在全球化市場的激烈競爭環境中，資訊科技對企業而言已是一種提升競爭優勢的策略性設備，而先前文獻對於資訊科技能力的評估或與企業績效關聯性的探討，多以行為性問卷的認知數據量表進行研究，少有利用事實性問卷所收集的現象數據評估資訊科技能力及進一步分析資訊科技能力與企業績效關聯性之研究。故本研究基於資源基礎觀點理論，利用企業事實性現象填答問卷建立企業資訊科技能力評估模型，包含資訊科技的導入狀態、應用方式及使用經驗等現象相關問項，如硬體、網路、資訊系統應用程度及範圍等，並探討資訊科技能力與企業績效的關聯性。使用典型相關分析進行實證研究發現，針對先前政府委託調查所收集資料計算出來的企業資訊科技能力，與公開發行的上市櫃企業財務資料所計算出來的企業績效具有顯著關聯性，特別是會計型財務績效之經營能力，經檢定具統計顯著性。進一步進行產業別比較，先使用灰色熵權重分析對於各個子構面進行權重估計，並以權重加權法重新計算每一樣本之資訊科技能力，再進行單因子變異數分析，顯示各產業間之資訊科技能力及子構面能力多數呈現顯著差異。本研究所提出的資訊科技能力評估模型與企業績效關聯檢定模式，以及產業間資訊科技能力差異性分析模式，可提供政府或產業觀察機構建立長期觀測平台，以彙整各種產業資訊科技導入現象及應用範圍，使政府與企業可檢視整體產業整體或個別產業資訊科技能力之差異，藉以擬定資訊科技投資策略，提升企業競爭優勢。 / In the highly competitive globalization environment, information technology (IT) has become strategic equipment for leveraging a business’s competitive advantage. Most previous studies use perceptual questionnaire to collect behavioral data for evaluating IT capability, and furthermore to explore the relationship between IT capability and firm performance. Very few studies use factual questionnaire to collect the phenomenon data for analysis. In this study, we propose a model of evaluating IT capability based on Resource-Based View (RBV) theory and use factual phenomenon questionnaire including induction status, application approach, and usage experience, such as hardware, networks, IS application levels and scopes, etc. The research also explores the relationship between IT capability and firm performance. The IT capability data are calculated from the earlier government-sponsored survey. The firm performance data by financial indicators are collected or calculated from the open data of listed companies in Taiwan Stock Exchange and Over-the-Counter Agencies. The Canonical Correlation Analysis is used and shows significantly positive relationship for the IT capability affecting the firm performance, especially in Accounting-Based Financial Indicators. Before further analysis of industry comparison, Grey Entropy is used to estimate the weights of three sub-constructs and the overall IT capability is then re-calculated by integrating the weighted sub-construct capabilities. Afterwards, the One-Way ANOVA analysis is conducted and shows significant differences across industries in the overall IT capability of the firm and the IT capabilities of the sub-constructs. The proposed IT capability estimation model and the relationship analysis for the IT capability and firm performance can be used by the government or industry observation institution to continuously watch the industry IT capability phenomena and its relationship with the firm performance. The observation for the whole country and across industries can be used as a reference to pursue appropriate IT investments for strategic advantage.

