Consumption of macronutrients, fiber, ethanol and main food groups.

<p>Consumption of macronutrients, fiber, ethanol and main food groups.</p

Multivariate linear regression analysis of the bacterial groups significantly different between active and sedentary women as dependent variants, and diet composition, fecal water, body composition parameters and physical activity parameters as independent variables.

<p>Multivariate linear regression analysis of the bacterial groups significantly different between active and sedentary women as dependent variants, and diet composition, fecal water, body composition parameters and physical activity parameters as independent variables.</p

Fat and muscle body composition parameters.

<p>Fat and muscle body composition parameters.</p

Changes induced in relative abundance of gut bacteria species by lifestyle in women.

<p>(A) <i>Bifidobacterium longum</i>, <i>(</i>B) <i>Faecalibacterium prausnitzii</i>, (C) <i>Roseburia hominis</i>, (D) <i>Akkermansia muciniphila</i>. Data were log transformed and analyzed by t-test * p<0.05 **p<0.01.</p

Bacterial phyla and families in feces of active and sedentary women.

<p>Bacterial phyla and families in feces of active and sedentary women.</p

Spearman correlation of microbiota genera, body composition and physical activity parameters.

<p>Spearman correlation of microbiota genera, body composition and physical activity parameters.</p

Differences in gut microbiota profile between women with active lifestyle and sedentary women - Fig 1

<p><b>Principal Coordinates Analysis (PcoA) plots of unweighted (A) and weighted (B) Unifrac distance metrics obtained from sequencing the 16s rRNA gene in fecal samples</b>. Axes represent percentage of data explained by each coordinate dimension.</p

Analysis of dynamic common factors in the presence of autocorrelated noise-processes

This thesis presents a procedure to build a dynamic factor model in the presence of orthogonal stationary noise-processes. The procedure is based on the Peña-Box model (Peña & Box, 1987), in which the number of observed time series is fixed, and in the extension proposed by Peña & Poncela (2006) to non-stationary common factors, in which the common factors may be integrated processes. As a first result, an alternative for detecting the number of common factors is proposed by extending the statistical test of Peña & Poncela (2006), proposed for the Peña-Box model with a white noise process. Furthermore, in the same context, a statistical test is proposed to identify the number of non-stationary common factors. These proposals are illustrated by simulation and an application with real data, in which some empirical findings related to seasonal factors are also presented. The model is estimated by maximum likelihood, via a state-space model.Esta tesis presenta un procedimiento para construir un modelo de factores comunes dinámicos en presencia de procesos de ruido estacionarios ortogonales. El procedimiento se basa en el modelo de Peña-Box (Peña & Box, 1987), en el cual el número de series de tiempo observadas es fijo, y en la extensión propuesta por Peña & Poncela (2006) a factores comunes no estacionarios, en la cual los factores comunes pueden ser procesos integrados. Como primer resultado, se propone una alternativa para la identificación del número de factores comunes extendiendo la prueba estadística de Peña & Poncela (2006) , propuesta para el modelo Peña-Box con proceso de ruido blanco. Adicionalmente, bajo el mismo contexto, se propone una prueba estadística para identificar el número de factores comunes no estacionarios. Estas propuestas son ilustradas mediante simulación y una aplicación con datos reales, en la cual también se presentan algunos hallazgos empíricos relacionados a factores estacionales. La estimación del modelo se realiza por máxima verosimilitud, vía un modelo espacio de estados.Línea de investigación: Series de TiempoDoctorad