Innovative and Additive Outlier Robust Kalman Filtering with a Robust Particle Filter

In this paper, we propose CE-BASS, a particle mixture Kalman filter which is robust to both innovative and additive outliers, and able to fully capture multi-modality in the distribution of the hidden state. Furthermore, the particle sampling approach re-samples past states, which enables CE-BASS to handle innovative outliers which are not immediately visible in the observations, such as trend changes. The filter is computationally efficient as we derive new, accurate approximations to the optimal proposal distributions for the particles. The proposed algorithm is shown to compare well with existing approaches and is applied to both machine temperature and server data

Green automotive supply chain for an emerging market

Thesis (M. Eng. in Logistics)--Massachusetts Institute of Technology, Engineering Systems Division, 2008.Includes bibliographical references (leaves 94-97).Green Supply Chain Management (GSCM) within the automotive industry is largely based on combining lean manufacturing with mandated supplier adoption of ISO 14001-compliant Environmental Management Systems (EMS). This approach evolved from automotive manufacturers seeking to expediently expand green practices within existing lean supply chains. However, a new automotive enterprise, without the legacy issues of an existing supply chain, has the opportunity to customize its supply chain from scratch, to comprehensively achieve both financial and green objectives. This thesis investigated a more holistic approach to creating a financially-viable green automotive supply chain for the MIT Vehicle Design Summit (VDS) - a start-up enterprise planning to enter the Indian emerging market with a new type of eco-friendly automobile. First, a hypothetical VDS supply chain was postulated by analyzing the contextual challenges of the Indian emerging economy, so as to optimize the location, supplier selection and manufacturing models within its business context. To ensure that the capital investments needed to fulfill the supply chain's green objectives do not compromise its primary purpose of value creation, a Triple Bottom Line technique called Environmental Cost Accounting was used as a managerial decision tool, which demonstrated the financial viability of GSCM for VDS. Next, green solutions for each supply chain function were identified for integration into the hypothetical supply chain. It was found that many important green solutions for an automotive supply chain like supplier selection, concurrent engineering, cascading of lean production best practices to the extended supply chain, fuel-efficient transport practices and green infrastructure design, have already been developed by various governmental and non-governmental agencies.(cont.) Also, product recovery through End-of-Life Vehicle (ELV) processing was identified as a vital green supply chain function required for closing the loop between sales and sourcing. The key issue was integrating these disparate solutions into a holistic environmental management framework for VDS to implement and sustain. This was accomplished using an IS014001-based EMS as the master plan. The developed EMS Manual is a pioneering document that leverages chain-wide participation in existing green initiatives like the Green Suppliers Network, SmartWay Transport Partnership and LEED Green Building Rating, to realize a green supply chain by ensuring continuous monitoring and improvement of the implemented initiatives.by Gene Fisch, Jr. [and] Tien Song Paul Neo.M.Eng.in Logistic

Institutional investors and post-ICO performance: an empirical analysis of investor returns in initial coin offerings (ICOs)

We examine the role of institutional investors in initial coin offerings (ICOs). Taking a financial investor's perspective, we assess the determinants of post-ICO performance via buy-and-hold abnormal returns (BHAR) in a sample of 565 ICO ventures. Conceptually, we argue that institutional investors' superior screening (selection effect) and coaching abilities (treatment effect) enable them to partly overcome the information asymmetry of the ICO context and extract informational rents from their ICO investments. We find that institutional investor backing is indeed associated with higher post-ICO performance. Disentangling selection and treatment effects econometrically, we find that both of these effects explain the positive impact institutional investors have on post-ICO performance. Overall, our results highlight the importance of institutional investors in the ICO context

anomaly : Detection of Anomalous Structure in Time Series Data

One of the contemporary challenges in anomaly detection is the ability to detect, and differentiate between, both point and collective anomalies within a data sequence or time series. The anomaly package has been developed to provide users with a choice of anomaly detection methods and, in particular, provides an implementation of the recently proposed Collective And Point Anomaly family of anomaly detection algorithms. This article describes the methods implemented whilst also highlighting their application to simulated data as well as real data examples contained in the package.Comment: 24 pages, 6 figures. An R package that implements the methods discussed in the paper can be obtained from The Comprehensive R Archive Network (CRAN) via https://cran.r-project.org/web/packages/anomaly/index.htm

Subset Multivariate Collective And Point Anomaly Detection

In recent years, there has been a growing interest in identifying anomalous structure within multivariate data sequences. We consider the problem of detecting collective anomalies, corresponding to intervals where one, or more, of the data sequences behaves anomalously. We first develop a test for a single collective anomaly that has power to simultaneously detect anomalies that are either rare, that is affecting few data sequences, or common. We then show how to detect multiple anomalies in a way that is computationally efficient but avoids the approximations inherent in binary segmentation-like approaches. This approach is shown to consistently estimate the number and location of the collective anomalies -- a property that has not previously been shown for competing methods. Our approach can be made robust to point anomalies and can allow for the anomalies to be imperfectly aligned. We show the practical usefulness of allowing for imperfect alignments through a resulting increase in power to detect regions of copy number variation

