A NOTE ON A MULTIVARIATE GENERALIZATION OF THE KRUSKAL-WALLIS TEST

Marketers are often interested in testing whether the mean vectors of multivariate distributions are equal. The test usually applied, one-way MANOVA, assumes the distributions are multinormal. Unfortunately, this assumption is not supported in many studies. As an alternative, a nonparametric multivariate one-way analysis of variance procedure is presented.     

PSYCHOLOGICAL DETERMINANTS OF BOUNDED RATIONALITY: IMPLICATIONS FOR DECISION-MAKING STRATEGIES

In this paper the implications of cognitive strain for decision-making effectiveness are explored. Literature concerning the psychological processes of decisionmakers which underlie the operation of cognitive strain and which predispose them to the effects of cognitive strain, is reviewed. Next, the impact of cognitive strain on narrowly constraining or “bounding” rational decision-making is examined, and the choice strategies of satisficing and incrementalizing are criticized as failing to broaden the bounds of rational decision-making. Finally, elements of an eclectic choice strategy designed to assist decision-makers handle the informational demands of complex decision problems are discussed.     

Decision Making in `Random in a Broad Sense' Environments

It is shown that the uncertainty connected with a `random in a broad sense' (not necessarily stochastic) event always has some `statistical regularity' (SR) in the form of a family of finite-additive probability distributions. The specific principle of guaranteed result in decision making is introduced. It is shown that observing this principle of guaranteed result leads to determine the one optimality criterion corresponding to a decision system with a given `statistical regularity'.     

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.     

Factors influencing Soay sheep survival

We present a survival analysis of Soay sheep mark recapture and recovery data. Unlike previous conditional analyses, it is not necessary to assume equality of recovery and recapture probabilities; instead these are estimated by maximum likelihood. Male and female sheep are treated separately, with the higher numbers and survival probabilities of the females resulting in a more complex model than that used for the males. In both cases, however, age and time aspects need to be included and there is a strong indication of a reduction in survival for sheep aged 7 years or more. Time variation in survival is related to the size of the population and selected weather variables, by using logistic regression. The size of the population significantly affects the survival probabilities of male and female lambs, and of female sheep aged 7 or more years. March rainfall and a measure of the North Atlantic oscillation are found to influence survival significantly for all age groups considered, for both males and females. Either of these weather variables can be used in a model. Several phenotypic and genotypic individual covariates are also fitted. The only covariate which is found to influence survival significantly is the type of horn of first-year female sheep. There is a substantial variation in the recovery probabilities over time, reflecting in part the increased effort when a population crash was expected. The goodness of fit of the model is checked by using graphical procedures.     

Binary probability maps using a hidden conditional autoregressive Gaussian process with an application to Finnish common toad data

The Finnish common toad data of Heikkinen and Hogmander are reanalysed using an alternative fully Bayesian model that does not require a pseudolikelihood approximation and an alternative prior distribution for the true presence or absence status of toads in each 10 km×10 km square. Markov chain Monte Carlo methods are used to obtain posterior probability estimates of the square-specific presences of the common toad and these are presented as a map. The results are different from those of Heikkinen and Hogmander and we offer an explanation in terms of the prior used for square-specific presence of the toads. We suggest that our approach is more faithful to the data and avoids unnecessary confounding of effects. We demonstrate how to extend our model efficiently with square-specific covariates and illustrate this by introducing deterministic spatial changes.     

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.     

Dynamic models and attainment in the workplace

The two most marked trends in recent stratification studies are: (1) the shift away from a statical approach to attainment and toward a dynamical representation of achievement, and (2) the shift away from the assumption that achievement is largely a matter of individual characteristics, and toward the view that achievement is the outcome of an employer-employee exchange of productive resources for earnings and status. This paper forges a link between these parallel trends by elaborating on previous formulations of dynamic models of achievement and applying the results to the analysis of earnings attainment in an internal labor market. The modeling section of the paper joins within a single framework the growing interest in ascertaining how a given structure of opportunity shapes achievement and in determining the different points in the career line at which individual background and resource variables impact attainment. Special attention is devoted to the problems facing researchers who wish to bring a dynamic conceptualization of achievement to cross-sectional or otherwise deficient data. Although the empirical application of the various models is largely meant to be illustrative, it is of interest in its own right because it goes substantially beyond previous efforts in this area.     

Elimination ofSitotroga cerealella (Olivier) bySitophilus zeamais Motschulsky from mixed laboratory cultures on maize

Summary This paper describes a series of experiments conducted to determine whySitophilus zeamais Mots. andSitotroga cerealella (Oliv.) could not survive together in maize cultures in the laboratory. The effect ofS. zeamais on different developmental stages ofS. cerealella was investigated. The presence of adultS. zeamais slightly affected moth copulation, egg laying and moth eggs in a mixed culture, but large numbers of developing moths inside maize grains were killed by the adult weevil through feeding on the grains. The major cause of elimination ofS. cerealella byS. zeamais from mixed cultures was therefore found to be damage to the immature moths in grain and such moth mortality increased as the developing moths became bigger in the grains. A weevil: grain ratio of approximately 1.4∶1 was found to be the critical weevil density at which the moth disappeared from the mixed cultures.     

The measurement of diversity: Ethnic and socioeconomic mixing in residential areas

Units of observation such as census tracts continue to be analyzed according to various modal characteristics while the variation or diversity existent in such units is often ignored. The qualitative or nominal-level indicators of diversity are examined which (1) are operative in the polytomous situation, and (2) measure within-unit diversity rather than divergences among units. Six qualitative indicators are explained and compared both theoretically and by example, with the Index of Qualitative Variation suggested as the most appropriate measure of diversity when variables representing a nominal scale are used. Quantitative or interval-level diversity also was examined with six measures analyzed, representing three operational situations. Because of the susceptibilities of five of the quantitative measures to skewness and variable sample sizes, the coefficient of variation was recommended for interval-level variables to evaluate within-unit diversity.