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.     

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.     

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.     

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.     

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.     

Small Area Estimation Using Estimated Population Level Auxiliary Data

Unit level linear mixed models are often used in small area estimation (SAE), and the empirical best linear unbiased prediction (EBLUP) is widely used for the estimation of small area means under such models. However, EBLUP requires population level auxiliary data, atleast area specific aggregated values. Sometimes population level auxiliary data is either not available or not consistent with the survey data. We describe a SAE method that uses estimated population auxiliary information. Empirical results show that proposed method for SAE produces an efficient set of small area estimates.     

Adolescent Religiosity and School Contexts*

Objectives. Schools serve as the primary social organizations for adolescents, structuring their lives and conveying a variety of skills, norms, and values, but relatively little is known about how schools influence the development of religious belief, attitudes, and behavior during adolescence. We explore how schools' religious norms, coupled with adolescents' pursuit of social status through conformity, affect public and private religiosity. Methods. Employing data from the National Longitudinal Study of Adolescent Health (Add Health), we use multilevel modeling to examine the impact of school context on adolescents' public and private religiosity. Results. We find that school norms are influential in shaping both public religious expressions and private devotional activities, but pursuit of social status is a motivation for change across religious contexts only of public religious activity. The effect of social status as a motivator of religious change was moderated by the strength of the adolescent's identification with the school, especially for private religiosity. Conclusions. Schools play a key role in the social development of adolescents, and students' religious beliefs and behaviors are influenced systematically and observably by the type of religious climate within the school.     

Of Sex and Romance: Late Adolescent Relationships and Young Adult Union Formation

To better understand the social factors that influence the diverse pathways to family formation young adults experience today, this research investigates the association between opposite‐gender relationships during late adolescence and union formation in early adulthood. Using data from the first and third waves of the Add Health (n= 4,911), we show that, for both men and women, there is continuity between adolescent and adult relationship experiences. Those involved in adolescent romantic relationships at the end of high school are more likely to marry and to cohabit in early adulthood. Moreover, involvement in a nonromantic sexual relationship is positively associated with cohabitation, but not marriage. We conclude that the precursors to union formation patterns in adulthood are observable in adolescence.