Least Absolute Value Regression: Necessary Sample Sizes to Use Normal Theory Inference Procedures*

Recently developed large sample inference procedures for least absolute value (LAV) regression are examined via Monte Carlo simulation to determine when sample sizes are large enough for the procedures to work effectively. A variety of different experimental settings were created by varying the disturbance distribution, the number of explanatory variables and the way the explanatory variables were generated. Necessary sample sizes range from as small as 20 when disturbances are normal to as large as 200 in extreme outlier-producing distributions.     

AN ALTERNATE MODEL FOR LEAD-TIME DEMAND: CONTINUOUS-REVIEW INVENTORY SYSTEMS

The calculation of reorder points when the distribution of lead-time demand is normal is quite complex, primarily because of having to bound it away from negative demand values. A number of researchers have sought feasible alternative forms of lead-time demand which can be calculated without undue difficulty. It is proposed here to assume a Poisson (daily) demand and an exponential lead time (days). If they are assumed independent their convolution is geometric, which is itself asymptotically exponential. This has a number of advantages: (1) The exponential lead time is appropriate where the lead time is often short (i.e., local source), occasionally longer (when the local sources stock out), and, infrequently, quite long. (2) The geometric lead-time demand is independent of changes of time scale. (3) Reorder points and lot sizes appear in simple closed form. (4) The exponential asymptote is sufficiently close that this further simplification is usually warranted.     

AN ALTERNATIVE VARIANCE-REDUCTION STRATEGY FOR RSM SIMULATION STUDIES

In the previous paper, Cooley and Houck [1] examined the simultaneous use of common and antithetic random number streams as a variance-reduction strategy for simulation studies employing response surface methodology (RSM). Our paper supplements their work and further explores pseudorandom number assignments in response surface designs. Specifically, an alternative strategy for assigning pseudorandom numbers is proposed; this strategy is more efficient than that given by Cooley and Houck, especially when more than two factors are involved.     

EFFECTS OF BUDGETARY CONTROL DESIGN ON MANAGEMENT DECISIONS: SOME EMPIRICAL EVIDENCE*

An examination of budgetary slack behavior reveals that managerial characteristics and the economic environment may contribute to the failure of budgetary control systems. Large management-controlled firms that operate in industries with high barriers to entry behave as if they allocate more slack resources when compared with owner-controlled firms of similar size and market power. The results confirm that accounting operating-income numbers reflect manifestations of slack behavior in the budgetary process. Management-controlled firms in particular seem to act as if they fail to release and redistribute slack resources. It is concluded that budgetary slack behavior points to failures in the organization design that may lead to inefficient allocation of resources and to inferior profit performance.     

A NOTE ON THE RELATIONSHIPS AMONG FINENESS,GARBLING, AND SUFFICIENCY FOR THE COMPARISON OF INFORMATION SYSTEMS*

Choice among competing information systems is an important problem for both the providers and users of information. When the attributes of the decision makers and decision problems for which information is produced are unknown or heterogeneous, it is difficult to choose among alternative information sources. Three criteria for such comparisons of information systems are available in the theory of information economics. These criteria may be distinguished by the differing restrictions that they impose on the generality of the studies in which they are used. This note explicates the relationships among these criteria and discusses the various limitations that they impose on generality.     

A PREFORMANCE ANALYSIS OF PARAMETRIC AND NONPARAMETRIC DISCRIMINANT APPROACHES TO BUSINESS DECISION MAKING

This paper empirically investigation the performance of four relatively new nonparametric techniques against four different parameteric versions of discriminant analysis. The models were constructed and analyzed using financial data drawn from 232 bankrupt and nonbankrupt companies. Generally, the nonparametric approaches, with the exception of linear programming, performed as well as or better than the more traditional discriminant analysis.     

PARTICIPATION IN BUDGETARY DECISION MAKING,TASK DIFFICULTY,LOCUS OF CONTROL,AND EMPLOYEE BEHAVIOR: AN EMPIRICAL STUDY*

This study investigates the relationship between participation in budgetary decision making and attitude (toward job and employer), taking into account the effects of task difficulty and locus of control on participation and attitude. Managers from two companies participated in the study. Attitude was found to be related positively to participation and negatively to task difficulty. Implications of the results are discusses.     

SOME DIFFICULTIES AND IMPROVEMENTS IN APPLYING LINEAR PROGRAMMING FORMULATIONS TO THE DISCRIMINANT PROBLEM

In this paper, we discuss some disturbing features of two linear programming (LP) approaches to the discriminant problem. Specifically, we show that both approaches are sensitive to the choice of origin for the data although, intuitively, placement of origin should have no effect on the method of assigning cases to groups. In addition, we show that these LP approaches may lead to discriminant functions which assign all cases to the same group. We show that the usual statistical approach to this problem does not share these difficulties, and we make recommendations for implementing these LP approaches which help to alleviate the difficulties.     

Closure,connectivity and degree distributions: Exponential random graph (p*) models for directed social networks

The new higher order specifications for exponential random graph models introduced by Snijders et al. [Snijders, T.A.B., Pattison, P.E., Robins G.L., Handcock, M., 2006. New specifications for exponential random graph models. Sociological Methodology 36, 99–153] exhibit substantial improvements in model fit compared with the commonly used Markov random graph models. Snijders et al., however, concentrated on non-directed graphs, with only limited extensions to directed graphs. In particular, they presented a transitive closure parameter based on path shortening. In this paper, we explain the theoretical and empirical advantages in generalizing to additional closure effects. We propose three new triadic-based parameters to represent different versions of triadic closure: cyclic effects; transitivity based on shared choices of partners; and transitivity based on shared popularity. We interpret the last two effects as forms of structural homophily, where ties emerge because nodes share a form of localized structural equivalence. We show that, for some datasets, the path shortening parameter is insufficient for practical modeling, whereas the structural homophily parameters can produce useful models with distinctive interpretations. We also introduce corresponding lower order effects for multiple two-path connectivity. We show by example that the in- and out-degree distributions may be better modeled when star-based parameters are supplemented with parameters for the number of isolated nodes, sources (nodes with zero in-degrees) and sinks (nodes with zero out-degrees). Inclusion of a Markov mixed star parameter may also help model the correlation between in- and out-degrees. We select some 50 graph features to be investigated in goodness of fit diagnostics, covering a variety of important network properties including density, reciprocity, geodesic distributions, degree distributions, and various forms of closure. As empirical illustrations, we develop models for two sets of organizational network data: a trust network within a training group, and a work difficulty network within a government instrumentality.