Feature extraction is an extremely important pre-processing step to pattern recognition, and machine learning problems. This thesis highlights how one can best extract features from the data in an exhaustively online and purely adaptive manner. The solution to this problem is given for both labeled and unlabeled datasets, by presenting a number of novel on-line learning approaches. Specifically, the differential equation method for solving the generalized eigenvalue problem is used to derive a number of novel machine learning and feature extraction algorithms. The incremental eigen-solution method is used to derive a novel incremental extension of linear discriminant analysis (LDA). Further the proposed incremental version is combined with extreme learning machine (ELM) in which the ELM is used as a preprocessor before learning. In this first key contribution, the dynamic random expansion characteristic of ELM is combined with the proposed incremental LDA technique, and shown to offer a significant improvement in maximizing the discrimination between points in two different classes, while minimizing the distance within each class, in comparison with other standard state-of-the-art incremental and batch techniques. In the second contribution, the differential equation method for solving the generalized eigenvalue problem is used to derive a novel state-of-the-art purely incremental version of slow feature analysis (SLA) algorithm, termed the generalized eigenvalue based slow feature analysis (GENEIGSFA) technique. Further the time series expansion of echo state network (ESN) and radial basis functions (EBF) are used as a pre-processor before learning. In addition, the higher order derivatives are used as a smoothing constraint in the output signal. Finally, an online extension of the generalized eigenvalue problem, derived from James Stone’s criterion, is tested, evaluated and compared with the standard batch version of the slow feature analysis technique, to demonstrate its comparative effectiveness. In the third contribution, light-weight extensions of the statistical technique known as canonical correlation analysis (CCA) for both twinned and multiple data streams, are derived by using the same existing method of solving the generalized eigenvalue problem. Further the proposed method is enhanced by maximizing the covariance between data streams while simultaneously maximizing the rate of change of variances within each data stream. A recurrent set of connections used by ESN are used as a pre-processor between the inputs and the canonical projections in order to capture shared temporal information in two or more data streams. A solution to the problem of identifying a low dimensional manifold on a high dimensional dataspace is then presented in an incremental and adaptive manner. Finally, an online locally optimized extension of Laplacian Eigenmaps is derived termed the generalized incremental laplacian eigenmaps technique (GENILE). Apart from exploiting the benefit of the incremental nature of the proposed manifold based dimensionality reduction technique, most of the time the projections produced by this method are shown to produce a better classification accuracy in comparison with standard batch versions of these techniques - on both artificial and real datasets.

On suppose que des vecteurs de données de grande dimension arrivant en ligne sont des observations indépendantes d'un vecteur aléatoire. Dans le second chapitre, ce dernier, noté Z, est partitionné en deux vecteurs R et S et les observations sont supposées identiquement distribuées. On définit alors une méthode récursive d'estimation séquentielle des r premiers facteurs de l'ACP projetée de R par rapport à S. On étudie ensuite le cas particulier de l'analyse canonique, puis de l'analyse factorielle discriminante et enfin de l'analyse factorielle des correspondances. Dans chacun de ces cas, on définit plusieurs processus spécifiques à l'analyse envisagée. Dans le troisième chapitre, on suppose que l'espérance En du vecteur aléatoire Zn dont sont issues les observations varie dans le temps. On note Rn = Zn - En et on suppose que les vecteurs Rn forment un échantillon indépendant et identiquement distribué d'un vecteur aléatoire R. On définit plusieurs processus d'approximation stochastique pour estimer des vecteurs directeurs des axes principaux d'une analyse en composantes principales (ACP) partielle de R. On applique ensuite ce résultat au cas particulier de l'analyse canonique généralisée (ACG) partielle après avoir défini un processus d'approximation stochastique de type Robbins-Monro de l'inverse d'une matrice de covariance. Dans le quatrième chapitre, on considère le cas où à la fois l'espérance et la matrice de covariance de Zn varient dans le temps. On donne finalement des résultats de simulation dans le chapitre 5 / High dimensional data are supposed to be independent on-line observations of a random vector. In the second chapter, the latter is denoted by Z and sliced into two random vectors R et S and data are supposed to be identically distributed. A recursive method of sequential estimation of the factors of the projected PCA of R with respect to S is defined. Next, some particular cases are investigated : canonical correlation analysis, canonical discriminant analysis and canonical correspondence analysis ; in each case, several specific methods for the estimation of the factors are proposed. In the third chapter, data are observations of the random vector Zn whose expectation En varies with time. Let Rn = Zn - En be and suppose that the vectors Rn form an independent and identically distributed sample of a random vector R. Stochastic approximation processes are used to estimate on-line direction vectors of the principal axes of a partial principal components analysis (PCA) of ~Z. This is applied next to the particular case of a partial generalized canonical correlation analysis (gCCA) after defining a stochastic approximation process of the Robbins-Monro type to estimate recursively the inverse of a covariance matrix. In the fourth chapter, the case when both expectation and covariance matrix of Zn vary with time n is considered. Finally, simulation results are given in chapter 5

