Cette thèse doctorale, réalisée dans le cadre d’un partenariat avec des professionnels de la pêche, a pour objet l’étude de l’impact du réchauffement climatique sur la distribution spatiale des poissons en Atlantique Nord, à l’aide de l’application d’un nouveau modèle d’habitat appelé le Non-Parametric Probabilistic Ecological Niche Model (NPPEN). Le modèle NPPEN est non-paramétrique et basé sur le concept de niche écologique (sensu Hutchinson). Le modèle ne requiert que des données de présence. Il est donc bien adapté à l’étude à macro-échelle de la biogéographie des espèces marines Le modèle NPPEN teste la distance généralisée de Mahalanobis par un test non-paramétrique de permutations afin de produire et de cartographier les probabilités de présence des espèces. L’application de ce nouveau modèle, sur plus de cinquante espèces marines en Atlantique Nord, a mis en évidence l’impact du réchauffement climatique sur la biogéographie des espèces et sur la structure et la trophodynamique de l’écosystème marin. Des bouleversements, déjà observés dans la distribution spatiale et l’abondance (probabilités de présence) d’espèces de poissons, tels la morue de l’Atlantique ou le lançon nordique, ont été retrouvés. En majorité, les espèces vont effectuer un déplacement dirigé vers le nord, pour rester dans un environnement conforme à leur niche écologique. L’intensité et la vitesse des mouvements biogéographiques attendus, de même que le bilan des gains ou pertes d’aires de répartition spatiale diffèrent selon les poissons ; régis par les capacités de déplacements des espèces, leur domaine de tolérance environnementale (largeur de leur niche) et l’intensité du réchauffement climatique / This aims to study the impact of climate warming on the spatial distribution of fish in the North Atlantic, using the new habitat model called the Non-Parametric Probabilistic Ecological Niche Model (NPPEN). The model NPPEN is nonparametric and requires only presence data. It is based on concept of the ecological niche sensu Hutchinson. The model NPPEN tests the Mahalanobis generalised distance by permutations to produce and map the probability of species occurrence. The model is therefore well suited to study expected changes in the biogeography of marine species at macro-scale. Applying this new model on more than fifty marine species in the North Atlantic, has highlighted the impact of global warming on the biogeography of species, structure and trophodynamic of the marine ecosystem. Disruption, already observed in spatial distribution and abundance (probability of occurrence) of fish species such as Atlantic cod and lesser sandeel were found again. The majority of species will move northward to stay in an environment consistent with their ecological niche. The intensity and rapidity of the biogeographic movements expected, as the balance of gains or losses in the spatial range differ among fish; governed by the ability of species movement, their range of environmental tolerance (niche breadth) and the intensity of global warming

La détection d’observations atypiques de manière non-supervisée est un enjeu crucial dans la pratique de la statistique. Dans le domaine de la détection de défauts industriels, cette tâche est d’une importance capitale pour assurer une production de haute qualité. Avec l’accroissement exponentiel du nombre de mesures effectuées sur les composants électroniques, la problématique de la grande dimension se pose lors de la recherche d’anomalies. Pour relever ce challenge, l’entreprise ippon innovation, spécialiste en statistique industrielle et détection d’anomalies, s’est associée au laboratoire de recherche TSE-R en finançant ce travail de thèse. Le premier chapitre commence par présenter le contexte du contrôle de qualité et les différentes procédures déjà mises en place, principalement dans les entreprises de semi-conducteurs pour l’automobile. Comme ces pratiques ne répondent pas aux nouvelles attentes requises par le traitement de données en grande dimension, d’autres solutions doivent être envisagées. La suite du chapitre résume l’ensemble des méthodes multivariées et non supervisées de détection d’observations atypiques existantes, en insistant tout particulièrement sur