Please read the abstract in the section front of this document / Dissertation (MSc (Mathematical Statistics))--University of Pretoria, 2009. / Statistics / unrestricted

Statisticians are used to model interactions between variables on the basis of observed<p>data. In a lot of emerging fields, like bioinformatics, they are confronted with datasets<p>having thousands of variables, a lot of noise, non-linear dependencies and, only, tens of<p>samples. The detection of functional relationships, when such uncertainty is contained in<p>data, constitutes a major challenge.<p>Our work focuses on variable selection and network inference from datasets having<p>many variables and few samples (high variable-to-sample ratio), such as microarray data.<p>Variable selection is the topic of machine learning whose objective is to select, among a<p>set of input variables, those that lead to the best predictive model. The application of<p>variable selection methods to gene expression data allows, for example, to improve cancer<p>diagnosis and prognosis by identifying a new molecular signature of the disease. Network<p>inference consists in representing the dependencies between the variables of a dataset by<p>a graph. Hence, when applied to microarray data, network inference can reverse-engineer<p>the transcriptional regulatory network of cell in view of discovering new drug targets to<p>cure diseases.<p>In this work, two original tools are proposed MASSIVE (Matrix of Average Sub-Subset<p>Information for Variable Elimination) a new method of feature selection and MRNET (Minimum<p>Redundancy NETwork), a new algorithm of network inference. Both tools rely on<p>the computation of mutual information, an information-theoretic measure of dependency.<p>More precisely, MASSIVE and MRNET use approximations of the mutual information<p>between a subset of variables and a target variable based on combinations of mutual informations<p>between sub-subsets of variables and the target. The used approximations allow<p>to estimate a series of low variate densities instead of one large multivariate density. Low<p>variate densities are well-suited for dealing with high variable-to-sample ratio datasets,<p>since they are rather cheap in terms of computational cost and they do not require a large<p>amount of samples in order to be estimated accurately. Numerous experimental results<p>show the competitiveness of these new approaches. Finally, our thesis has led to a freely<p>available source code of MASSIVE and an open-source R and Bioconductor package of<p>network inference. / Doctorat en sciences, Spécialisation Informatique / info:eu-repo/semantics/nonPublished

Submitted by Adriana Alves Rodrigues (aalves@espm.br) on 2018-10-09T12:07:43Z No. of bitstreams: 1 MPCC - MARYANNE AKEMI SAKAI.pdf: 1800094 bytes, checksum: 4cb5c8618e0dbdbec0c30d59dc1f6b16 (MD5) / Approved for entry into archive by Adriana Alves Rodrigues (aalves@espm.br) on 2018-10-09T12:08:17Z (GMT) No. of bitstreams: 1 MPCC - MARYANNE AKEMI SAKAI.pdf: 1800094 bytes, checksum: 4cb5c8618e0dbdbec0c30d59dc1f6b16 (MD5) / Approved for entry into archive by Debora Cristina Bonfim Aquarone (deborabonfim@espm.br) on 2018-10-09T12:11:44Z (GMT) No. of bitstreams: 1 MPCC - MARYANNE AKEMI SAKAI.pdf: 1800094 bytes, checksum: 4cb5c8618e0dbdbec0c30d59dc1f6b16 (MD5) / Made available in DSpace on 2018-10-09T12:11:54Z (GMT). No. of bitstreams: 1 MPCC - MARYANNE AKEMI SAKAI.pdf: 1800094 bytes, checksum: 4cb5c8618e0dbdbec0c30d59dc1f6b16 (MD5) Previous issue date: 2018-03-19 / Companies have a wide range of information from their customers, such as their registration and last purchase data. For a service provider in a market like Pet Shops, know your customer and know what the variables are. which most impact on your purchases is of paramount importance. The study gain additional gains when improving the purchasing model through variables consumer attitudes. The RFM analysis (frequency, frequency and monetary value) contributed clients to be grouped according to frequency, frequency and value transaction. The methodology was complemented with semi-structured interviews, 140 respondents, and multivariate linear regression models. The object of the study was the Pet Shop located in São Paulo. Five regression models were used to verify the incremental gains with the incorporation of attitudinal variables. The model that obtained R2 (68.13%) was the one that considered as a response variable the mean value of transaction of the year 2017 and as explanatory variables the quintiles of recency, frequency, of value, in addition to incomplete higher education, pet be considered as a child or as a member family, amount of other pets and cluster of clients that need to be remembered. As As a result of the research, it is noted that the perception of the family vis-a-vis the pet prevailing in the decision to purchase services and products, making variables such as distance from the residence to the Pet Shop or household income become secondary when determining the attitudinal variables that most influence the purchase decision. Understand each client and how it relates to the pet allows you to increase your transaction value because the client seeks above all the welfare of your pet. / As empresas possuem uma grande variedade de informação de seus clientes, como seu cadastro e dados de últimas compras. Para uma empresa prestadora de serviços presente em um mercado pulverizado como o de Pet Shops, conhecer o seu cliente e saber quais são as variáveis que mais impactam em suas compras é de suma importância. O estudo verifica se é possível auferir ganhos adicionais quando se aprimora o modelo de compras por meio de variáveis atitudinais do consumidor. A análise RFM (recência, frequência e valor monetário) contribuiu para que os clientes fossem agrupados conforme os padrões de recência, frequência e valor de transação. A metodologia foi complementada com entrevistas semiestruturadas, survey com 140 respondentes, e modelos de regressão linear multivariada. O objeto do estudo foi a loja de Pet Shop localizada em São Paulo. Foram conduzidos 5 modelos de regressão para se verificar os ganhos incrementais com a incorporação de variáveis atitudinais. O modelo que obteve o R2 mais alto (68,13%) foi aquele que contemplava como variável resposta o valor médio de transação do ano de 2017 e como variáveis explicativas os quintis da recência, da frequência, do valor, além de ensino superior incompleto, pet ser considerado como filho ou como membro da família, quantidade de outros pets e cluster de clientes que precisam ser lembrados. Como resultado da pesquisa, nota-se que a percepção da família frente ao pet exerce papel preponderante na decisão de compra de serviços e produtos, fazendo com que variáveis como distância da residência ao Pet Shop ou renda domiciliar tornem-se secundárias ao se determinar as variáveis atitudinais que mais influenciam na decisão de compra. Entender cada cliente e como ele se relaciona com o pet permite aumentar o seu valor de transação, pois o cliente busca acima de tudo o bem-estar de seu pet.