L'imagerie cérébrale connaît un intérêt grandissant, en tant que phénotype intermédiaire, dans la compréhension du chemin complexe qui relie les gènes à un phénotype comportemental ou clinique. Dans ce contexte, un premier objectif est de proposer des méthodes capables d'identifier la part de variabilité génétique qui explique une certaine part de la variabilité observée en neuroimagerie. Les approches univariées classiques ignorent les effets conjoints qui peuvent exister entre plusieurs gènes ou les covariations potentielles entre régions cérébrales.Notre première contribution a été de chercher à améliorer la sensibilité de l'approche univariée en tirant avantage de la nature multivariée des données génétiques, au niveau local. En effet, nous adaptons l'inférence au niveau du cluster en neuroimagerie à des données de polymorphismes d'un seul nucléotide (SNP), en cherchant des clusters 1D de SNPs adjacents associés à un même phénotype d'imagerie. Ensuite, nous prolongeons cette idée et combinons les clusters de voxels avec les clusters de SNPs, en utilisant un test simple au niveau du "cluster 4D", qui détecte conjointement des régions cérébrale et génomique fortement associées. Nous obtenons des résultats préliminaires prometteurs, tant sur données simulées que sur données réelles.Notre deuxième contribution a été d'utiliser des méthodes multivariées exploratoires pour améliorer la puissance de détection des études d'imagerie génétique, en modélisant la nature multivariée potentielle des associations, à plus longue échelle, tant du point de vue de l'imagerie que de la génétique. La régression Partial Least Squares et l'analyse canonique ont été récemment proposées pour l'analyse de données génétiques et transcriptomiques. Nous proposons ici de transposer cette idée à l'analyse de données de génétique et d'imagerie. De plus, nous étudions différentes stratégies de régularisation et de réduction de dimension, combinées avec la PLS ou l'analyse canonique, afin de faire face au phénomène de sur-apprentissage dû aux très grandes dimensions des données. Nous proposons une étude comparative de ces différentes stratégies, sur des données simulées et des données réelles d'IRM fonctionnelle et de SNPs. Le filtrage univarié semble nécessaire. Cependant, c'est la combinaison du filtrage univarié et de la PLS régularisée L1 qui permet de détecter une association généralisable et significative sur les données réelles, ce qui suggère que la découverte d'associations en imagerie génétique nécessite une approche multivariée. / Brain imaging is increasingly recognised as an interesting intermediate phenotype to understand the complex path between genetics and behavioural or clinical phenotypes. In this context, a first goal is to propose methods to identify the part of genetic variability that explains some neuroimaging variability. Classical univariate approaches often ignore the potential joint effects that may exist between genes or the potential covariations between brain regions. Our first contribution is to improve the sensitivity of the univariate approach by taking advantage of the multivariate nature of the genetic data in a local way. Indeed, we adapt cluster-inference techniques from neuroimaging to Single Nucleotide Polymorphism (SNP) data, by looking for 1D clusters of adjacent SNPs associated with the same imaging phenotype. Then, we push further the concept of clusters and we combined voxel clusters and SNP clusters, by using a simple 4D cluster test that detects conjointly brain and genome regions with high associations. We obtain promising preliminary results on both simulated and real datasets .Our second contribution is to investigate exploratory multivariate methods to increase the detection power of imaging genetics studies, by accounting for the potential multivariate nature of the associations, at a longer range, on both the imaging and the genetics sides. Recently, Partial Least Squares (PLS) regression or Canonical Correlation Analysis (CCA) have been proposed to analyse genetic and transcriptomic data. Here, we propose to transpose this idea to the genetics vs. imaging context. Moreover, we investigate the use of different strategies of regularisation and dimension reduction techniques combined with PLS or CCA, to face the overfitting issues due to the very high dimensionality of the data. We propose a comparison study of the different strategies on both a simulated dataset and a real fMRI and SNP dataset. Univariate selection appears to be necessary to reduce the dimensionality. However, the generalisable and significant association uncovered on the real dataset by the two-step approach combining univariate filtering and L1-regularised PLS suggests that discovering meaningful imaging genetics associations calls for a multivariate approach.

