Resistant estimates for high dimensional and functional data based on random projections

We herein propose a new robust estimation method based on random projections that is adaptive and, automatically produces a robust estimate, while enabling easy computations for high or infinite dimensional data. Under some restricted contamination models, the procedure is robust and attains full efficiency. We tested the method using both simulated and real data.Comment: 24 pages, 6 figure

Interpretable Clustering using Unsupervised Binary Trees

We herein introduce a new method of interpretable clustering that uses unsupervised binary trees. It is a three-stage procedure, the first stage of which entails a series of recursive binary splits to reduce the heterogeneity of the data within the new subsamples. During the second stage (pruning), consideration is given to whether adjacent nodes can be aggregated. Finally, during the third stage (joining), similar clusters are joined together, even if they do not descend from the same node originally. Consistency results are obtained, and the procedure is used on simulated and real data sets.Comment: 25 pages, 6 figure

A nonparametric approach to the estimation of lengths and surface areas

The Minkowski content $L_0(G)$ of a body $G\subset{\mathbb{R}}^d$ represents the boundary length (for $d=2$) or the surface area (for $d=3$) of $G$. A method for estimating $L_0(G)$ is proposed. It relies on a nonparametric estimator based on the information provided by a random sample (taken on a rectangle containing $G$) in which we are able to identify whether every point is inside or outside $G$. Some theoretical properties concerning strong consistency, $L_1$-error and convergence rates are obtained. A practical application to a problem of image analysis in cardiology is discussed in some detail. A brief simulation study is provided.Comment: Published at http://dx.doi.org/10.1214/009053606000001532 in the Annals of Statistics (http://www.imstat.org/aos/) by the Institute of Mathematical Statistics (http://www.imstat.org

Principal components for multivariate functional data

This is the author's version of a work that was accepted for publication in Computational Statistics and Data Analysis. Changes resulting from the publishing process, such as peer review, editing, corrections, structural formatting, and other quality control mechanisms may not be reflected in this document. Changes may have been made to this work since it was submitted for publication. A definitive version was subsequently published in COMPUTATIONAL STATISTICS AND DATA ANALYSIS, Vol 55, Issue 9, (2011) http://dx.doi.org/10.1016/j.csda.2011.03.01

Насінництво

У методичних рекомендаціях наведено загальні відомості щодо визначення посівних якостей насіння деревних порід, послідовність, а також методику виконання окремих лабораторних робіт, зразки документації лісонасіннєвих лабораторій, які оформляються за результатами виконання лабораторних робіт

A Model of TFP

This paper proposes an aggregative model of Total Factor Productivity (TFP) in the spirit of Houthakker (1955-1956). It considers a frictional labor market where production units are subject to idiosyncratic shocks and jobs are created and destroyed as in Mortensen and Pissarides (1994). An aggregate production function is derived by aggregating across production units in equilibrium. The level of TFP is explicitly shown to depend on the underlying distribution of shocks as well as on all the characteristics of the labor market as summarized by the job-destruction decision. The model is also used to study the effects of labor-market policies on the level of measured TFP

Internet Policy Formation in Latin America: Understanding the Links Between the National, the Regional, and the Global

Until recently, internet governance was a relatively obscure topic in most technology policy agendas in Latin America. But in mid-2013, revelations about widespread surveillance of internet communications dramatically transformed conversations about the issue. The work addresses the institutional consolidation of emerging experiences in national contexts to address internet governance and policy as well as their effectiveness in shaping regional and global processes. This paper takes a comparative approach, by looking at several national cases; the experience of Argentine Commission for Internet Policy (CAPI) created in 2014; Costa Rica with the Internet Consulting Committee (in 2012) and Mexico with the Initiative Group (2012). These cases were examined against the backdrop of the well documented Brazilian experience and its Internet Steering Committee (CGI)( 2005). The research analysed the national internet governance mechanisms in the early stages of the institutionalization process, looking at the main developments that have shaped actors’ strategies as well as the evolution of internet regulations in these countries. The three cases differ in both the degree of formality, working mechanisms and stakeholder representation in these new bodies. In each national context, it is clear that governments are now working to formalize policymaking arrangements, as the original informal coordination mechanisms that gave rise to the internet in these countries are no longer sufficient. The bridges between the international and the domestic field will tend to rely on more formally institutionalized spaces as states become more involved with the issue

Multidimensional aspects of welfare : Argentina 1991-2014

Fil: Edo, María. Universidad de San Andrés. Departamento de Economía; Argentina.Sosa Escudero, Walte

River Plate Sport & Pastime

Vol. XVl, Nº 45

The Colombian case : adopting collaborative governance as a path for implementing ethical artificial intelligence

Fil: Muñoz, Victor. Departamento Administrativo de la Presidencia de la República de Colombia; Colombia.Fil: Tamayo, Elena. Transformación Digital e Inteligencia Artificial de la Presidencia de la República de Colombia; Colombia.Fil: Guio, Armando. Universidad de Harvard. Berkman Klein Center; Colombia."Artificial intelligence has permeated most industries from manufacturing, to healthcare, to food, to the creative industries. It has enormous potential to solve global issues we face today, but it also represents considerable risks in terms of discrimination, privacy, bias, inequality, safety, and security. The paper identifies the main risks of AI particularly for the Latin American region: discrimination, threats to civil liberties, and threats to security. This paper presents the challenges that Latin American countries face in the need to address ethical risks of AI while the concrete path for practical implementation of ethical AI remains unclear. Then, this paper analyzes the case of Colombia that has adopted a collaborative governance approach in the path of promoting ethical AI but that needs to deepen its practical implementation of AI. For this, the paper focuses on the ‘Ethical Framework for Artificial Intelligence in Colombia’, whose content and adoption process are both oriented towards the implementation of ethical AI, the first document in Latin America on this subject with a practical approach.