8,697 research outputs found
A Model-Based Dimension Reduction Approach to Classification of Gene Expression Data
No full textThe monitoring of the expression profiles of thousands of genes have proved to be particularly promising for biological classification, particularly for cancer diagnosis. However, microarray data present major challenges due to the complex, multiclass nature and the overwhelming number of variables characterizing gene expression profiles. We introduce a methodology that combine dimension reduction method and classification based on finite mixture of Gaussian densities. Information on the dimension reduction subspace is based on the variation of components means for each class, which in turn are obtained by modeling the within class distribution of the predictors through finite mixtures of Gaussian densities. The proposed approach is applied to the leukemia data, a well known dataset in the microarray literature. We show that the combination of dimension reduction and model-based clustering is a powerful technique to find groups among gene expression data
Closed Skew Normal Stochastic frontier Models for Panel data
No full textWe introduce a stochastic frontier model for longitudinal
data where a subject random effect coexists with a time independent
random inefficiency component and with a time dependent random
inefficiency component. The role of the closed skew normal
distribution in this kind of modeling is stressed
Archetypal Symbolic Objects
No full textSymbolic Data Analysis has represented an important
innovation in statistics since its first presentation by
E. Diday in the late 1980s. Most of the interest has
been for the statistical analysis of Symbolic Data that
represent complex data structure where variables can
assume more than just a single value. Thus, Symbolic
Data allow to describe classes of statistical units as a
whole. Furthermore, other entities can be defined in
the realm of Symbolic data. These entities are the
Symbolic objects, defined in terms of the
relationships between two different knowledge levels.
This article aims at introducing a new type of SO
based on the archetypal analysis
Using the Autodependogram in Model Diagnostic Checking
No full textIn this chapter the autodependogram is contextualized in model diagnostic checking for nonlinear models by studying the lag-dependencies of the residuals. Simulations are considered to evaluate its effectiveness in this context. An application to the Swiss Market Index is also provided
The longevity pattern in Emilia Romagna, Italy: a spatio-temporal analysis
No full textIn this paper, we investigate the pattern of longevity in an Italian region at a municipality level in two different periods. Spatio-temporal modeling is used to tackle at both periods the random variations in the occurrence of long-lived individuals, due to the rareness of such events in small areas. This method allows to exploit the spatial proximity smoothing the observed data, as well as to control for the effects of a set of regressors. As a result, clusters of areas characterized by extreme indexes of longevity are well identified and the temporal evolution of the phenomenon can be depicted. A joint analysis of male and female longevity by cohort in the two periods is conducted specifying a set of hierarchical Bayesian models
On Gaussian Compound Poisson Type Limiting Likelihood Ratio Process
No full textDifferent change-point type models encountered in statistical inference for stochastic processes give rise to different limiting likelihood ratio processes. Recently it was established that one of these likelihood ratios, which is an exponential functional of a two-sided Poisson process driven by some parameter, can be approximated (for sufficiently small values of the parameter) by another one, which is an exponential functional of a two-sided Brownian motion. In this chapter we consider yet another likelihood ratio, which is the exponent of a two- sided compound Poisson process driven by some parameter. We establish that the compound Poisson type likelihood ratio can also be approximated by the Brownian type one for sufficiently small values of the parameter. We equally discuss the asymptotics for large values of the parameter
Spectral decomposition of the AR metric
No full textThis work investigates a spectral decomposition of the AR metric proposed as a measure of structural dissimilarity among ARIMA processes. Specifically, the metric will be related to the variance of a stationary process so that its behaviour in the frequency domain will help to detect how unobserved components generated by the parameters of both phenomena concur in specifying the obtained distance. Foundations for the metric are briefly reminded and the main consequences of the proposed decomposition are discussed with special reference to some specific stochastic processes in order to improve the interpretative content of the AR metric
A regionalization method for spatial functional data based on variogram models: an application on environmental data
No full text"\"This paper proposes a Dynamic Clustering Algorithm as a new regionalization. method for spatial functional data. The method looks for the best partition. optimizing a criterion of spatial association among functional data. Furthermore it. is such that a summary of the variability structure of each cluster is discovered. The. performance of the proposal is checked through an application on real data.\"
An iterative procedure for differential analysis of gene expression
No full textMicroarrays are a popular technology to study genes that are differentially expressed between two conditions. In this Note, we propose an iterative procedure to determine the biggest subset of non-differentially expressed genes. We prove a pseudo Markov relationship that allows practical computations. We obtain explicit expressions for FDR and the level of the proposed test at each step. To cite this article: A. Bar-Hen, S. Robin, C. R. Acad. Sci. Paris, Ser. I ••• (••••)
Supplementary Material for: Homogeneity and identity tests for unidimensional Poisson processes for neurophysiological peri-stimulus time histograms
No full textThis file contains the complete code required to reproduce the analysis of Pouzat, Chaffiol and Bar-Hen (2015) "Homogeneity and identity tests for unidimensional Poisson processes for neurophysiological peri-stimulus time histograms" in both R and Python
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