Preference in flower visits and partitioning in pollen diets of stingless bees in an Asian tropical rain forest

Floral resource partitioning among stingless bees (Trigona, Meliponini, Apidae) in a lowland rain forest in Sarawak, Malaysia, was investigated using tree towers and walkways in a 4-year study that included a general flowering period. We obtained 100 collections of insect visitors to flowers of varying floral location and shape representing 81 plant species. The tendency of 11 species of stingless bees to visit specific flowers with a particular floral location and shape was analyzed by logistic regression analysis. This analysis showed that the proportion of flower visitor collections containing Trigona fuscobalteata and T. melanocephala differed according to floral location. The former was frequently collected at canopy and gap flowers, whereas the latter was most often collected at understory flowers. The analysis also suggested that T. erythrogastra was more rarely collected at shallow flowers than at deep flowers. Analysis of the pollen diets of T. collina, T. fuscobalteata, T. melanocephala, and T. melina revealed that similarity of pollen sources differed among the six permutated pairs of the four species. The lowest mean rank of similarity found was between T. fuscobalteata and T. melanocephala. This result supports the hypothesis that preference in visiting flowers in different locations leads to pollen resource partitioning. Received: May 14, 1997 / Accepted: April 23, 1999     

Retrospective information on health status and its application for population health measures

Healthy life expectancies are almost always calculated by using health data from cross-sectional surveys. This type of calculation is done partly because data from longitudinal surveys are not always available, and when they are available, they are collected at intervals that are longer than one year. In such cases, collecting health information retrospectively for the years skipped by the survey is useful. The main purpose of this paper is to show how retrospective health information can be used to estimate life expectancies in different health states. Healthy life expectancies are estimated with and without using data on retrospective health information, and the corresponding estimates are compared. The two sets of estimates are similar. We conclude that retrospectively assessed health information based on a one-year recall period can be used to estimate years of life in various health states and that estimates based on such information will closely approximate estimates based on concurrent health information.     

MANAGING ACADEMIC INTERDISCIPLINARY RESEARCH PROJECTS*

This field study provides variables that can be used by practitioners as action levers and by future researchers as the basis for theoretical development. Conclusions from relevant literature and findings from interviews with interdisciplinary research management identified forty variables that were viewed as important to interdisciplinary research project success. After adjusting the data for reliability attenuation, these variables were further analyzed to identify the best prediction equation. The findings suggested that project age or the longevity of the project and open discussion of disagreements were the best predictors of performance.     

When Adaptations Go Awry: Functional and Dysfunctional Aspects of Social Anxiety

Adaptations are psychological and behavioral mechanisms designed through evolution to serve specific purposes ultimately related to reproductive success. Although adaptations are inherently functional, in some cases their operation can nevertheless cause personal and social dysfunction. We describe a theoretical framework for understanding, predicting, and reducing the dysfunctional consequences of psychological adaptations. We discuss three general sources of dysfunction: a) the existence of adaptive tradeoffs, b) mismatches between current environments and ancestral environments, and c) individual differences. The paper applies this framework primarily to the topic of social anxiety, a psychological phenomenon marked by concerns pertaining to social rejection and embarrassment. Although social anxiety can serve useful functions, it can also involve excessive worry, negative affect, and avoidance of social situations, leading to significant distress and social impairment. We consider sources of dysfunction in social anxiety and discuss implications for policy, including recommendations for psychological, situational, and biological interventions. We also discuss broader applications of this theoretical framework to other areas of social life.     

A pragmatic approach to experimental design in industry

The importance of statistically designed experiments in industry has been well recognized. However, the use of 'design of experiments' is still not pervasive, owing in part to the inefficient learning process experienced by many non-statisticians. In this paper, the nature of design of experiments, in contrast to the usual statistical process control techniques, is discussed. It is then pointed out that for design of experiments to be appreciated and applied, appropriate approaches should be taken in training, learning and application. Perspectives based on the concepts of objective setting and design under constraints can be used to facilitate the experimenters' formulation of plans for collection, analysis and interpretation of empirical information. A review is made of the expanding role of design of experiments in the past several decades, with comparisons made of the various formats and contexts of experimental design applications, such as Taguchi methods and Six Sigma. The trend of development shows that, from the realm of scientific research to business improvement, the competitive advantage offered by design of experiments is being increasingly felt.     

