Multivariate modelling of responses to conditional items: New possibilities for latent class analysis

Questionnaire data may contain missing values because certain questions do not apply to all respondents. For instance, questions addressing particular attributes of a symptom, such as frequency, triggers or seasonality, are only applicable to those who have experienced the symptom, while for those who have not, responses to these items will be missing. This missing information does not fall into the category 'missing by design', rather the features of interest do not exist and cannot be measured regardless of survey design. Analysis of responses to such conditional items is therefore typically restricted to the subpopulation in which they apply. This article is concerned with joint multivariate modelling of responses to both unconditional and conditional items without restricting the analysis to this subpopulation. Such an approach is of interest when the distributions of both types of responses are thought to be determined by common parameters affecting the whole population. By integrating the conditional item structure into the model, inference can be based both on unconditional data from the entire population and on conditional data from subjects for whom they exist. This approach opens new possibilities for multivariate analysis of such data. We apply this approach to latent class modelling and provide an example using data on respiratory symptoms (wheeze and cough) in children. Conditional data structures such as that considered here are common in medical research settings and, although our focus is on latent class models, the approach can be applied to other multivariate models.

The effects of impingement and dysplasia on stress distributions in the hip joint during sitting and walking: a finite element analysis

Soft tissue damage has been observed in hip joints with pathological geometries. Our primary goal was to study the relationship between morphological variations of the bony components of the hip and resultant stresses within the soft tissues of the joint during routine daily activities. The secondary goal was to find the range of morphological parameters in which stresses are minimized. Computational models of normal and pathological joints were developed based on variations of morphological parameters of the femoral head (Alpha angle) and acetabulum (CE angle). The Alpha angle was varied between 40 degrees (normal joint) and 80 degrees (cam joint). The CE angle was varied between 0 degrees (dysplastic joint) and 40 degrees (pincer joint). Dynamic loads and motions for walking and standing to sitting were applied to all joint configurations. Contact pressures and stresses were calculated and crosscompared to evaluate the influence of morphology. The stresses in the soft tissues depended strongly on the head and acetabular geometry. For the dysplastic joint, walking produced high acetabular rim stresses. Conversely, for impinging joints, standing-to-sitting activities that involved extensive motion were critical, inducing excessive distortion and shearing of the tissue-bone interface. Zones with high von Mises stresses corresponded with clinically observed damage zones in the acetabular cartilage and labrum. Hip joint morphological parameters that minimized were 20 degrees

Multiasset Derivatives and Joint Distributions of Asset Prices

Several of multiasset derivatives like basket options or options on the weighted maximum of assets exhibit the property that their prices determine uniquely the underlying asset distribution. Related to that the question how to retrieve this distributions from the corresponding derivatives quotes will be discussed. On the contrary, the prices of exchange options do not uniquely determine the underlying distributions of asset prices and the extent of this non-uniqueness can be characterised. The discussion is related to a geometric interpretation of multiasset derivatives as support functions of convex sets. Following this, various symmetry properties for basket, maximum and exchange options are discussed alongside with their geometric interpretations and some decomposition results for more general payoff functions.

On low-dimensional projections of high-dimensional distributions

Let P be a probability distribution on q -dimensional space. The so-called Diaconis-Freedman effect means that for a fixed dimension d<distributions. The present paper provides necessary and sufficient conditions for this phenomenon in a suitable asymptotic framework with increasing dimension q . It turns out, that the conditions formulated by Diaconis and Freedman (1984) are not only sufficient but necessary as well. Moreover, letting P ^ be the empirical distribution of n independent random vectors with distribution P , we investigate the behavior of the empirical process n √ (P ^ −P) under random projections, conditional on P ^ .