Submitted by Valquíria Barbieri (kikibarbi@hotmail.com) on 2018-05-11T20:59:15Z No. of bitstreams: 1 DISS_2015_Karen Janones da Rocha.pdf: 4327569 bytes, checksum: 1207959001ffdfc221d56edf601d488a (MD5) / Approved for entry into archive by Jordan (jordanbiblio@gmail.com) on 2018-05-24T14:50:15Z (GMT) No. of bitstreams: 1 DISS_2015_Karen Janones da Rocha.pdf: 4327569 bytes, checksum: 1207959001ffdfc221d56edf601d488a (MD5) / Made available in DSpace on 2018-05-24T14:50:15Z (GMT). No. of bitstreams: 1 DISS_2015_Karen Janones da Rocha.pdf: 4327569 bytes, checksum: 1207959001ffdfc221d56edf601d488a (MD5) Previous issue date: 2015-02-06 / CAPES / O objetivo geral do presente estudo foi caracterizar um fragmento secundário de Floresta Estacional Semidecidual localizado em Tapurah-MT, quanto a sua estrutura e composição florestal, verificar a formação de grupos florísticos e, ainda, explorar possíveis relações com o ambiente. Aplicou-se o método de área fixa com conglomerados retangulares de dimensões de 10 x 250 m, foram alocados e medidos cinco conglomerados com cinco subunidades cada de 10 X 50 m. Em cada subunidade amostral foi considerada todas as espécies arbóreas e arbustivas com diâmetro à altura do peito (DAP) superior ou igual a 10 cm. A composição florística foi analisada quanto ao número de famílias, gêneros e espécies botânicas encontradas no levantamento do componente arbóreo e a suficiência amostral do levantamento florístico foi verificada pelo procedimento Bootstrap. A similaridade florística entre as subunidades amostrais foi obtida através do Índice de Jaccard e a diversidade de espécies nas subunidades amostrais foi medida pelo Índice de Shannon (H’) e pelo Índice de Equabilidade de Pielou (J’). A caracterização da estrutura horizontal da vegetação foi feita a partir dos parâmetros fitossociológicos e a estrutura diamétrica pelo procedimento de Spiegel. A presença de grupos florísticos foi verificada pelo método de associação das espécies e o número de grupos foi estabelecido pelo coeficiente de concordância de Kendall, onde para cada grupo florístico foi analisada a estrutura horizontal, o padrão de distribuição espacial das espécies pelo índice de Payandeh e a estrutura diamétrica dos indivíduos pelo procedimento de Spiegel. A construção da matriz das variáveis edáficas foi realizada através de uma análise preliminar para identificar variáveis semelhantes entre as subunidades amostrais, as quais não apresentaram influência foram retiradas. A correlação entre os dados de vegetação e dados ambientais foi realizada por meio da Análise de Correlação Canônica, que permitiu confirmar se os nutrientes do solo influenciam na presença das espécies e pela Análise de Redundância Canônica para avaliar quais as variáveis ambientais apresentaram maior influência sobre os indivíduos. No fragmento foi verificada uma alta variabilidade florística e estrutural, que pode ser explicada pelos históricos de perturbação local a que este fragmento foi submetido no passado. De uma forma geral, a vegetação corresponde a de florestas secundárias jovens e apresenta uma comunidade estável e autorregenerativa, além de preservar características da estrutura original. Através da análise de agrupamento foi verificado que as características autoecológicas das espécies assim como os DAP’s médios de cada espécie foram os principais responsáveis pela associação e similaridade entre os grupos. Também foi verificada que apesar das perturbações no ambiente que salientam a saturação do sítio florestal, o fragmento está se recuperando. A heterogeneidade das variáveis edáficas relacionadas influencia no comportamento florístico-estrutural do fragmento secundário de Floresta Estacional Semidecidual. Sendo, as espécies das famílias Fabaceae, Lauraceae, Moraceae e Vochysiaceae as mais influentes para o presente estudo. Destacando a Qualea paraensis Ducke quanto à importância ecológica e a sua adaptabilidade ao ambiente. / The general objective of this study was to characterize a secondary fragment of semideciduous forest located in Tapurah-MT, as its structure and forest composition, verify the formation of floristic groups and also explore possible relationships with the environment. Was applied the fixed area method with five rectangular clusters of 10 x 250 m, they were measured and allocated to five subunits of 10 x 50 m each. In each sample subunit was considered all tree and shrub species with diameter at breast height (DBH) greater than or equal to 10 cm. The floristic composition was analyzed for the number of families, genera and plant species found in the survey of the tree component and sampling sufficiency of floristic survey was tested by bootstrap procedure. The floristic similarity between plots was obtained through the Jaccard index and the diversity of species in the sample subunits was measured by the Shannon Index (H') and equability index of Pielou (J'). The characterization of the horizontal structure of vegetation was made from the phytosociological parameters and the structure diameter by Spiegel procedure. The presence of floristic groups was verified by the association method of species and the number of groups was established by Kendall concordance coefficient, where for each floristic group was analyzed horizontal structure, the pattern of spatial distribution of species by Payandeh index and the diameter distribution of individuals by Spiegel procedure. The construction of the matrix of the soil variables was performed in a preliminary analysis to identify variables similar to the sample subunits, which showed no influence were dropped. The correlation between the data of vegetation and environmental data was performed by Canonical Correlation Analysis, which allowed confirm that soil nutrients influence the presence of the species and the Canonical Redundancy Analysis to evaluate which environmental variables had the greatest influence on the individuals. In the studied fragment was observed high variability floristic and structural, which can be explained by historical local disturbance that this fragment was in the past. In general, the vegetation corresponds to young secondary forests and presents a stable and self-regenerative community, besides preserving the original structure characteristics. Through cluster analysis it was found that the ecological self characteristics of the species as well as the average DBH of each species were mainly responsible for the association and similarity between the groups. We also observed that despite the disturbances in the environment that emphasize the saturation of forest site, the fragment is recovering. The soil variables heterogeneity related influence the floristic-structural behavior of the secondary fragment of semideciduous forest. Being, the species of the families: Fabaceae, Lauraceae, Moraceae and Vochysiaceae the most influential for the present study. Highlighting the Qualea paraensis Ducke about its ecological significance and adaptability to the environment.

