Assisting the High Administrative Court in Restricting Too Broad a Concept of Academic Judgment

I have received substantial monetary compensation and a formal apology from my first doctoral school, and a Ph.D. from another university. This essay describes my personal view on discussing the boundaries of academic judgment and research supervision with the ombudsman agency for higher education, and at the High Administrative Court of England and Wales. The Court’s judicial doctrine addresses substantial research accountability matters. It clarifies that although the Court and ombudsman agency must not interfere with academic judgment, not everything done by an academic can be considered as academic judgment. A Ph.D. supervisor can seriously fail to perform his/her duties.     

Does the prevention of brain ageing constitute anti-ageing medicine? Outline of a new space of representation for Alzheimer's Disease

In this paper we pose the question: Does the prevention of brain ageing constitute anti-ageing medicine? We answer the question by looking at recent changes in the knowledge and organisational structure of dementia research, practice and policy and investigating the boundary relations between this disease-specific field and anti-ageing medicine. Drawing on a qualitative study of the ways in which different clinical and scientific constituencies work to define, organise and negotiate the boundaries between normal and abnormal cognitive aging, we suggest that the field of dementia research, practice and policy has experienced internal diversification forming a ‘new space of representation'. We show how this space is structured by two axes: one that relates to how AD is conceptualised and another that concerns the modes of intervention or actions that will prevent the condition in individuals or populations. We argue that while these changes have blurred the epistemic boundaries between dementia and other fields of research, there are still strong institutional, economic and political factors that glue the field together.     

Delta Method,Moment Convergence,and Inference

Statistics, as functions of the observations, are usually given by well-behaved functions. This fact is used to obtain limit distributions for statistics whose components are given by asymptotically linear functions. These results are then extended to the moments of distributions, covariance matrices and confidence regions for parameters of interest. These regions may be used to test, through duality, hypothesis on these parameters. A theoretical application is presented.     

Strong consistency of least squares estimates with i.i.d. errors with mean values not necessarily defined

We establish strong consistency of the least squares estimates in multiple regression models discarding the usual assumption of the errors having null mean value. Thus, we required them to be i.i.d. with absolute moment of order r, 0<r<2, and null mean value when r>1. Only moderately restrictive conditions are imposed on the model matrix. In our treatment, we use an extension of the Marcinkiewicz–Zygmund strong law to overcome the errors mean value not being defined. In this way, we get a unified treatment for the case of i.i.d. errors extending the results of some previous papers.     

Tracking Transformations in Health Movement Organisations: Alzheimer's Disease Organisations and their Changing ‘Cause Regimes’

In this paper, we aim to contribute to the elaboration of a framework for the systematic periodisation of health social movement organisations (HSMOs). Drawing on historical and contemporaneous data on two organisations that identify as Alzheimer's disease movement organisations (the Alzheimer's Society in Britain and the Alzheimer Society of Ireland), we consider transformations in these organisations' ‘cause regimes’. By cause regime, we refer to who and what an HSMO is fighting for, as articulated in its public self-identifications; to the broader framing of the cause and to how organisations' public self-identifications of their cause can govern or regulate their operation, including their interactions with and representations of those on whose behalf they advocate. We show that the transformation of HSMOs' cause regime can give rise to a series of organisational tensions and challenges, including the alignment of the public identification of its cause with the patient identities it promotes, or its day-to-day ‘patient identity work’.     

Modeling of paired zero-inflated continuous data without breaking down paired designs

Paired data have been widely collected in the efficiency studies of a new method against an established method in environmental, ecological and medical studies. For example, in comparative fishing studies, ability of catching target species (fish catch) or reducing the catch of non-target species (fish bycatch) is usually investigated through a paired design. These paired fish catches by weight are generally skewed and continuous, but with a significant portion of exact zeros (no catch). Such zero-inflated continuous data are traditionally handled by two-part models where the zero and positive components are handled separately; however, this separation generally destroys paired structure, and thus may result in substantial difficulty in characterizing the relative efficiency between two methods. To overcome this problem, we consider compound Poisson mixed model for paired data with which the zero and non-zero components are characterized in an integral way. In our approach, the clustering effects by pair are captured by incorporating relevant random effects. Our model is estimated using orthodox best linear unbiased predictor approach. Unlike two-part models, our approach unifies inferences of zero and positive components. Our method is illustrated with analyses of winter flounder bycatch data and ultrasound safety data.     

From distance sampling to spatial capture–recapture

Distance sampling and capture–recapture are the two most widely used wildlife abundance estimation methods. capture–recapture methods have only recently incorporated models for spatial distribution and there is an increasing tendency for distance sampling methods to incorporated spatial models rather than to rely on partly design-based spatial inference. In this overview we show how spatial models are central to modern distance sampling and that spatial capture–recapture models arise as an extension of distance sampling methods. Depending on the type of data recorded, they can be viewed as particular kinds of hierarchical binary regression, Poisson regression, survival or time-to-event models, with individuals’ locations as latent variables and a spatial model as the latent variable distribution. Incorporation of spatial models in these two methods provides new opportunities for drawing explicitly spatial inferences. Areas of likely future development include more sophisticated spatial and spatio-temporal modelling of individuals’ locations and movements, new methods for integrating spatial capture–recapture and other kinds of ecological survey data, and methods for dealing with the recapture uncertainty that often arise when “capture” consists of detection by a remote device like a camera trap or microphone.