Molecular detection of human T-lymphotropic virus type 1 infection among oncology patients in Germany: A retrospective view

Human T-cell lymphotropic virus (HTLV) belongs to a larger group of primate T-cell lymphotropic viruses (PTLVs) within the family Retroviridae. It is estimated that 10 to 20 million people worldwide may be infected with HTLV-1. Although most of them are asymptomatic, around 5% of infected individuals may develop either HTLV-1 Associated Myelopathy/Tropical Spastic Paraparesis (HAM/TSP) or Adult T-cell Leukaemia/Lymphoma (ATLL). Public Health authorities in many countries have implemented routine blood-donor tests for HTLVspecific antibodies; but this is not the case for Germany since the reported prevalence is very low (7/100,000). With the aim to evaluate retrospectively the presence of HTLV-1 among oncology patients in this country, samples stored at the Universitä tsklinikum Freiburg, were analyzed. For this purpose, two different nested-PCR (n-PCR) protocols have been modified and set up for HTLV-1 detection. One positive case was detected by n-PCR among 406 samples (0,25%) in a period of 5 years (2008-2012) corresponding to a T-Cell Lymphoma. Despite the low prevalence, this virus is circulating in Germany, probably due to the increasing numbers of immigrants in these last years. Physicians should consider HTLV-1 infection and suspect it taking in account the ethnic and relation to endemic regions regardless the patient´s residence.Fil: Ruggieri, Matias. Albert Ludwigs University of Freiburg; Alemania. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Houssay. Instituto de Investigaciones Biomédicas en Retrovirus y Sida. Universidad de Buenos Aires. Facultad de Medicina. Instituto de Investigaciones Biomédicas en Retrovirus y Sida; ArgentinaFil: Berini, Carolina Andrea. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Houssay. Instituto de Investigaciones Biomédicas en Retrovirus y Sida. Universidad de Buenos Aires. Facultad de Medicina. Instituto de Investigaciones Biomédicas en Retrovirus y Sida; ArgentinaFil: Ducasa, Nicolás. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Houssay. Instituto de Investigaciones Biomédicas en Retrovirus y Sida. Universidad de Buenos Aires. Facultad de Medicina. Instituto de Investigaciones Biomédicas en Retrovirus y Sida; ArgentinaFil: Malkovsky, Miroslav. University of Wisconsin; Estados UnidosFil: Fisch, Paul. Albert Ludwigs University of Freiburg; AlemaniaFil: Biglione, Mirna Marcela. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Houssay. Instituto de Investigaciones Biomédicas en Retrovirus y Sida. Universidad de Buenos Aires. Facultad de Medicina. Instituto de Investigaciones Biomédicas en Retrovirus y Sida; Argentin

Institutional investors and post-ICO performance: an empirical analysis of investor returns in initial coin offerings (ICOs)

We examine the role of institutional investors in initial coin offerings (ICOs). Taking a financial investor's perspective, we assess the determinants of post-ICO performance via buy-and-hold abnormal returns (BHAR) in a sample of 565 ICO ventures. Conceptually, we argue that institutional investors' superior screening (selection effect) and coaching abilities (treatment effect) enable them to partly overcome the information asymmetry of the ICO context and extract informational rents from their ICO investments. We find that institutional investor backing is indeed associated with higher post-ICO performance. Disentangling selection and treatment effects econometrically, we find that both of these effects explain the positive impact institutional investors have on post-ICO performance. Overall, our results highlight the importance of institutional investors in the ICO context

The CEO beauty premium: Founder CEO attractiveness and firm valuation in initial coin offerings

We apply insights from research in social psychology and labor economics to the domain of entrepreneurial finance and investigate how founder chief executive officers' (founder CEOs') facial attractiveness influences firm valuation. Leveraging the novel context of initial coin offerings (ICOs), we document a pronounced founder CEO beauty premium, with a positive relationship between founder CEO attractiveness and firm valuation. We find only very limited evidence of stereotype-based evaluations, through the association of founder CEO attractiveness with latent traits such as competence, intelligence, likeability, or trustworthiness. Rather, attractiveness seems to bear economic value per se, especially in a context in which investors base their decisions on a limited information set. Indeed, attractiveness has a sustainable effect on post-ICO performance

A linear time method for the detection of point and collective anomalies

The challenge of efficiently identifying anomalies in data sequences is an important statistical problem that now arises in many applications. Whilst there has been substantial work aimed at making statistical analyses robust to outliers, or point anomalies, there has been much less work on detecting anomalous segments, or collective anomalies. By bringing together ideas from changepoint detection and robust statistics, we introduce Collective And Point Anomalies (CAPA), a computationally efficient approach that is suitable when collective anomalies are characterised by either a change in mean, variance, or both, and distinguishes them from point anomalies. Theoretical results establish the consistency of CAPA at detecting collective anomalies and empirical results show that CAPA has close to linear computational cost as well as being more accurate at detecting and locating collective anomalies than other approaches. We demonstrate the utility of CAPA through its ability to detect exoplanets from light curve data from the Kepler telescope