<p>A Real-Time Brain-Computer Interface is a technical system classifying increased or decreased brain activity in Real-Time between different body movements, actions performed by a person. Focus in this thesis will be on testing algorithms and settings, finding the initial time interval and how increased activity in the brain can be distinguished and satisfyingly classified. The objective is letting the system give an output somewhere within 250ms of a thought of an action, which will be faster than a persons reaction time. </p><p>Algorithms in the preprocessing were Blind Signal Separation and the Fast Fourier Transform. With different frequency and time interval settings the algorithms were tested on an offline Electroencephalographic data file based on the "Ten Twenty" Electrode Application System, classified using an Artificial Neural Network. </p><p>A satisfying time interval could be found between 125-250ms, but more research is needed to investigate that specific interval. A reduction in frequency resulted in a lack of samples in the sample window preventing the algorithms from working properly. A high frequency is therefore proposed to help keeping the sample window small in the time domain. Blind Signal Separation together with the Fast Fourier Transform had problems finding appropriate correlation using the Ten-Twenty Electrode Application System. Electrodes should be placed more selectively at the parietal lobe, in case of requiring motor responses.</p>

A Real-Time Brain-Computer Interface is a technical system classifying increased or decreased brain activity in Real-Time between different body movements, actions performed by a person. Focus in this thesis will be on testing algorithms and settings, finding the initial time interval and how increased activity in the brain can be distinguished and satisfyingly classified. The objective is letting the system give an output somewhere within 250ms of a thought of an action, which will be faster than a persons reaction time. Algorithms in the preprocessing were Blind Signal Separation and the Fast Fourier Transform. With different frequency and time interval settings the algorithms were tested on an offline Electroencephalographic data file based on the "Ten Twenty" Electrode Application System, classified using an Artificial Neural Network. A satisfying time interval could be found between 125-250ms, but more research is needed to investigate that specific interval. A reduction in frequency resulted in a lack of samples in the sample window preventing the algorithms from working properly. A high frequency is therefore proposed to help keeping the sample window small in the time domain. Blind Signal Separation together with the Fast Fourier Transform had problems finding appropriate correlation using the Ten-Twenty Electrode Application System. Electrodes should be placed more selectively at the parietal lobe, in case of requiring motor responses.

The development of modern information technology has enabled collecting data of unprecedented size and complexity. Examples include web text data, microarray & proteomics, and data from scientific domains (e.g., meteorology). To learn from these high dimensional and complex data, traditional machine learning techniques often suffer from the curse of dimensionality and unaffordable computational cost. However, learning from large-scale high-dimensional data promises big payoffs in text mining, gene analysis, and numerous other consequential tasks. Recently developed sparse learning techniques provide us a suite of tools for understanding and exploring high dimensional data from many areas in science and engineering. By exploring sparsity, we can always learn a parsimonious and compact model which is more interpretable and computationally tractable at application time. When it is known that the underlying model is indeed sparse, sparse learning methods can provide us a more consistent model and much improved prediction performance. However, the existing methods are still insufficient for modeling complex or dynamic structures of the data, such as those evidenced in pathways of genomic data, gene regulatory network, and synonyms in text data. This thesis develops structured sparse learning methods along with scalable optimization algorithms to explore and predict high dimensional data with complex structures. In particular, we address three aspects of structured sparse learning: 1. Efficient and scalable optimization methods with fast convergence guarantees for a wide spectrum of high-dimensional learning tasks, including single or multi-task structured regression, canonical correlation analysis as well as online sparse learning. 2. Learning dynamic structures of different types of undirected graphical models, e.g., conditional Gaussian or conditional forest graphical models. 3. Demonstrating the usefulness of the proposed methods in various applications, e.g., computational genomics and spatial-temporal climatological data. In addition, we also design specialized sparse learning methods for text mining applications, including ranking and latent semantic analysis. In the last part of the thesis, we also present the future direction of the high-dimensional structured sparse learning from both computational and statistical aspects.

