Nonparametric Estimation of Random Effect Variance with Partial Information from the Clusters

This work is a new proposal for estimating the variance of the random effects in case the knowledge of the internal variability of the clusters is (or might be) assumed to be known. Here by clusters we mean, for instance, second-level units in multi-level models (schools, hospitals etc.), or subjects in repeated measure experiments. The proposed approach is useful whenever the variability of the response in a linear model can be viewed as the sum of two independent sources of variability, one that is common to all clusters and it is unknown, and another which is assumed to be available and it is clusterspecific. The responses here have to be thought as functions of the first-level observations, whose variability is known to depend only on the cluster's specificities. These settings include linear mixed models (LMM) when the estimators of the effects of interest are obtained conditionally on each cluster. The model may account for additional informations on the clusters, such as covariates, or contrast vectors. An estimator of the common source of variability is obtained from the residual deviance of the model, opportunely re-scaled, through the moment method. An iterative procedure is then suggested (whose initial step depends on the available information), that turns out to be a special case of the EM-algorithm

Nonparametric Estimation of the Random Effect Variance-Covariance Matrix with Partial Information from the Clusters

The proposed approach is useful whenever the variability of the response in a linear model can be viewed as the sum of two independent sources of variability, one that is common to all clusters and it is unknown, and another which is assumed to be available and it is cluster-specific. Here by clusters we mean, for instance, secondlevel units in multi-level models (schools, hospitals etc.), or subjects in repeated measure experiments. The responses here have to be thought as functions of the first-level observations, whose variability is known to depend only on the cluster’s specificities. These settings include linear mixed models (LMM) when the stimators of the parameters of interest are obtained conditionally on each cluster. The model may account for additional informations on the clusters, such as covariates, or contrast vectors. An estimator of the common source of variability is obtained from the residual deviance of the (multivariate) model, opportunely re-scaled, through the moment method. An iterative procedure is then suggested (whose initial step depends on the available information), that turns out to be a special case of the EM-algorithm

Exact Multivariate Permutation Tests for Fixed Effects in Mixed-Models

A test for the fixed effect in mixed-models is proposed. It is based on permutation strategy and is exact. The testing approach presented is very general and the class of model covered is very broad. Multivariate responses with different type of variables (e.g. continuous, categorical and ranks) are usually tested with separated models and the overall test are usually reached trough Bonferroni-like combinations, i.e. without taking in account the joint distribution of the tests statistics. On the contrary in this approach the joint distribution is immediately obtained and the dependence among tests is taken in account in the overall test

A locally adaptive statistical procedure (LAP) to identify differentially expressed chromosomal regions

Abstract Motivation: The systematic integration of expression profiles and other types of gene information, such as chromosomal localization, ontological annotations and sequence characteristics, still represents a challenge in the gene expression arena. In particular, the analysis of transcriptional data in context of the physical location of genes in a genome appears promising in detecting chromosomal regions with transcriptional imbalances often characterizing cancer. Results: A computational tool named locally adaptive statistical procedure (LAP), which incorporates transcriptional data and structural information for the identification of differentially expressed chromosomal regions, is described. LAP accounts for variations in the distance between genes and in gene density by smoothing standard statistics on gene position before testing the significance of their differential levels of gene expression. The procedure smoothes parameters and computes p-values locally to account for the complex structure of the genome and to more precisely estimate the differential expression of chromosomal regions. The application of LAP to three independent sets of raw expression data allowed identifying differentially expressed regions that are directly involved in known chromosomal aberrations characteristic of tumors. Availability: Functions in R for implementing the LAP method are available at Contact: [email protected] Supplementary Information

Comportamiento de métricas para proyectos de explotación de información en PyMEs

En este trabajo se presenta un estudio del comportamiento de las métricas propuestas para proyectos de explotación de información, la cual considera características y parámetros identificados para proyectos pequeños, de aplicación en las empresas PyMEs. Para ello, se realiza una introducción sobre la categorización definida para las métricas consideradas y el modelo de proceso de desarrollo utilizado como referencia, se delimita el problema presentando el diseño experimental y los resultados obtenidos para finalizar con la puntualización de algunas conclusiones.XII Workshop Ingeniería de Software (WIS)Red de Universidades con Carreras en Informática (RedUNCI

Propuesta de métricas para proyectos de explotación de información

Los proyectos de explotación de información requieren de un proceso de planificación que permita estimar sus tiempos y medir el avance del producto en cada etapa de su desarrollo y calidad del mismo. Las métricas usuales para realizar una estimación no se consideran adecuadas ya que los parámetros a ser utilizados son de naturaleza diferentes y no se ajustan a sus características particulares. En este contexto, se plantea una propuesta de métricas aplicables al proceso de desarrollo de Proyectos de Explotación de Información, siguiendo los lineamientos del Modelo de Procesos para Proyectos de Explotación de Información para PyMEs.X Workshop bases de datos y minería de datosRed de Universidades con Carreras en Informática (RedUNCI

Comportamiento de métricas para proyectos de explotación de información en PyMEs

En este trabajo se presenta un estudio del comportamiento de las métricas propuestas para proyectos de explotación de información, la cual considera características y parámetros identificados para proyectos pequeños, de aplicación en las empresas PyMEs. Para ello, se realiza una introducción sobre la categorización definida para las métricas consideradas y el modelo de proceso de desarrollo utilizado como referencia, se delimita el problema presentando el diseño experimental y los resultados obtenidos para finalizar con la puntualización de algunas conclusiones.XII Workshop Ingeniería de Software (WIS)Red de Universidades con Carreras en Informática (RedUNCI

Comportamiento de métricas para proyectos de explotación de información en PyMEs

En este trabajo se presenta un estudio del comportamiento de las métricas propuestas para proyectos de explotación de información, la cual considera características y parámetros identificados para proyectos pequeños, de aplicación en las empresas PyMEs. Para ello, se realiza una introducción sobre la categorización definida para las métricas consideradas y el modelo de proceso de desarrollo utilizado como referencia, se delimita el problema presentando el diseño experimental y los resultados obtenidos para finalizar con la puntualización de algunas conclusiones.XII Workshop Ingeniería de Software (WIS)Red de Universidades con Carreras en Informática (RedUNCI