The Theological Dimension of Community Life

xvi, 431 hal; 23,5 c

Infant mortality and isotopic complexity: new approaches to stress, maternal health, and weaning

Objectives: Studies of the carbon and nitrogen stable isotope ratios (δ13C and δ15N) of modern tissues with a fast turnover, such as hair and fingernails, have established the relationship between these values in mothers and their infants during breastfeeding and weaning. Using collagen from high-resolution dentine sections of teeth, which form in the perinatal period we investigate the relationship between diet and physiology in this pivotal stage of life. Materials and Methods: Childhood dentine collagen δ13C and δ15N profiles were produced from horizontal sections of permanent and deciduous teeth following the direction of development. These were from two 19th-century sites (n = 24) and a small number (n = 5) of prehistoric samples from Great Britain and Ireland. Results: These high-resolution data exhibit marked differences between those who survived childhood and those who did not, the former varying little and the latter fluctuating widely. Discussion: Breastfeeding and weaning behavior have a significant impact on the morbidity and mortality of infants and the adults they become. In the absence of documentary evidence, archaeological studies of bone collagen of adults and juveniles have been used to infer the prevalence and duration of breastfeeding. These interpretations rely on certain assumptions about the relationship between isotope ratios in the bone collagen of the adult females and the infants who have died. The data from this study suggest a more complex situation than previously proposed and the potential for a new approach to the study of maternal and infant health in past populations

A simulated annealing methodology for clusterwise linear regression

In many regression applications, users are often faced with difficulties due to nonlinear relationships, heterogeneous subjects, or time series which are best represented by splines. In such applications, two or more regression functions are often necessary to best summarize the underlying structure of the data. Unfortunately, in most cases, it is not known a priori which subset of observations should be approximated with which specific regression function. This paper presents a methodology which simultaneously clusters observations into a preset number of groups and estimates the corresponding regression functions' coefficients, all to optimize a common objective function. We describe the problem and discuss related procedures. A new simulated annealing-based methodology is described as well as program options to accommodate overlapping or nonoverlapping clustering, replications per subject, univariate or multivariate dependent variables, and constraints imposed on cluster membership. Extensive Monte Carlo analyses are reported which investigate the overall performance of the methodology. A consumer psychology application is provided concerning a conjoint analysis investigation of consumer satisfaction determinants. Finally, other applications and extensions of the methodology are discussed.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/45745/1/11336_2005_Article_BF02296405.pd

A stochastic multidimensional scaling procedure for the empirical determination of convex indifference curves for preference/choice analysis

The vast majority of existing multidimensional scaling (MDS) procedures devised for the analysis of paired comparison preference/choice judgments are typically based on either scalar product (i.e., vector) or unfolding (i.e., ideal-point) models. Such methods tend to ignore many of the essential components of microeconomic theory including convex indifference curves, constrained utility maximization, demand functions, et cetera. This paper presents a new stochastic MDS procedure called MICROSCALE that attempts to operationalize many of these traditional microeconomic concepts. First, we briefly review several existing MDS models that operate on paired comparisons data, noting the particular nature of the utility functions implied by each class of models. These utility assumptions are then directly contrasted to those of microeconomic theory. The new maximum likelihood based procedure, MICROSCALE, is presented, as well as the technical details of the estimation procedure. The results of a Monte Carlo analysis investigating the performance of the algorithm as a number of model, data, and error factors are experimentally manipulated are provided. Finally, an illustration in consumer psychology concerning a convenience sample of thirty consumers providing paired comparisons judgments for some fourteen brands of over-the-counter analgesics is discussed.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/45748/1/11336_2005_Article_BF02294463.pd

Microeconomic Theory : A Mathematical Approach

xx.420 Hal.;21 c

Microeconomic Theory A Mathematical Approach

xvi.431 hal.;ill.;24 c