Ce mémoire traite de l'acoustique de la clarinette et du rôle de l'anche, résumant des travaux menés entre 2001 et 2018 sur divers sujets :I) Étude de modèles analytiques élémentaire focalisée sur : 1) le rôle des pertes. 2) les cartes itérées, mettant en évidence divers régimes de fonctionnement, utiles aussi pour la pédagogie instrumentale. II) Étude de caractérisation des anches : 1) Étude dynamique des résonances de l'anche réalisée par holographie. Elle conduit à un modèle de matériau viscoélastique expliquant certaines différences observées dans les fréquences des 15 premiers modes de l'anche. 2) Étude statique des caractéristiques mécaniques et aérauliques de l'excitateur (anche+bec+lèvre). La méthode mesure précisément la quantité d'air entrant dans l'instrument en fonction de la pression de lèvre et d'air. III) Synthèse sonore par modèle physique en temps réel : 1) Modélisation mécanique et aéraulique de l'anche, d'après mesure. Le modèle de ressort raidissant non linéaire proposé autorise une simulation dynamique efficace. 2) Estimation modale de l'impédance d'entrée (mesurée) des instruments à vent. On montre les techniques de conception de filtres numériques précis et passifs à toute fréquence. 3) Modélisation et simulation instruments à vent au moyen de guide-ondes, par estimation modale, implémentée dans un logiciel en C++. IV) Une étude de jouabilité d'un panel de 40 anches par analyse canonique des corrélations révèle des liens statistiquement solides entre mesures physiques, évaluations subjectives et synthèse sonore. Elle autorise une caractérisation des anches pouvant être réalisé par le fabricant, selon au moins 4 facteurs indépendants. / This thesis deals with the acoustics of the clarinet and the role of the reed, summarizing studies carried out between 2001 and 2018 on various topics : I) Study of elementary analytical models, focused on 1) role of losses. 2) iterated maps, highlighting various operating regimes, which are also useful for the instrumental pedagogy. II) Reed characterization study : 1) Dynamic study of the reed resonances, by holography. It leads to a model of viscoelastic material explaining some differences observed in the frequencies of the first 15 modes of the reed. 2) Static study of the mechanical and aeraulic characteristics of the exciter (reed + mouthpiece + lip). The method accurately measures the airflow entering the instrument as a function of lip and air pressure. III) Sound synthesis by physical model in real time : 1) Mechanical and aeraulic modeling of the reed, according to measurements. The proposed nonlinear stiffening spring model allows for an efficient dynamic simulation. 2) Modal estimation of the (measured) input impedance of wind instruments. Design techniques for accurate digital filters, passive at any frequency, are described. 3) Modal estimation and simulation of wind instruments by waveguides, implemented in C ++ software. IV) A playability study of a panel of 40 reeds by canonical correlation analysis reveals statistically strong links between physical measurements, subjective evaluations and sound synthesis. It allows a characterization of the reeds that can be made by the manufacturer, according to at least 4 independent factors.

The study was aimed at exploring the relationship between poverty and aggravated robbery in Limpopo Province. Sampled secondary data of aggravated robbery of- fenders, obtained from the South African Police (SAPS), Polokwane, was used in the analysis. From empirical researches on poverty and crime, there are some deductions that vulnerability to crime is increased by poverty. Poverty set was categorised by gender, employment status, marital status, race, age and educational attainment. Variables for aggravated robbery were house robbery, bank robbery, street/common robbery, carjacking, truck hijacking, cash-in-transit and business robbery. Canonical correlation analysis was used to make some inferences about the relationship of these two sets. The results revealed a signi cant positive correlation of 0.219(p-value = 0.025) between poverty and aggravated robbery at ve per cent signi cance level. Of the thirteen variables entered into the poverty-aggravated model, ve emerged as sta- tistically signi cant. These were gender, marital status, employment status, common robbery and business robbery. / Mathematical Sciences / M. Sc. (Statistics)