Asymmetry in the reconstructed deceleration parameter

We study the orientation dependence of the reconstructed deceleration parameter as a function of redshift. We use the Union 2 and Loss datasets, by using the well known preferred axis discussed in the literature, finding the best fit reconstructed deceleration parameter. We found that a low redshift transition of the reconstructed $q(z)$ is clearly absent in one direction and amazingly sharp in the opposite one. We discuss the possibility that such a behavior can be associated with large scale structures affecting the data.Comment: 9 pages, 12 figure

Improved reconstruction of molecular networks with Gaussian graphical models

This doctoral thesis contributes to the advancement of statistical analyses of gene expression profiles using Gaussian graphical models (GGMs). The main focus has been on studying the properties of GGMs obtained with the LW shrinkage. The premise is that, despite its advantages, the shrinkage approach introduces some biases that have not been studied in sufficient detail. These biases have the potential of obscuring the interpretation of the network structure and impeding the validation of earlier analyses. In this sense, this thesis may be considered a continuation of the works of Schäfer and Strimmer, where the LW-shrinkage was originally used to model GRNs with GGMs.The thesis is organized as follows; Chapter 1 presents a test of statistical significance for GGMs based on the LW-shrinkage. Here the probability density of the ‘shrunk’ partial correlation is derived by means of geometric arguments. In Chapter 2 a network analysis of (matched) nasal and bronchial expression profiles is presented. The method developed in the previous chapter is employed to explore whether expression profiles from nasal epithelial cells can be used as a proxy for bronchial epithelial cells. Chapter 3 shows the existence of a non-linear bias on the partial correlations obtained with the LW-shrinkage. This bias is removed via ‘un-shrinking’; a new concept that de-regularizes the partial correlation. In Chapter 4 the LW-shrinkage is revisited from a data-level perspective. The goal is to explore the correspondence between the shrinkage-based covariance matrix and the dataset

The 'un-shrunk' partial correlation in Gaussian graphical models

Abstract Background In systems biology, it is important to reconstruct regulatory networks from quantitative molecular profiles. Gaussian graphical models (GGMs) are one of the most popular methods to this end. A GGM consists of nodes (representing the transcripts, metabolites or proteins) inter-connected by edges (reflecting their partial correlations). Learning the edges from quantitative molecular profiles is statistically challenging, as there are usually fewer samples than nodes (‘high dimensional problem’). Shrinkage methods address this issue by learning a regularized GGM. However, it remains open to study how the shrinkage affects the final result and its interpretation. Results We show that the shrinkage biases the partial correlation in a non-linear way. This bias does not only change the magnitudes of the partial correlations but also affects their order. Furthermore, it makes networks obtained from different experiments incomparable and hinders their biological interpretation. We propose a method, referred to as ‘un-shrinking’ the partial correlation, which corrects for this non-linear bias. Unlike traditional methods, which use a fixed shrinkage value, the new approach provides partial correlations that are closer to the actual (population) values and that are easier to interpret. This is demonstrated on two gene expression datasets from Escherichia coli and Mus musculus. Conclusions GGMs are popular undirected graphical models based on partial correlations. The application of GGMs to reconstruct regulatory networks is commonly performed using shrinkage to overcome the ‘high-dimensional problem’. Besides it advantages, we have identified that the shrinkage introduces a non-linear bias in the partial correlations. Ignoring this type of effects caused by the shrinkage can obscure the interpretation of the network, and impede the validation of earlier reported results

Modelación de Sistema Viable aplicado a la MiPyME para incrementar la productividad. Caso de Estudio en las artes gráficas.

Para que una empresa pueda ser competitiva necesita ser productiva. Las acciones que se llevan a cabo en las organizaciones para mejorar sus índices pueden ser solo de impacto local y no atender resultados sistémicos. El presente estudio sostiene que el incremento de la productividad es el resultado de esfuerzos holísticos que deben estar debidamente estructurados, de forma tal que se manejen adecuadamente sus niveles de complejidad. Tomando como guía estructural el modelo de sistema viable (VSM), se realiza una aplicación a una MiPyME de las artes gráficas para determinar un arquetipo, que se ajuste a sus características particulares, a partir del análisis de la información actual y relevante

La simulación como herramienta para la mejora en el uso de recursos empresariales. Caso pruebas destructivas de calidad.

Creadores del éxito empresarial, los expertos coinciden en que los procesos de simulación son una herramienta que permite promover el desarrollo tecnológico y la sustentabilidad; a través de éstos, se permite fortalecer y generar nuevas capacidades y acciones que mejoran la eficiencia en la utilización de los recursos existentes, con una connotación de sentido social, económico y ambiental. En este marco, se presenta el trabajo que documenta la forma en que se aplica un modelo Montecarlo a las pruebas destructivas con las que se monitorea el nivel de calidad  de la soldadura en una empresa metal mecánica de México, proponiendo ahorros considerables al reducir el nivel y costo del scrap, en beneficio de los grupos de interés, como lo son, trabajadores, accionistas, gobierno y sociedad

TRIBUNAL ARBITRAL DE SCIENTIA CONSULTORES S.A.S. VS. AGROINDUSTRIA UVE S.A.

450

Clasificación con variables discretas observadas con error y con variables continuas

Este artículo considera un problema de clasificación con variables discretas observables con error y con variables continuas. Se da una regla de clasificación por el método Bayesiano y se estiman los parámetros por el método de máxima verosimilitud descrito por N.E. Day (1969) para mezcla de distribuciones nonnales