Origin of Crashes in 3 US stock markets: Shocks and Bubbles

This paper presents an exclusive classification of the largest crashes in Dow Jones Industrial Average (DJIA), SP500 and NASDAQ in the past century. Crashes are objectively defined as the top-rank filtered drawdowns (loss from the last local maximum to the next local minimum disregarding noise fluctuations), where the size of the filter is determined by the historical volatility of the index. It is shown that {\it all} crashes can be linked to either an external shock, {\it e.g.}, outbreak of war, {\it or} a log-periodic power law (LPPL) bubble with an empirically well-defined complex value of the exponent. Conversely, with one sole exception {\it all} previously identified LPPL bubbles are followed by a top-rank drawdown. As a consequence, the analysis presented suggest a one-to-one correspondence between market crashes defined as top-rank filtered drawdowns on one hand and surprising news and LPPL bubbles on the other. We attribute this correspondence to the Efficient Market Hypothesis effective on two quite different time scales depending on whether the market instability the crash represent is internally or externally generated.Comment: 7 pages including 3 tables and 3 figures. Subm. for Proceeding of Frontier Science 200

Outlier Detection for Shape Model Fitting

Medical image analysis applications often benefit from having a statistical shape model in the background. Statistical shape models are generative models which can generate shapes from the same family and assign a likelihood to the generated shape. In an Analysis-by-synthesis approach to medical image analysis, the target shape to be segmented, registered or completed must first be reconstructed by the statistical shape model. Shape models accomplish this by either acting as regression models, used to obtain the reconstruction, or as regularizers, used to limit the space of possible reconstructions. However, the accuracy of these models is not guaranteed for targets that lie out of the modeled distribution of the statistical shape model. Targets with pathologies are an example of out-of-distribution data. The target shape to be reconstructed has deformations caused by pathologies that do not exist on the healthy data used to build the model. Added and missing regions may lead to false correspondences, which act as outliers and influence the reconstruction result. Robust fitting is necessary to decrease the influence of outliers on the fitting solution, but often comes at the cost of decreased accuracy in the inlier region. Robust techniques often presuppose knowledge of outlier characteristics to build a robust cost function or knowledge of the correct regressed function to filter the outliers. This thesis proposes strategies to obtain the outliers and reconstruction simultaneously without previous knowledge about either. The assumptions are that a statistical shape model that represents the healthy variations of the target organ is available, and that some landmarks on the model reference that annotate locations with correspondence to the target exist. The first strategy uses an EM-like algorithm to obtain the sampling posterior. This is a global reconstruction approach that requires classical noise assumptions on the outlier distribution. The second strategy uses Bayesian optimization to infer the closed-form predictive posterior distribution and estimate a label map of the outliers. The underlying regression model is a Gaussian Process Morphable Model (GPMM). To make the reconstruction obtained through Bayesian optimization robust, a novel acquisition function is proposed. The acquisition function uses the posterior and predictive posterior distributions to avoid choosing outliers as next query points. The algorithms give as outputs a label map and a a posterior distribution that can be used to choose the most likely reconstruction. To obtain the label map, the first strategy uses Bayesian classification to separate inliers and outliers, while the second strategy annotates all query points as inliers and unused model vertices as outliers. The proposed solutions are compared to the literature, evaluated through their sensitivity and breakdown points, and tested on publicly available datasets and in-house clinical examples. The thesis contributes to shape model fitting to pathological targets by showing that: - performing accurate inlier reconstruction and outlier detection is possible without case-specific manual thresholds or input label maps, through the use of outlier detection. - outlier detection makes the algorithms agnostic to pathology type i.e. the algorithms are suitable for both sparse and grouped outliers which appear as holes and bumps, the severity of which influences the results. - using the GPMM-based sequential Bayesian optimization approach, the closed-form predictive posterior distribution can be obtained despite the presence of outliers, because the Gaussian noise assumption is valid for the query points. - using sequential Bayesian optimization instead of traditional optimization for shape model fitting brings forth several advantages that had not been previously explored. Fitting can be driven by different reconstruction goals such as speed, location-dependent accuracy, or robustness. - defining pathologies as outliers opens the door for general pathology segmentation solutions for medical data. Segmentation algorithms do not need to be dependent on imaging modality, target pathology type, or training datasets for pathology labeling. The thesis highlights the importance of outlier-based definitions of pathologies in medical data that are independent of pathology type and imaging modality. Developing such standards would not only simplify the comparison of different pathology segmentation algorithms on unlabeled datsets, but also push forward standard algorithms that are able to deal with general pathologies instead of data-driven definitions of pathologies. This comes with theoretical as well as clinical advantages. Practical applications are shown on shape reconstruction and labeling tasks. Publicly-available challenge datasets are used, one for cranium implant reconstruction, one for kidney tumor detection, and one for liver shape reconstruction. Further clinical applications are shown on in-house examples of a femur and mandible with artifacts and missing parts. The results focus on shape modeling but can be extended in future work to include intensity information and inner volume pathologies

An R library for compositional data analysis in archaeometry

Compositional data naturally arises from the scientific analysis of the chemical composition of archaeological material such as ceramic and glass artefacts. Data of this type can be explored using a variety of techniques, from standard multivariate methods such as principal components analysis and cluster analysis, to methods based upon the use of log-ratios. The general aim is to identify groups of chemically similar artefacts that could potentially be used to answer questions of provenance. This paper will demonstrate work in progress on the development of a documented library of methods, implemented using the statistical package R, for the analysis of compositional data. R is an open source package that makes available very powerful statistical facilities at no cost. We aim to show how, with the aid of statistical software such as R, traditional exploratory multivariate analysis can easily be used alongside, or in combination with, specialist techniques of compositional data analysis. The library has been developed from a core of basic R functionality, together with purpose-written routines arising from our own research (for example that reported at CoDaWork'03). In addition, we have included other appropriate publicly available techniques and libraries that have been implemented in R by other authors. Available functions range from standard multivariate techniques through to various approaches to log-ratio analysis and zero replacement. We also discuss and demonstrate a small selection of relatively new techniques that have hitherto been little-used in archaeometric applications involving compositional data. The application of the library to the analysis of data arising in archaeometry will be demonstrated; results from different analyses will be compared; and the utility of the various methods discussedGeologische Vereinigung; Institut d’Estadística de Catalunya; International Association for Mathematical Geology; Patronat de l’Escola Politècnica Superior de la Universitat de Girona; Fundació privada: Girona, Universitat i Futur; Càtedra Lluís Santaló d’Aplicacions de la Matemàtica; Consell Social de la Universitat de Girona; Ministerio de Ciencia i Tecnología