A bayesian approach to model-based clustering for panel probit models

Consideration of latent heterogeneity is of special importance in non linear models for gauging correctly the effect of explaining variables on the dependent variable. This paper adopts the stratified model-based clustering approach for modeling latent heterogeneity for panel probit models. Within a Bayesian framework an estimation algorithm dealing with the inherent label switching problem is provided. Determination of the number of clusters is based on the marginal likelihood and out-of-sample criteria. The ability to decide on the correct number of clusters is assessed within a simulation study indicating high accuracy for both approaches. Different concepts of marginal effects incorporating latent heterogeneity at different degrees arise within the considered model setup and are directly at hand within Bayesian estimation via MCMC methodology. An empirical illustration of the developed methodology indicates that consideration of latent heterogeneity via latent clusters provides the preferred model specification compared to a pooled and a random coefficient specification. --Bayesian Estimation,MCMC Methods,Panel Probit Model,Mixture Modelling

Model-based clustering based on sparse finite Gaussian mixtures

In the framework of Bayesian model-based clustering based on a finite mixture of Gaussian distributions, we present a joint approach to estimate the number of mixture components and identify cluster-relevant variables simultaneously as well as to obtain an identified model. Our approach consists in specifying sparse hierarchical priors on the mixture weights and component means. In a deliberately overfitting mixture model the sparse prior on the weights empties superfluous components during MCMC. A straightforward estimator for the true number of components is given by the most frequent number of non-empty components visited during MCMC sampling. Specifying a shrinkage prior, namely the normal gamma prior, on the component means leads to improved parameter estimates as well as identification of cluster-relevant variables. After estimating the mixture model using MCMC methods based on data augmentation and Gibbs sampling, an identified model is obtained by relabeling the MCMC output in the point process representation of the draws. This is performed using K-centroids cluster analysis based on the Mahalanobis distance. We evaluate our proposed strategy in a simulation setup with artificial data and by applying it to benchmark data sets. (authors' abstract

Translation across modalities : the practice of translating written text into recorded signed language : an ethnographic case study

This study creates a space for analysing an emerging translational activity, the practice of translating written text into recorded signed language. With its non-prototypical modality pair of source and target texts, the activity neither matches existing conceptualisations of interpreting nor those of translation modes. In an ethnographic case study I investigate the translational mode displayed, paying particular attention to the translational process designed by the practitioner and the impact of source and target text modalities. Drawing on literacy and multimodality research, this work reaffirms that communication is embedded in social, cultural, historical and ideological contexts and foregrounds the involved (human and non-human) agents. Data generated through observation, interviews and analysis of source, target and preparatory documents reveal an event influenced by the intrinsic properties of text modalities, the translator’s socio-professional background, and socially constructed constraints and opportunities. Developing concepts of “translational practice”, “translational events” and “affordances”, I challenge the prototype-based dichotomy (translation/interpreting) used to conceptualise translational activity. By negotiating data of a non-central practice with theoretical concepts developed within Western Translation Studies, this research contributes to enlarging and de-centralising the discipline. Thickly describing one translational event, conceptualising written-signed translation practice and re-thinking central translational concepts, this study highlights implications for theory, pedagogy and the profession

Statistical aspects of elastic scattering spectroscopy with applications to cancer diagnosis

Elastic scattering spectroscopy (ESS), is a non-invasive and real-time in vivo optical diagnosis technique sensitive to changes in the physical properties of human tissue, and thus able to detect early cancer and precancerous changes. This thesis focuses on the statistical issue on how to eliminate irrelevant variations in the high-dimensional ESS spectra and extract the most useful information to enable the classification of tissue as normal or abnormal. Multivariate statistical methods have been used to tackle the problems, among which principal component discriminant analysis and partial least squares discriminant analysis are the most explored throughout the thesis as general tools for supervised dimension reduction and classification. Customized multivariate methods are proposed in the specific context of ESS. When ESS spectra are measured in vivo by a hand-held optical probe, differences in the angle and pressure of the probe are a major source of variability between the spectra from replicate measurements. A customized spectral pre-treatment called error removal by orthogonal subtraction (EROS) is designed to ameliorate the effect of this variability. This pre-treatment reduces the complexity and increases both the accuracy and interpretability of the subsequent classification models when applied to early detection of cancer risk in Barrett’s oesophagus. For the application of ESS to diagnosis of sentinel lymph node metastases in breast cancer, an automated ESS scanner was developed to take measurements from a larger area of tissue to produce ESS images for cancer diagnosis. Problems arise due to the existence of background area in the image with considerable between-node variation and no training data available. A partially supervised Bayesian multivariate finite mixture classification model with a Markov random field spatial prior in a reduced dimensional space is proposed to recognise the background area automatically at the same time as distinguishing normal from metastatic tissue