THE HARMS SHOE COMPANY: DEVELOPMENT OF A COMPUTER MODEL FOR THE CLASSROOM

This paper describes a case that examines five key topics in production and operations management [1]—forecasting, inventory control, transportation planning, aggregate planning, and the disaggregation of the aggregate plan—in an integrated, realistic manner for upper-level undergraduate business majors [3]. The case structure stresses the linkage of these interrelated subjects and supplements the regular classroom presentations dealing with them. The case relies on computer calculations at each stage to provide the information input for calculations at the next stage. It is a static model with a twelve-month planning horizon. Students complete several exercises and assume several roles as the case unfolds. They act on their own in filling some roles and as members of teams in performing others. They do not compete with each other, as the purpose of the case is to help them develop an appreciation of the factors that persons performing the role must consider.     

Unmet Need for Family Planning in Developing Countries and Implications for Population Policy

Unmet need for family planning has been a core concept in international population discourse for several decades. This article reviews the history of unmet need and the development of increasingly refined methods of its empirical measurement and delineates the main questions that have been raised about unmet need during the past decade, some of which concern the validity of the concept and others its role in policy debates. The discussion draws heavily on empirical research conducted during the 1990s, much of it localized, in‐depth studies combining quantitative and qualitative methodologies. Of the causes of unmet need other than those related to access to services, three emerge as especially salient: lack of necessary knowledge about contraceptive methods, social opposition to their use, and health concerns about possible side effects. The article argues that the concept of unmet need for family planning, by joining together contraceptive behavior and fertility preferences, encourages an integration of family planning programs and broader development approaches to population policy. By focusing on the fulfillment of individual aspirations, unmet need remains a defensible rationale for the formulation of population policy and a sensible guide to the design of family planning programs.     

Correcting missing-data bias in historical demography

Studies on population history are often based on incomplete records of life histories. For instance, in studies using data obtained from family reconstitution, the date of death is right censored (by migration) and the censoring time is never observed. Several methods for the correction of mortality estimates are proposed in the literature, most of which first estimate the number of individuals at risk and then use standard techniques to estimate mortality. Other methods are based on statistical models. In this paper all methods are reviewed, and their merits are compared by applying them to simulated and to seventeenth-century data from the English parish of Reigate. An ad hoc method proposed by Ruggles performs reasonably well. Methods based on statistical models, provided they are sufficiently realistic, give comparable accuracy and allow the estimation of several other quantities of interest, such as the distribution of migration times.     

Homogamy by education and migration status in Monterrey,Mexico: changes and continuities over time

This paper reviews changes in homogamy by migration status and educational level in Monterrey, Mexico, through the analysis of marriage patterns for two cohorts of men born in 1905–1934 and 1940–1969. Results show a significant increase in educational homogamy, as well as in homogamy by rural origins. The changes suggest that education has played an increasingly important role in the process of mate selection, although certain particularistic characteristics, such as being a rural immigrant, are still important in marriage formation. The paper concludes with a discussion of the implications of these findings for the relationship between homogamy and social stratification.

Emotion Regulation in Low-income Preschoolers

The present study was concerned with identifying the causes of low-income preschoolers' negative emotions and their most common regulation responses. The relations of family socialization practices and temperament to the children's emotion regulation skills were also examined. Ninety predominantly minority low-income preschoolers (46 boys) and their mothers participated. During visits to the children's preschools, observers watched for expressions of anger and sadness, and recorded the causes of the displays and the children's reactions. Mothers reported on their emotion socialization and discipline practices and their children's temperament. Although the children expressed more anger than sadness, they used more constructive reactions in response to sadness and more non-constructive reactions in response to anger. Maternal reports of appropriate family emotion were associated with low levels of non-constructive regulation responses to anger and sadness whereas reports of inconsistent parental discipline were generally associated with non-constructive regulation responses. All in all, the findings of this study are in accord with findings on middle-income children and indicate that low- and middle-income children are more alike than different with regard to the regulation of negative emotion in the peer environment.     

Designing and integrating composite networks for monitoring multivariate gaussian pollution fields

Networks of ambient monitoring stations are used to monitor environmental pollution fields such as those for acid rain and air pollution. Such stations provide regular measurements of pollutant concentrations. The networks are established for a variety of purposes at various times so often several stations measuring different subsets of pollutant concentrations can be found in compact geographical regions. The problem of statistically combining these disparate information sources into a single 'network' then arises. Capitalizing on the efficiencies so achieved can then lead to the secondary problem of extending this network. The subject of this paper is a set of 31 air pollution monitoring stations in southern Ontario. Each of these regularly measures a particular subset of ionic sulphate, sulphite, nitrite and ozone. However, this subset varies from station to station. For example only two stations measure all four. Some measure just one. We describe a Bayesian framework for integrating the measurements of these stations to yield a spatial predictive distribution for unmonitored sites and unmeasured concentrations at existing stations. Furthermore we show how this network can be extended by using an entropy maximization criterion. The methods assume that the multivariate response field being measured has a joint Gaussian distribution conditional on its mean and covariance function. A conjugate prior is used for these parameters, some of its hyperparameters being fitted empirically.     

Bayesian prediction of waiting times in stochastic models

The authors show how saddlepoint techniques lead to highly accurate approximations for Bayesian predictive densities and cumulative distribution functions in stochastic model settings where the prior is tractable, but not necessarily the likelihood or the predictand distribution. They consider more specifically models involving predictions associated with waiting times for semi‐Markov processes whose distributions are indexed by an unknown parameter θ. Bayesian prediction for such processes when they are not stationary is also addressed and the inverse‐Gaussian based saddlepoint approximation of Wood, Booth & Butler (1993) is shown to accurately deal with the nonstationarity whereas the normal‐based Lugannani & Rice (1980) approximation cannot, Their methods are illustrated by predicting various waiting times associated with M/M/q and M/G/1 queues. They also discuss modifications to the matrix renewal theory needed for computing the moment generating functions that are used in the saddlepoint methods.     

Confidence intervals for secondary parameters following a sequential test

In sequential studies, formal interim analyses are usually restricted to a consideration of a single null hypothesis concerning a single parameter of interest. Valid frequentist methods of hypothesis testing and of point and interval estimation for the primary parameter have already been devised for use at the end of such a study. However, the completed data set may warrant a more detailed analysis, involving the estimation of parameters corresponding to effects that were not used to determine when to stop, and yet correlated with those that were. This paper describes methods for setting confidence intervals for secondary parameters in a way which provides the correct coverage probability in repeated frequentist realizations of the sequential design used. The method assumes that information accumulates on the primary and secondary parameters at proportional rates. This requirement will be valid in many potential applications, but only in limited situations in survival analysis.