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dc.contributorAluja Banet, Tomàs
dc.contributor.authorHernández Potiomkin, Yaroslav
dc.contributor.otherUniversitat Politècnica de Catalunya. Departament d'Estadística i Investigació Operativa
dc.date.accessioned2017-02-10T10:40:04Z
dc.date.available2017-02-10T10:40:04Z
dc.date.issued2017-02
dc.identifier.urihttp://hdl.handle.net/2117/100826
dc.description.abstractIn classical model fitting techinques, such as traditional Multiple Linear Regression models (MLR) or Generalized Linear Models (GLM), the assumption is that the individuals come from homogeneous population. However, this condition may be not necessarily met, as there may be many factors that influence the behaviour of the individuals and therefore, biasing the model estimations. For instance, let us consider that we want to study the salaries among a certain set of individuals that come from relatively defined professional sector. The first approach would be to collect all possible modeling variables and fit the model. But it may happen that this could lead us to inaccurate estimations, since the salaries can be driven differently according to gender, region, ethnicity, among others. These variables are called segmentation variables and their number may grow very fast. In this case arises a combinatorial problem giving many possibilities of how to group those individuals. Our main goal in this work, is to go deeper in this kind of problems, and present an automatic solution to detect homogeneous segments among the heterogeneous population in the GLM context. The PATHMOX methodology is a powerful method proposed by Gastón (2009) [19] to automate the task of finding segments. The statistical tests needed to guide the PATHMOX algorithm and discover the constructs that differentiate those segments, are proposed by Lamberti (2015) [8]. First, we provide several solutions to detect heterogeneity, by means of moderating variables as in Covariance Analysis or by means of comparison of coefficients using parametric or non-parametric approaches, in section 2. Additionally, we present the method to characterize classes or continuous response by taking into account only segmentation variables in section 4. Then, we concentrate on the Generalized Linear Modeling context to define the automatic heterogeneity detection method. Then, we accurately present all the needed hypothesis test procedures in section 3. Finally, we also carry out a quite extensive simulation studies and a real problem application in sections 6 and 7, respectively.
dc.language.isoeng
dc.publisherUniversitat Politècnica de Catalunya
dc.subjectÀrees temàtiques de la UPC::Informàtica
dc.subject.lcshLinear models (Statistics)
dc.subject.otherHeterogeneity
dc.subject.otherGeneralized Linear Modeling
dc.subject.otherF-statistic
dc.subject.otherLikelihood Ratio Test
dc.subject.otherPathmox
dc.subject.otherclass characterization
dc.subject.othervaleur-test
dc.subject.othernon-parametric tests
dc.subject.otherAnalysis of Covariance
dc.subject.otherstatistical test
dc.titleDetecting heterogeneity in generalized linear modeling
dc.title.alternativeDetecting heterogeneity in generalized linear modeling and principal component analysis
dc.typeMaster thesis
dc.subject.lemacModels lineals (Estadística)
dc.identifier.slug123241
dc.rights.accessOpen Access
dc.date.updated2017-02-06T05:00:14Z
dc.audience.educationlevelMàster
dc.audience.mediatorFacultat d'Informàtica de Barcelona
dc.audience.degreeMÀSTER UNIVERSITARI EN INNOVACIÓ I RECERCA EN INFORMÀTICA (Pla 2012)


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