Correcting for misclassification error in gross flows using double sampling: moment-based inference vs. likelihood-based inference

Gross flows are discrete longitudinal data that are defined as transition counts, between a finite number of states, from one point in time to another. We discuss the analysis of gross flows in the presence of misclassification error via double sampling methods. Traditionally, adjusted for misclassification error estimates are obtained using a moment-based estimator. We propose a likelihood-based approach that works by simultaneously modeling the true transition process and the misclassification error process within the context of a missing data problem. Monte-Carlo simulation results indicate that the maximumlikelihood estimator is more efficient than the moment-based estimator

The Abduction of Disorder in Psychiatry

The evolutionary cornerstone of J. C. Wakefield's (1999) harmful dysfunction thesis is a faulty assumption of comparability between mental and biological processes that overlooks the unique plasticity and openness of the brain?s functioning design. This omission leads Wakefield to an idealized concept of natural mental functions, illusory interpretations of mental disorders as harmful dysfunctions, and exaggerated claims for the validity of his explanatory and stipulative proposals. The authors argue that there are numerous ways in which evolutionarily intact mental and psychological processes, combined with striking discontinuities within and between evolutionary and contemporary social/cultural environments, may cause non-dysfunction variants of many widely accepted major mental disorders. These examples undermine many of Wakefield's arguments for adopting a harmful dysfunction concept of mental disorder

The design research pyramid: a three layer framework

To support knowledge-based design development, considerable research has been conducted from various perspectives at different levels. The research on knowledge-based design support systems, generic design artefact and design process modelling, and the inherent quality of design knowledge itself are some examples of these perspectives. The structure underneath the research is not a disparate one but ordered. This paper provides an overview of some ontologies of design knowledge and a layered research framework of knowledge-based engineering design support. Three layers of research are clarified in this pattern: knowledge ontology, design knowledge model, and application. Specifically, the paper highlights ontologies of design knowledge by giving a set of classifications of design knowledge from different points of view. Within the discussion of design knowledge content ontology, two topologies, i.e., teleology and evolutionary, are identified

On a generalization of distance sets

A subset $X$ in the $d$-dimensional Euclidean space is called a $k$-distance set if there are exactly $k$ distinct distances between two distinct points in $X$ and a subset $X$ is called a locally $k$-distance set if for any point $x$ in $X$, there are at most $k$ distinct distances between $x$ and other points in $X$. Delsarte, Goethals, and Seidel gave the Fisher type upper bound for the cardinalities of $k$-distance sets on a sphere in 1977. In the same way, we are able to give the same bound for locally $k$-distance sets on a sphere. In the first part of this paper, we prove that if $X$ is a locally $k$-distance set attaining the Fisher type upper bound, then determining a weight function $w$, $(X,w)$ is a tight weighted spherical $2k$-design. This result implies that locally $k$-distance sets attaining the Fisher type upper bound are $k$-distance sets. In the second part, we give a new absolute bound for the cardinalities of $k$-distance sets on a sphere. This upper bound is useful for $k$-distance sets for which the linear programming bound is not applicable. In the third part, we discuss about locally two-distance sets in Euclidean spaces. We give an upper bound for the cardinalities of locally two-distance sets in Euclidean spaces. Moreover, we prove that the existence of a spherical two-distance set in $(d-1)$-space which attains the Fisher type upper bound is equivalent to the existence of a locally two-distance set but not a two-distance set in $d$-space with more than $d(d+1)/2$ points. We also classify optimal (largest possible) locally two-distance sets for dimensions less than eight. In addition, we determine the maximum cardinalities of locally two-distance sets on a sphere for dimensions less than forty.Comment: 17 pages, 1 figur

Categorisation of designs according to preference values for shape rules

Shape grammars have been used to explore design spaces through design generation according to sets of shape rules with a recursive process. Although design space exploration is a persistent issue in computational design research, there have been few studies regarding the provision of more preferable and refined outcomes to designers. This paper presents an approach for the categorisation of design outcomes from shape grammar systems to support individual preferences via two customised viewpoints: (i) absolute preference values of shape rules and (ii) relative preference values of shape rules with shape rule classification levels with illustrative examples