Boosting with Bayesian stumps

Boosting is a new, powerful method for classification. It is an iterative procedure which successively classifies a weighted version of the sample, and then reweights this sample dependent on how successful the classification was. In this paper we review some of the commonly used methods for performing boosting and show how they can be fit into a Bayesian setup at each iteration of the algorithm. We demonstrate how this formulation gives rise to a new splitting criterion when using a domain-partitioning classification method such as a decision tree. Further we can improve the predictive performance of simple decision trees, known as stumps, by using a posterior weighted average of them to classify at each step of the algorithm, rather than just a single stump. The main advantage of this approach is to reduce the number of boosting iterations required to produce a good classifier with only a minimal increase in the computational complexity of the algorithm.     

Heteroscedastic Regression Models and Applications to Off-line Quality Control

We discuss in the present paper the analysis of heteroscedastic regression models and their applications to off-line quality control problems. It is well known that the method of pseudo-likelihood is usually preferred to full maximum likelihood since estimators of the parameters in the regression function obtained are more robust to misspecification of the variance function. Despite its popularity, however, existing theoretical results are difficult to apply and are of limited use in many applications. Using more recent results in estimating equations, we obtain an efficient algorithm for computing the pseudo-likelihood estimator with desirable convergence properties and also derive simple, explicit and easy to apply asymptotic results. These results are used to look in detail at variance minimization in off-line quality control, yielding techniques of inferences for the optimized design parameter. In application of some existing approaches to off-line quality control, such as the dual response methodology, rigorous statistical inference techniques are scarce and difficult to obtain. An example of off-line quality control is presented to discuss the practical aspects involved in the application of the results obtained and to address issues such as data transformation, model building and the optimization of design parameters. The analysis shows very encouraging results, and is seen to be able to unveil some important information not found in previous analyses.     

A note on kernel density estimation for non-negative random variables

Kernel-based density estimation algorithms are inefficient in presence of discontinuities at support endpoints. This is substantially due to the fact that classic kernel density estimators lead to positive estimates beyond the endopoints. If a nonparametric estimate of a density functional is required in determining the bandwidth, then the problem also affects the bandwidth selection procedure. In this paper algorithms for bandwidth selection and kernel density estimation are proposed for non-negative random variables. Furthermore, the methods we propose are compared with some of the principal solutions in the literature through a simulation study.     

On predictive distributions and Bayesian networks

In this paper we are interested in discrete prediction problems for a decision-theoretic setting, where the task is to compute the predictive distribution for a finite set of possible alternatives. This question is first addressed in a general Bayesian framework, where we consider a set of probability distributions defined by some parametric model class. Given a prior distribution on the model parameters and a set of sample data, one possible approach for determining a predictive distribution is to fix the parameters to the instantiation with the maximum a posteriori probability. A more accurate predictive distribution can be obtained by computing the evidence (marginal likelihood), i.e., the integral over all the individual parameter instantiations. As an alternative to these two approaches, we demonstrate how to use Rissanen's new definition of stochastic complexity for determining predictive distributions, and show how the evidence predictive distribution with Jeffrey's prior approaches the new stochastic complexity predictive distribution in the limit with increasing amount of sample data. To compare the alternative approaches in practice, each of the predictive distributions discussed is instantiated in the Bayesian network model family case. In particular, to determine Jeffrey's prior for this model family, we show how to compute the (expected) Fisher information matrix for a fixed but arbitrary Bayesian network structure. In the empirical part of the paper the predictive distributions are compared by using the simple tree-structured Naive Bayes model, which is used in the experiments for computational reasons. The experimentation with several public domain classification datasets suggest that the evidence approach produces the most accurate predictions in the log-score sense. The evidence-based methods are also quite robust in the sense that they predict surprisingly well even when only a small fraction of the full training set is used.     

Estimating the propagation rate of a viral infection of potato plants via mixtures of regressions

A problem arising from the study of the spread of a viral infection among potato plants by aphids appears to involve a mixture of two linear regressions on a single predictor variable. The plant scientists studying the problem were particularly interested in obtaining a 95% confidence upper bound for the infection rate. We discuss briefly the procedure for fitting mixtures of regression models by means of maximum likelihood, effected via the EM algorithm. We give general expressions for the implementation of the M-step and then address the issue of conducting statistical inference in this context. A technique due to T. A. Louis may be used to estimate the covariance matrix of the parameter estimates by calculating the observed Fisher information matrix. We develop general expressions for the entries of this information matrix. Having the complete covariance matrix permits the calculation of confidence and prediction bands for the fitted model. We also investigate the testing of hypotheses concerning the number of components in the mixture via parametric and 'semiparametric' bootstrapping. Finally, we develop a method of producing diagnostic plots of the residuals from a mixture of linear regressions.