celles qui gèrent des données en grande dimension. Le Chapitre 2 montre théoriquement que la très connue distance de Mahalanobis n’est pas adaptée à la détection d’anomalies si celles-ci sont contenues dans un sous-espace de petite dimension alors que le nombre de variables est grand.Dans ce contexte, la méthode Invariant Coordinate Selection (ICS) est alors introduite comme une alternative intéressante à la mise en évidence de la structure des données atypiques. Une méthodologie pour sélectionner seulement les composantes d’intérêt est proposée et ses performances sont comparées aux standards habituels sur des simulations ainsi que sur des exemples réels industriels. Cette nouvelle procédure a été mise en oeuvre dans un package R, ICSOutlier, présenté dans le Chapitre 3 ainsi que dans une application R shiny (package ICSShiny) qui rend son utilisation plus simple et plus attractive.Une des conséquences directes de l’augmentation du nombre de dimensions est la singularité des estimateurs de dispersion multivariés, dès que certaines variables sont colinéaires ou que leur nombre excède le nombre d’individus. Or, la définition d’ICS par Tyler et al. (2009) se base sur des estimateurs de dispersion définis positifs. Le Chapitre 4 envisage différentes pistes pour adapter le critère d’ICS et investigue de manière théorique les propriétés de chacune des propositions présentées. La question de l’affine invariance de la méthode est en particulier étudiée. Enfin le dernier chapitre, se consacre à l’algorithme développé pour l’entreprise. Bien que cet algorithme soit confidentiel, le chapitre donne les idées générales et précise les challenges relevés, notamment numériques. / The unsupervised outlier detection is a crucial issue in statistics. More specifically, in the industrial context of fault detection, this task is of great importance for ensuring a high quality production. With the exponential increase in the number of measurements on electronic components, the concern of high dimensional data arises in the identification of outlying observations. The ippon innovation company, an expert in industrial statistics and anomaly detection, wanted to deal with this new situation. So, it collaborated with the TSE-R research laboratory by financing this thesis work. The first chapter presents the quality control context and the different procedures mainly used in the automotive industry of semiconductors. However, these practices do not meet the new expectations required in dealing with high dimensional data, so other solutions need to be considered. The remainder of the chapter summarizes unsupervised multivariate methods for outlier detection, with a particular emphasis on those dealing with high dimensional data. Chapter 2 demonstrates that the well-known Mahalanobis distance presents some difficulties to detect the outlying observations that lie in a smaller subspace while the number of variables is large. In this context, the Invariant Coordinate Selection (ICS) method is introduced as an interesting alternative for highlighting the structure of outlierness. A methodology for selecting only the relevant components is proposed. A simulation study provides a comparison with benchmark methods. The performance of our proposal is also evaluated on real industrial data sets. This new procedure has been implemented in an R package, ICSOutlier, presented in Chapter 3, and in an R shiny application (package ICSShiny) that makes it more user-friendly. When the number of dimensions increases, the multivariate scatter matrices turn out to be singular as soon as some variables are collinear or if their number exceeds the number of individuals. However, in the presentation of ICS by Tyler et al. (2009), the scatter estimators are defined as positive definite matrices. Chapter 4 proposes three different ways for adapting the ICS method to singular scatter matrices and theoretically investigates their properties. The question of affine invariance is analyzed in particular. Finally, the last chapter is dedicated to the algorithm developed for the company. Although the algorithm is confidential, the chapter presents the main ideas and the challenges, mostly numerical, encountered during its development.

