TARGETING WORKING PROFESSIONALS: A MASTER'S OF ARTS IN GERONTOLOGY

This overview of an online degree programme aims to develop a concrete illustration of an exploding array of courses and programmes. The approach at the Ethel Percy Andrus Gerontology Center, at the University of Southern California, seeks to address a targeted audience and to build on the special strengths and more unique degree programmes of the University.     

Testing for lack of fit in linear multiresponse models based on exact or near replicates

Three procedures for testing the adequacy of a proposed linear multiresponse regression model against unspecified general alternatives are considered. The model has an error structure with a matrix normal distribution which allows the vector of responses for a particular run to have an unknown covariance matrix while the responses for different runs are uncorrelated. Furthermore, each response variable may be modeled by a separate design matrix. Multivariate statistics corresponding to the classical univariate lack of fit and pure error sums of squares are defined and used to determine the multivariate lack of fit tests. A simulation study was performed to compare the power functions of the test procedures in the case of replication. Generalizations of the tests for the case in which there are no independent replicates on all responses are also presented.     

On using the jackknife to estimate quantile variance

We show that the jackknife technique fails badly when applied to the problem of estimating the variance of a sample quantile. When viewed as a point estimator, the jackknife estimator is known to be inconsistent. We show that the ratio of the jackknife variance estimate to the true variance has an asymptotic Weibull distribution with parameters 1 and 1/2. We also show that if the jackknife variance estimate is used to Studentize the sample quantile, the asymptotic distribution of the resulting Studentized statistic is markedly nonnormal, having infinite mean. This result is in stark contrast with that obtained in simpler problems, such as that of constructing confidence intervals for a mean, where the jackknife-Studentized statistic has an asymptotic standard normal distribution.     

Axiomatic foundations for fairness-motivated preferences

Much work in social choice theory takes individual preferences as uninvestigated inputs into aggregation functions designed to reflect considerations of fairness. Advances in experimental and behavioural economics show that fairness can also be an important motivation in the preferences of individuals themselves. A proper characterisation of how fairness concerns enter such preferences can enrich the informational basis of many social choice exercises. This paper proposes axiomatic foundations for individual fairness-motivated preferences that cover most of the models developed to rationalise observed behaviour in experiments. These models fall into two classes: Outcome-based models, which see preferences as defined only over distributive outcomes, and context-dependent models, which allow rankings over distributive outcomes to change systematically with non-outcome factors. I accommodate outcome-based and context-sensitive fairness concerns by modelling fairness-motivated preferences as a reference-dependent preference structure. I first present a set of axioms and two theorems that generate commonly used outcome-based models as special cases. I then generalise the axiomatic basis to allow for reference-dependence, and derive a simple functional form in which the weight on each person’s payoff depends on a reference vector of how much each person deserves.     

Child care outcomes: economic perspectives and issues

Economics starts with the observation of widespread scarcity and the consequent need for choices about resource allocation. Common criteria for such choices are economy, effectiveness, efficiency and equity. This paper defines these criteria and locates them within a conceptual framework (the ‘production of welfare’). It also examines some of the reasons for the growth in interest in them. The main modes of evaluation—cost-benefit, cost-effectiveness, cost-utility and cost-consequences analyses—are described and illustrated. Few such evaluations have been conducted in child care, and the paper ends with speculation as to why this is so.     

Land-Grant University-Industry Relationships in Biotechnology: A Comparison with the Non-Land-Grant Research Universities1

Abstract The results of a 1986 survey of 185 biotechnology faculty in the land-grant colleges of agriculture are presented. Comparisons are made to a previous survey of biotechnology faculty in nonagricultural research universities. The survey investigates issues raised by industrial involvement in university biotechnology research and the impact of the new biosciences on the agricultural colleges. It was found that college of agriculture biotechnologists have more recently received their highest degrees, show more industrial involvement, and are more positive about the possible benefits of that involvement than their nonagricultural university counterparts. Industrial funding proved to be a significant exogenous variable affecting the activities and attitudes of agricultural college biotechnology faculty. Those with higher levels of industrial funding appear to have lower university productivity.     

Inference in hybrid Bayesian networks using dynamic discretization

We consider approximate inference in hybrid Bayesian Networks (BNs) and present a new iterative algorithm that efficiently combines dynamic discretization with robust propagation algorithms on junction trees. Our approach offers a significant extension to Bayesian Network theory and practice by offering a flexible way of modeling continuous nodes in BNs conditioned on complex configurations of evidence and intermixed with discrete nodes as both parents and children of continuous nodes. Our algorithm is implemented in a commercial Bayesian Network software package, AgenaRisk, which allows model construction and testing to be carried out easily. The results from the empirical trials clearly show how our software can deal effectively with different type of hybrid models containing elements of expert judgment as well as statistical inference. In particular, we show how the rapid convergence of the algorithm towards zones of high probability density, make robust inference analysis possible even in situations where, due to the lack of information in both prior and data, robust sampling becomes unfeasible.