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.     

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.     

Influence diagnostics for the Weibull-Negative-Binomial regression model with cure rate under latent failure causes

In this paper, we propose a flexible cure rate survival model by assuming that the number of competing causes of the event of interest follows the Negative Binomial distribution and the time to event follows a Weibull distribution. Indeed, we introduce the Weibull-Negative-Binomial (WNB) distribution, which can be used in order to model survival data when the hazard rate function is increasing, decreasing and some non-monotonous shaped. Another advantage of the proposed model is that it has some distributions commonly used in lifetime analysis as particular cases. Moreover, the proposed model includes as special cases some of the well-know cure rate models discussed in the literature. We consider a frequentist analysis for parameter estimation of a WNB model with cure rate. Then, we derive the appropriate matrices for assessing local influence on the parameter estimates under different perturbation schemes and present some ways to perform global influence analysis. Finally, the methodology is illustrated on a medical data.     

Dynamic Updating of Labour Force Estimates: JARES

Abstract. In this paper a new algorithm is proposed in order to produce an automatic dynamic compound estimator of the labour force based on an interactive scheme. The proposed algorithm, JARES, is based on the probability estimator of Jaynes based on the notion of maximum entropy of a given probability distribution with a constraint on the average of an external information. The iterative scheme is based on the solution of a set of linear equations which represent the algebraic relationships between the weights and the estimates.     

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.