I studied translocation of Texas horned lizards on Tinker Air Force Base, Midwest City, Oklahoma, using correlative and mechanistic habitat suitability models. My goals were broadly classified into two categories: first, to determine if the addition of mechanistic data layers (i.e., habitat-niche models) in a correlative model improved the overall accuracy of model predictions, and second, to apply the best model produced from my dataset to a planned translocation event on Tinker Air Force Base. Correlative data layers (i.e., habitat models) included typically applied datasets such as vegetative components, Euclidean distance statistics, neighborhood analyses, and topographically-derived information. Mechanistic data layers were estimates of thermal suitability derived from field-collected datasets and biophysical calculations, and estimates of prey availability taken from interpolated datasets. I estimated habitat suitability using the partitioned Mahalanobis distance statistic, which is a suitable model technique for presence-only data. Translocated and resident lizards were monitored via radiotelemetry and using fluorescent powder trails. Telemetry locations and powder trails were overlaid onto habitat suitability models to provide the datasets used to quantify interaction between site occupancy and habitat model predictions. Lizard paths were tested against random walk models to determine efficiency of travel, and site occupancy metrics (powder track and telemetry Mahalanobis distance values) were tested using parametric (repeated-measures ANOVA) and nonparametric (Wilcoxon rank-sum and signed-rank tests) tests. Mechanistic data layers did not substantially improve model accuracy over correlative-only layers, and data layers taken from mixed bare soil-vegetation, shrub, and grassland habitat types dominated important eigenvector weights. Analyses of fluorescent powder track data suggested that lizards did not move through habitat differently from a random walk model, potentially due to neighborhood factor loadings strongly influencing the area in which entire trails traveled. Wilcoxon tests and repeated-measures ANOVA results suggested that although lizards experienced different median Mahalanobis distance values by group (translocated, resident), there appeared to be an overall decrease in distance scores for translocated individuals over time. In this context, translocated individuals seemed to acclimate their behavior to areas that were predicted to be more suitable by Mahalanobis classifiers. Although survival results were not encouraging and habitat models did not suggest that my translocation site was ideal, my data supports the idea that translocations may be aided in the future by modeling efforts. My models suggest that mechanistic data layers may not improve classification accuracy over correlative processes, but this may be due to inaccurate representation of specific mechanisms over spatial and temporal scales. Future work should focus on including more explicit measures of mechanisms, as well as broadening biotic influences on species distributions (i.e., predator distribution, intra- and interspecific competition).

EMG classification is widely used in electric control of mechanically developed prosthesis, robots development, clinical application etc. It has been evaluated for years, but the main goal of this research is to develop an easy to implement and fast to execute pattern recognition method for classifying signals used for human gait analysis. This method is based on adding two new temporal features (form factor and standard deviation) for EMG signal recognition and using them along with several popular features (area under the curve, wavelength function-pathway and zero crossing rate) to come up with a low complexity suitable feature extraction. Results are presented for EMG data and a comparison with existing methods is made to validate the applicability of the foregoing method. It is shown that the best combination in terms of accuracy and time performance is given by spectral and temporal extraction features along with neural network recognition (NN) algorithm.

Orientador: Bruno Ettore Pavan / Resumo: O Feijão comum (Phaseolus vulgaris L.) é uma das principais fontes alimentares no Brasil, agregado tanto por valores culturais como nutricionais. Seu cultivo pode ser encontrado pequenos e grandes produtores, e em diferentes níveis tecnológicos, e se estende por todo o território Nacional. O potencial produtivo do feijoeiro está em muito ligada a sanidade de plantas, tendo os patógenos como os principais responsáveis pelas quedas em produção. O trabalho objetivou verificar a interação genótipo x ambiente, procedendo com a estratificação ambiental de épocas de semeadura afim de recomendar a melhor época que possibilite a discriminação entre os genótipos e a seleção dos genótipos mais adaptados e estáveis em relação ao ataque de Mancha Angular e Crestamento Bacteriano Comum em cerrado de baixa altitude. Os experimentos foram conduzidos no período de: Junho, Agosto, Outubro e Dezembro de 2015 e Março