units, and then converting them to US dollars using 2011 PPP conversion factors. With a small approximation, this procedure yields a new international poverty line of $1.90 per person per day. In combination with other changes described in the paper, this revision leads to relatively small changes in global poverty incidence for 2011: from 14.5 % using the old method to 14.1 % using the new method. In 2012, the new reference year for the global count, we find 12.7 % of the world’s population, or 897 million people, are living in extreme poverty. There are changes in the regional composition of poverty, but they are also relatively small. This paper documents methodological decisions taken in the process of updating both the poverty line and the consumption and income distributions at the country level, including issues of inter-temporal and spatial price adjustments. It also describes various caveats and limitations of the approach taken.     

Disability and employee benefits receipt: evidence from the U.S. Vocational Rehabilitation Services Program

Studies indicate positive effects of the U.S. Vocational Rehabilitation Services (VRS) in assisting people with disabilities to find independent employment. Underemployment continues to impact access to adequate health care and other benefits. Workers with disabilities receive fewer benefits, overall. With data from the Longitudinal Study of Vocational Rehabilitation Services Program (LSVRSP), the authors compare the rates of receipt of 6 types of benefits for people with physical, mental, and sensory impairments. Although those with physical disabilities are most likely to receive benefits, all groups lack adequate access to health care, sick leave, and vacation. The authors discuss implications for services provision in the current job market.     

Challenging the Myths of Black Women—A Short-Term,Structured, Art Experience Group: Exploring the Intersections of Race,Gender, and Intergenerational Trauma

ABSTRACT

The authors describe a short-term structured group process for African American women in the suburbs of Washington, DC, highlighting the perspective of the group leaders. The group was started to address the unique needs of African American women who are also survivors of childhood abuse and neglect. The group leaders used discussion and art in the group to explore the intersections of race, gender, and intergenerational trauma. Having shared experiences with the participants of the group, the authors explore the ways the group leaders were changed and challenged in the process.     

The cut-off rate for investment decisions in growth companies

In this article the author attempts to define cut-off rate for capital budgeting decisions of a “growth company” and to relate it to the firms long range planning. Dr Chandra argues that conventional measurement of cost of capital cannot be used in the growth company. In brief, he defines the cut-off rate in this type of company as— dividend yield plus the rate at which dividend is expected to grow.