Bayesian non-linear matching of pairwise microarray gene expressions

In this paper, we present a Bayesian non-linear model to analyze matching pairs of microarray expression data. This model generalizes, in terms of neural networks, standard linear matching models. As a practical application, we analyze data of patients with Acute Lymphoblastic Leukemia and we find out the best neural net model that relates the expression levels of two types of cytogenetically different samples from them

Spatial matching of M configurations of points with a bioinformatics application

In this paper, we present a model to deal with the problem of matching M objects or configurations of points. This is a generalization of the model proposed by Green and Mardia (2006). We consider, as a direct and simple application, the case of three configurations with labelled and with unlabelled points. In both cases, we consider data from a microarray experiment of gorilla, bonobo and human cultured fibroblasts published by Karaman et al. (2003). We find out the matchings and the best affine transformation between the projections of genes in a two dimensional space, obtained by a Multidimensional Scaling technique

Bayesian hierarchical modelling of bacteria growth

Bacterial growth models are commonly used in food safety. Such models permit the prediction of microbial safety and the shelf life of perishable foods. In this paper, we study the problem of modelling bacterial growth when we observe multiple experimental results under identical environmental conditions. We develop a hierarchical version of the Gompertz equation to take into account the possibility of replicated experiments and we show how it can be fitted using a fully Bayesian approach. This approach is illustrated using experimental data from Listeria monocytogenes growth and the results are compared with alternative models. Model selection is undertaken throughout using an appropriate version of the deviance information criterion and the posterior predictive loss criterion. Models are fitted using WinBUGS via R2WinBUGS.Predictive microbiology, Growth models, Gompertz curve, Bayesian hierarchical modelling

Using weibull mixture distributions to model heterogeneous survival data

In this article we use Bayesian methods to fit a Weibull mixture model with an unknown number of components to possibly right censored survival data. This is done using the recently developed, birth-death MCMC algorithm. We also show how to estimate the survivor function and the expected hazard rate from the MCMA output

La Curva de Engel de los Servicios de Salud En Colombia. Una Aproxima-ción Semiparamétrica

Resumen: en este trabajo estamos interesados en estimar curvas de Engel para diferentes rubros de gasto en salud, incluida la salud como un todo. Para llevar a cabo este objetivo, usamos información contenida en la Encuesta de Calidad de Vida de 1997. La técnica usada aquí está basada en modelos semiparamétricos, o Parcialmente Aditivos, combinando estructuras paramétricas y noparamétricas. Adicionalmente, corregimos los problemas de endogeneidad que pueden aparecen y detectamos si son suficientes modelos lineales o cuadráticos. Nuestros resultados indican que el transporte para ir al médico es un bien denominado inferior debido a la forma de la curva estimada. Así mismo la linealidad es fuertemente rechazada en muchos casos, pero el efecto cuadrático (imperfecto) no pudo ser rechazado en el caso del transporte.curva de Engel, gasto en salud, modelos semiparamétricos, endogeneidad, boots-trap, instrumentos

Multiple hypothesis testing and clustering with mixtures of non-central t-distributions applied in microarray data analysis

Multiple testing analysis, based on clustering methodologies, is usually applied in Microarray Data Analysis for comparisons between pair of groups. In this paper, we generalize this methodology to deal with multiple comparisons among more than two groups obtained from microarray expressions of genes. Assuming normal data, we define a statistic which depends on sample means and sample variances, distributed as a non-central t-distribution. As we consider multiple comparisons among groups, a mixture of non-central t-distributions is derived. The estimation of the components of mixtures is obtained via a Bayesian approach, and the model is applied in a multiple comparison problem from a microarray experiment obtained from gorilla, bonobo and human cultured fibroblasts

Viabilidad de comercialización e implementación de los detectores de fugas hidráulicas en los clientes de CDEM & CDEB S.A.

106 páginasEste proyecto consiste en analizar la viabilidad de la comercialización y distribución de detectores de fugas de aceite hidráulico en las compañías industriales que utilicen maquinaria móvil o estacionaria para la empresa Central de Mangueras y Central de Bandas S.A (CDEM & CDEB S.A.). La implementación del proyecto será de gran impacto positivo para el medio ambiente, reducirá el desperdicio de aceite por lo que las empresas se verán beneficiadas económicamente y a su vez mantendrá constante el rendimiento de sus máquinas, además, evitará accidentes laborales por quemaduras o caídas provocadas por una fuga de aceite. La elaboración consiste en alcanzar el objetivo general de la investigación que es “Establecer la viabilidad de los productos para detectar fugas de aceite para la empresa CDEM & CDEB S.A.” por medio de cinco objetivos específicos que se encargan de realizar los siguientes estudios: Comercial, Técnico y Legal, Organizacional, Ambiental y Financiero. La realización de estos estudios dará a conocer la viabilidad del proyecto. Se espera que el proyecto sea totalmente viable en todos los aspectos analizados para así poder proceder adecuadamente con su implementación en la compañía, en caso contrario, se realizaran las respectivas modificaciones logrando así su viabilidad.PregradoIngeniero(a) Administrativo(a

Modelo integral de motores alternativos con aplicaciones docentes: motores diesel de inyección directa

Con fines docentes se ha desarrollado un programa en MATLAB que simula el comportamiento de un MDID utilizando un modelo de combustión de una zona incluyendo renovación de la carga, de fugas y perdidas mecánicas. Para la validación del modelo se han comparado los resultados con los reales de varios motores a pesar de la falta de información detallada del motor. Las mayores diferencias aparecen para régimen de giro alto donde los efectos dinámicos, que este modelo no considera, son importantesWith educational aims a program in MATLAB has been developed that simulates a zone combustion model of compression ignition engine and submodels for the inlet and exhaust processes, for the blow by, the exhaust emissions and the mechanical losses. For the validation of the model the results with the real ones of several engines have been observed. The greater differences appear for high rpm where the dynamics effects, not considered by this model, are more importan

Non-linear models of disability and age applied to census data

It is usually considered that the proportion of handicapped people grows with age. Namely, the older the man/woman is, the more level of disability he/she suffers. However, empirical evidence shows that this assessment is not always true, or at least, it is not true in the Spanish population. This study tries to assess the impact of age on disability in Spain. It is divided into three different parts. The first one is focused in describing the way disability is measured in this work. We used a former index defined by the authors that distinguishes between men and women. The second one is focused in a literature review about the methods used in this paper. This section emphasizes on local regression, feed forward neural networks and BARS. Finally, in the last section estimations are undertaken. Several methods are used and, therefore, there are fairly differences in the results, not only among the methodologies, but also between genders

ABC and Hamiltonian Monte-Carlo methods in COGARCH models

The analysis of financial series, assuming calendar effects and unequally spaced times over continuous time, can be studied by means of COGARCH models based on Lévy processes. In order to estimate the COGARCH model parameters, we propose to use two different Bayesian approaches. First, we suggest to use a Hamiltonian Montecarlo (HMC) algorithm that improves the performance of standard MCMC methods. Secondly, we introduce an Approximate Bayesian Computational (ABC) methodology which allows to work with analytically infeasible or computationally expensive likelihoods. After a simulation and comparison study for both methods, HMC and ABC, we apply them to model the behaviour of some NASDAQ time series and we discuss the results