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.     

Conflict Methodology: an Introduction and Preliminary Assessment1

Proponents of conflict methodology have recently asserted that consensus methodologies are ineffective in the study of elite-controlled, large-scale organizations. They have proposed utilization of a variety of techniques which place researchers in a condition of hostile contrast to subject organizations. The purpose of this paper is to introduce and assess the conflict methodology position. It, therefore, identifies the organizational subjects of conflict research, examines the assertion that consensus methodologies are ineffective in the study of elite-controlled, large-scale organizations, and provides a definition of conflict methodology. The paper concludes by assessing the implications, problems, and possible contributions of conflict methodology.     

THE BIAS IN PERIODOGRAM ORDINATES AND THE ESTIMATION OF ARMA MODELS IN THE FREQUENCY DOMAIN

The paper has its origin in the finding that the frequency-domain estimation of ARh4A models can produce estimates which may be remarkably biased. Both of the frequency-domain estimation methods considered in the paper are based on the frequency-domain likelihood function, which depends on the periodogram ordinates of the time series. It is found that, as estimates of the spectrum ordinates, the corresponding periodogram ordinates may contain a rather remarkable bias, which again causes bias in the estimates of parameters produced by a frequency-domain estimation method of an ARMA model. The bias is reduced by tapering the observed time series. An example is given of estimation experiments for simulated time series from a pure autoregressive process of order two.     

The validity of assessing mental health needs with social indicators.

Selection of the appropriate number and types of social indicators for use in mental health planning has been a perennial problem. Social indicators have been associated with several concepts (quality of life, community disorganization, populations at risk) of varying relevance for planning mental health services, and abstracting social indicators from these conceptual domains poses a variety of validity issues. The issues are discussed, and the viability of social indicators in mental health planning is reexamined.