Bayesian analysis of mixture modelling using the multivariate t distribution

A finite mixture model using the multivariate t distribution has been shown as a robust extension of normal mixtures. In this paper, we present a Bayesian approach for inference about parameters of t-mixture models. The specifications of prior distributions are weakly informative to avoid causing nonintegrable posterior distributions. We present two efficient EM-type algorithms for computing the joint posterior mode with the observed data and an incomplete future vector as the sample. Markov chain Monte Carlo sampling schemes are also developed to obtain the target posterior distribution of parameters. The advantages of Bayesian approach over the maximum likelihood method are demonstrated via a set of real data.     

Maintaining the connection: Parenting as a noncustodial father

The purpose of this study was to identify relevant factors associated with the noncustodial father's frequency of contact with his child following divorce. The findings of the research revealed that a combination of demographic, personal characteristics, and psychosocial factors are related to the father's frequency of contact with the child.     

The 10% Flat Tax: Tithing and the Definition of Income

Developing a fair and widely accepted income definition presents one of the greatest challenges to tax reform. To arrive at a definition separate from the federal tax code, we surveyed 1200 Latter‐day Saints about their practice of tithing. Tithing is similar to a flat tax with no deductions, where individuals voluntarily contribute 10% of self‐defined income to the church. The results of our survey indicate that most respondents operate on a cash realization basis, which excludes savings and does not allow any deductions. Respondents' income concepts generally do not coincide with current tax laws or economists' views of comprehensive income.     

ATSDR Science Panel on the Bioavailability of Mercury in Soils: Lessons Learned

On the basis of discussion and analysis during and following an ATSDR science panel on the bioavailability of mercury in soils, it is apparent that the default assumption of 100% relative bioavailability for mercury-contaminated soils is excessively conservative. However, current knowledge does not allow the development of default assumptions or guidelines for determining relative bioavailability of mercury in soils. Until such default assumptions or guidelines can be developed, site-specific assays of bioavailability, preferably using either animal bioassays or validated in vitro techniques, may provide the best approach for estimating soil-mercury bioavailability.     

OPTIMIZING ORGANIZATIONAL GOALS IN ASSIGNING FACULTY TEACHING SCHEDULES

The administration of higher education, as with other administrative environments, has become increasingly complex. This paper presents a mathematical model which addresses one of the administrative tasks concerning administrators in all institutions, the establishment of faculty teaching schedules. The assignment technique utilized by the model is linear programming with two special characteristics: goal programming and mixed-integer programming. The goal programming characteristic refers to the provision of explicit stack variables to take on values representing deviations from assignment criteria that may result in resolving conflicts which arise from interactive administrative priorities. The mixed-integer programming characteristic refers to a requirement of the model that certain variables take on only integer values if they appear in the final solution. The model is demonstrated using two different sets of preference orderings for goal achievement.     

Bayesian analysis of a general growth curve model with predictions using power transformations and AR(1) autoregressive dependence

In this paper, we consider a Bayesian analysis of the unbalanced (general) growth curve model with AR(1) autoregressive dependence, while applying the Box-Cox power transformations. We propose exact, simple and Markov chain Monte Carlo approximate parameter estimation and prediction of future values. Numerical results are illustrated with real and simulated data.     

BUSINESS COMBINATION DECISIONS: A U.S./CANADA STUDY*

The purpose of this paper is to clarify the motives behind business combination decisions. Twenty-one favorable characteristics associated with the motivations for business combinations were selected to serve as the basis for an empirical survey. Our analysis revealed that there are seven motivational variables of varying import and that the motivations underlying business combination decisions are similar for U.S. and Canadian executives.