Quasi-binomial zero-inflated regression model suitable for variables with bounded support

In recent years, a variety of regression models, including zero-inflated and hurdle versions, have been proposed to explain the case of a dependent variable with respect to exogenous covariates. Apart from the classical Poisson, negative binomial and generalised Poisson distributions, many proposals have appeared in the statistical literature, perhaps in response to the new possibilities offered by advanced software that now enables researchers to implement numerous special functions in a relatively simple way. However, we believe that a significant research gap remains, since very little attention has been paid to the quasi-binomial distribution, which was first proposed over fifty years ago. We believe this distribution might constitute a valid alternative to existing regression models, in situations in which the variable has bounded support. Therefore, in this paper we present a zero-inflated regression model based on the quasi-binomial distribution, taking into account the moments and maximum likelihood estimators, and perform a score test to compare the zero-inflated quasi-binomial distribution with the zero-inflated binomial distribution, and the zero-inflated model with the homogeneous model (the model in which covariates are not considered). This analysis is illustrated with two data sets that are well known in the statistical literature and which contain a large number of zeros.     

A KNOWLEDGE-BASED DSS FOR MANAGERIAL PROBLEM DIAGNOSIS

A knowledge-based system supporting managerial problem diagnosis is described. The system provides the capability to monitor values of selected variables for problem situations. When problems are located, a list of problem symptoms is delivered to a problem processor for structuring and diagnosis. Problem structuring is based on a combination of concepts from expert systems and structural modeling. User assertions about cause-effect relationships between pairs of variables are maintained in a semantic network. Problem diagnosis uses the relationships in the semantic network to construct causation trees, the branches of which represent potential explanations of the problem symptoms. Mathematical models are constructed based on causation-tree branches, and values from the data base are used to test whether the model confirms the diagnosis. If so, the source of the problem has been located and it is then up to the user to resolve the problem. If the model fails to explain the problem, the model apparently is deficient and the user may perform “what if…” type scenarios in attempts to improve the model and search for problem causes. Realistic applications in the accounting and health care areas are discussed.     

Applications and Implementation DECISION SUPPORT SYSTEMS: AN EMPIRICAL ASSESSMENT FOR DECISION MAKING

A laboratory experiment examined the effects of applying decision support system (DSS) technology to decision making in ill-structured problem environments under varying information conditions. Marketing executives participated in the experiment which investigated the effects of DSS availability, DSS training, and data availability on dependent variables that included: (1) the number of alternatives considered by a subject during decision making, (2) the period of time spent by a subject to complete the decision-making process, (3) the subject's perceived confidence in the decisions he or she had made, (4) the amount of data considered by a subject's during decision making, (5) the individual subject's decision processing, and (6) the subject's performance overall. Our results indicate that all three factors significantly affect the number of alternatives considered by subjects during the decision-making process. We therefore suggest that DSS training be coordinated with decision training in order to realize the potential of DSSs as described in the DSS literature.     

A UNIVERSITY EXAMINATION-SCHEDULING MODEL TO MINIMIZE MULTIPLE-EXAMINATION DAYS FOR STUDENTS

Scheduling university examinations is often done with the objective of spreading a student's required examinations over an examination week. That is the equivalent of the problem of minimizing the number of examinations a student must take on any one day. An approach to scheduling exams which relates the problem to the classical assignment problem is discussed. The model developed is a symmetry-constrained assignment model, and the solution method requires use of a branch-and-bound algorithm. Results from application of the algorithm to six semesters of actual data are presented.     

Personal income taxation under mobility, exogenous and endogenous welfare weights, and asymmetric information

This paper analyses how governments should tax labour income accruing to a group of highly skilled and geographically mobile individuals who divide their time or career between several jurisdictions. The analysis differs from previous models on migration and taxation by addressing optimal regulation when agents work for several principals. Optimal taxation is developed for social welfare functions with exogenous and endogenous welfare weights. Marginal income taxes are applied for screening purposes, and the rates are lower with endogenous than with exogenous welfare weights. Received: 22 January 1998/Accepted: 3 July 1999)