Modelling age variation in survival and reporting rates for recovery models

In this paper, we focus on models for recovery data from birds ringed as young. In some cases, it is important to be able to include in these models a degree of age variation in the reporting probability. For certain models this has been found, empirically, to result in completely flat likelihood surfaces, due to parameter redundancy. These models cannot then be fitted to the data, to produce unique parameter estimates. However, empirical evidence also exists that other models with such age variation can be fitted to data by maximum likelihood. Using the approach of Catchpole and Morgan (1996b), we can now identify which models in this area are parameter-redundant, and which are not. Models which are not parameter-redundant may still perform poorly in practice, and this is investigated through examples, involving both real and simulated data. The Akaike Information Criterion is found to select inappropriate models in a number of instances. The paper ends with guidelines for fitting models to data from birds ringed as young, when age dependence is expected in the reporting probability.     

Putting modularity in business planning and control

The use of modules in constructing corporate planning systems has been reported by companies such as I.B.M. Xerox, and Standard Oil of New Jersey. The modules may be of organizational units, or of functions such as production, distribution and finance. Modules may also be used for different types of calculations and for generating report output. A module is quite simply a standard unit. Modularity becomes more important as systems grow large, because it is one means of keeping order within large systems. Modularisation is a key concept in structuring large systems, and a useful approach to use in designing procedures for Corporate Planning.     

Intention and uncertainty at later stages of childbearing: the united states 1965 and 1970

While births may be dichotomous, fertility intentions are not inherently so. Intentions are predictions about the future and, as such, are couched in considerable uncertainty. Ignoring this uncertainty hides much of what could be learned from data on fertility intentions. This paper presents a model which allows analysis of the full range of intentions. After selecting a sample of women in the later stage of childbearing (e.g., those who intend fewer than two additional children) from the 1965and 1970 National Fertility Studies, it is shown that: (1) substantial portions of women at this stage of the reproductive life cycle were indeed uncertain of their parity-specific intention; (2) this certainty, like more firm intentions, varies by age and parity as the model predicts; and (3) there were significant shifts in the level of certainty between 1965 and 1970. Specifically, while intentions for third, fourth, and fifth births declined, more women “didn’t know” if they intended to have another child or not. Among those not intending another child, more seemed uncertain of this intention in 1970 than did comparable women in 1965. In contrast, those intending another child seemed more certain. These changes in intention and uncertainty indicate that the observed decline in intended parity was tentative. Post-1970 evidence suggests that this tentative decline has become an unequivocal one.     

Automatic starting point selection for function optimization

Traditional (non-stochastic) iterative methods for optimizing functions with multiple optima require a good procedure for selecting starting points. This paper illustrates how the selection of starting points can be made automatically by using a method based upon simulated annealing. We present a hybrid algorithm, possessing the accuracy of traditional routines, whilst incorporating the reliability of annealing methods, and illustrate its performance for a particularly complex practical problem.Now at the Statistical Laboratory, University of Cambridge, 16 Mill Lane, Cambridge, CB2 1SB, UK.     

Patterns of Advanced Manufacturing Technology Utilization and Manufacturing Capabilities

The objectives of this research are first to empirically replicate Advanced Manufacturing Technology (AMT) utilization taxonomies identified in foregoing research, second to investigate the relationship between patterns of AMT utilization and manufacturing capabilities attainment, and third to explore differences in context, and performance across AMT groups. Theories of performance frontiers and capability progression provide the basis for our hypotheses. Data were collected from 224 U.S. manufacturing plants in industries considered to have potential utilizations of AMTs. A cluster analysis of the data yields a solution that closely resembles a previous AMT utilization taxonomy, including four groups labeled, respectively, as Traditionalists, Generalists, High Investors, and Designers. Significant manufacturing capability differences across these four groups indicate that plants that utilize a broader scope of AMTs enjoy a greater breadth of manufacturing capabilities. The implied capability attainment pattern is consistent with cumulative capability theory. However, the results suggest that cost capability is not included in the capability mix when broad‐based AMT utilization is the enabler of capability gains. A post hoc exploration of the AMT groups indicates significant differences in performance across the groups. Collectively, the results extend prior research by providing added insights into the possible rationale and impact of AMT utilization patterns.     

Unit Roots: Bayesian Significance Test

The unit root problem plays a central role in empirical applications in the time series econometric literature. However, significance tests developed under the frequentist tradition present various conceptual problems that jeopardize the power of these tests, especially for small samples. Bayesian alternatives, although having interesting interpretations and being precisely defined, experience problems due to the fact that that the hypothesis of interest in this case is sharp or precise. The Bayesian significance test used in this article, for the unit root hypothesis, is based solely on the posterior density function, without the need of imposing positive probabilities to sets of zero Lebesgue measure. Furthermore, it is conducted under strict observance of the likelihood principle. It was designed mainly for testing sharp null hypotheses and it is called FBST for Full Bayesian Significance Test.