Análisis de la susceptibilidad de riesgo de inundación del río Tajuña a su paso por Morata de Tajuña, Perales de Tajuña y Valdilecha.

El presente estudio se basa en el análisis de la susceptibilidad del riesgo de inundación del río Tajuña en los municipios de Morata de Tajuña, Perales de Tajuña y Valdilecha (Comunidad de Madrid) a través de las Tecnologías de la Información Geográfica (TIG), y consta de tres fases diferenciadas: modelización hidráulica y representación de resultados, cálculo de la superficie afectada y clasificación de la misma según el Corine Land Cover y análisis de imágenes satélite de los ámbitos estudiados como refuerzo a los resultados obtenidos en las fases anteriores. Los resultados muestran no solo la superficie afectada en cada municipio y su clasificación, sino también la localización de edificaciones externas a los usos urbanos, que permite valorar como poco adecuada la ordenación territorial de los tres municipios al no tener en cuenta lo establecido en las últimas modificaciones del Reglamento de Dominio Público Hidráulico relacionadas con la prevención de riesgos de inundación

Statistical Analysis of Solar Neutrino Data

We calculate with Monte Carlo the goodness of fit and the confidence level of the standard allowed regions for the neutrino oscillation parameters obtained from the fit of the total rates measured in solar neutrino experiments. We show that they are significantly overestimated in the standard method. We also calculate exact allowed regions with correct frequentist coverage. We show that the exact VO, LMA and LOW regions are much larger than the standard ones and merge together giving an allowed band at large mixing angles for all Delta m^2 > 10^{-10} eV^2.Comment: 4 pages. Talk presented by C. Giunti at NOW 2000, Conca Specchiulla (Otranto, Italy), 9-16 Sep. 200

Statistical topological data analysis using persistence landscapes

We define a new topological summary for data that we call the persistence landscape. Since this summary lies in a vector space, it is easy to combine with tools from statistics and machine learning, in contrast to the standard topological summaries. Viewed as a random variable with values in a Banach space, this summary obeys a strong law of large numbers and a central limit theorem. We show how a number of standard statistical tests can be used for statistical inference using this summary. We also prove that this summary is stable and that it can be used to provide lower bounds for the bottleneck and Wasserstein distances.Comment: 26 pages, final version, to appear in Journal of Machine Learning Research, includes two additional examples not in the journal version: random geometric complexes and Erdos-Renyi random clique complexe

GNA: new framework for statistical data analysis

We report on the status of GNA --- a new framework for fitting large-scale physical models. GNA utilizes the data flow concept within which a model is represented by a directed acyclic graph. Each node is an operation on an array (matrix multiplication, derivative or cross section calculation, etc). The framework enables the user to create flexible and efficient large-scale lazily evaluated models, handle large numbers of parameters, propagate parameters' uncertainties while taking into account possible correlations between them, fit models, and perform statistical analysis. The main goal of the paper is to give an overview of the main concepts and methods as well as reasons behind their design. Detailed technical information is to be published in further works.Comment: 9 pages, 3 figures, CHEP 2018, submitted to EPJ Web of Conference

Statistical analysis of SSME system data

A statistical methodology to enhance the Space Shuttle Main Engine (SSME) performance prediction accuracy is proposed. This methodology was to be used in conjunction with existing SSME performance prediction computer codes to improve parameter prediction accuracy and to quantify that accuracy. However, after a review of related literature, researchers concluded that the proposed problem required a coverage of areas such as linear and nonlinear system theory, measurement theory, statistics, and stochastic estimation. Since state space theory is the foundation for a more complete study of each of the before mentioned areas, these researchers chose to refocus emphasis to cover the more specialized topic of state vector estimation procedures. State vector estimation was also selected because of current and future concerns by NASA for SSME performance evaluation; i.e., there is a current interest in an improved evaluation procedure for actual SSME post flight performance as well as for post static test performance of a single SSME. A current investigation of analytical methods may be used to improve test stand failure detection. This paper considers the issue of post flight/test state variable reconstruction through the application of observations made on the output of the Space Shuttle propulsion system. Rogers used the Kalman filtering procedure to reconstruct the state variables of the Space Shuttle propulsion system. An objective of this paper is to give the general setup of the Kalman filter and its connection to linear regression. A second objective is to examine the reconstruction methodology for application to the reconstruction of the state vector of a single Space Shuttle Main Engine (SSME) by using static test firing data

Statistical methods of SNP data analysis with applications

Various statistical methods important for genetic analysis are considered and developed. Namely, we concentrate on the multifactor dimensionality reduction, logic regression, random forests and stochastic gradient boosting. These methods and their new modifications, e.g., the MDR method with "independent rule", are used to study the risk of complex diseases such as cardiovascular ones. The roles of certain combinations of single nucleotide polymorphisms and external risk factors are examined. To perform the data analysis concerning the ischemic heart disease and myocardial infarction the supercomputer SKIF "Chebyshev" of the Lomonosov Moscow State University was employed