e Abril de 2016, na Fazenda de Ensino, Pesquisa e Extensão da Faculdade de Engenharia de Ilha Solteira (FEIS), situada no município de Selvíria-MS. O delineamento experimental adotado foi em blocos casualizados, onde foram usados 20 genótipos, sendo 5 deles comerciais, IAC – Una, IAC – Imperador, IAC – Formoso, IAC – Milênio, IAC – Alvorada; e 15 provenientes do programa de melhoramento da FEIS. Para a fonte de variação “ambiente” foram utilizadas as seis épocas de semeadura. Os caracteres avaliados foram: incidência de Crestamento Bacteriano Comum e Mancha Angular. Os parâmetros ge... (Resumo completo, clicar acesso eletrônico abaixo) / Abstract: Common Bean (Phaseolus vulgaris L.) is one of the main food sources in Brazil, aggregated both by cultural and nutritional values. Its cultivation can be found both small and large producers, and at different technological levels, and extends throughout the national territory. The productive potential of the bean plant is closely related to plant health, with pathogens being the main cause of falls in production. The objective of this work was to verify the genotype x environment interaction, proceeding with the environmental stratification of sowing times in order to recommend the best season that allows discrimination between the genotypes and the selection of the most adapted and stable genotypes in relation to the attack of angular spot and blight bacterial in cerrado of low altitude. The experiments were conducted in the period of: June, August, October and December of 2015 and March and April of 2016, in the Fazenda de Ensino, Pesquisa e Extensão da Faculdade de Engenharia de Ilha Solteira (FEIS), located in the municipality of Selvíria-MS . The experimental design was randomized blocks, where 20 genotypes were used, 5 of them commercial, IAC - Una, IAC - Imperador, IAC - Formoso, IAC - Milênio, IAC - Alvorada; and 15 from the FEIS breeding program. For the "environment" variation source, the six sowing times were used. The evaluated characters were: incidence of Bacterial and Angular Spotting. The genetic parameters and variance components were obtained by the REML / B... (Complete abstract click electronic access below) / Mestre

Submitted by João Édino Rossetto null (jerossetto@terra.com.br) on 2018-04-20T13:56:04Z No. of bitstreams: 1 Dissertação_JoaoEdinoRossetto.pdf: 1124751 bytes, checksum: 62ba748ae485faac54f96efa6f360817 (MD5) / Approved for entry into archive by Cristina Alexandra de Godoy null (cristina@adm.feis.unesp.br) on 2018-04-20T14:15:24Z (GMT) No. of bitstreams: 1 rossetto_je_me_ilha.pdf: 1124751 bytes, checksum: 62ba748ae485faac54f96efa6f360817 (MD5) / Made available in DSpace on 2018-04-20T14:15:24Z (GMT). No. of bitstreams: 1 rossetto_je_me_ilha.pdf: 1124751 bytes, checksum: 62ba748ae485faac54f96efa6f360817 (MD5) Previous issue date: 2018-02-22 / Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES) / O Feijão comum (Phaseolus vulgaris L.) é uma das principais fontes alimentares no Brasil, agregado tanto por valores culturais como nutricionais. Seu cultivo pode ser encontrado pequenos e grandes produtores, e em diferentes níveis tecnológicos, e se estende por todo o território Nacional. O potencial produtivo do feijoeiro está em muito ligada a sanidade de plantas, tendo os patógenos como os principais responsáveis pelas quedas em produção. O trabalho objetivou verificar a interação genótipo x ambiente, procedendo com a estratificação ambiental de épocas de semeadura afim de recomendar a melhor época que possibilite a discriminação entre os genótipos e a seleção dos genótipos mais adaptados e estáveis em relação ao ataque de Mancha Angular e Crestamento Bacteriano Comum em cerrado de baixa altitude. Os experimentos foram conduzidos no período de: Junho, Agosto, Outubro e Dezembro de 2015 e Março e Abril de 2016, na Fazenda de Ensino, Pesquisa e Extensão da Faculdade de Engenharia de Ilha Solteira (FEIS), situada no município de Selvíria-MS. O delineamento experimental adotado foi em blocos casualizados, onde foram usados 20 genótipos, sendo 5 deles comerciais, IAC – Una, IAC – Imperador, IAC – Formoso, IAC – Milênio, IAC – Alvorada; e 15 provenientes do programa de melhoramento da FEIS. Para a fonte de variação “ambiente” foram utilizadas as seis épocas de semeadura. Os caracteres avaliados foram: incidência de Crestamento Bacteriano Comum e Mancha Angular. Os parâmetros genéticos e componentes de variância foram obtidos pelo procedimento REML/BLUP. As metodologias MHPRVG (Média Harmônica da Performance Relativa dos Valores Genéticos), AMMI (Additive Main effects and Multiplicative Interaction) e Dendograma baseado na distância de Mahalanobis foram utilizadas para estudo estratificação ambiental, estabilidade e adaptabilidade. Os resultados apontaram que houve interação entre genótipos e ambientes, gerando ambientes favoráveis e não favoráveis a incidência de Mancha Angular e Crestamento Bacteriano Comum. Foi possível detectar os ambientes (épocas) que proporcionaram boa discriminação dos genótipos (A1, junho de 2015 e A6, abril de 2016) e os melhores genótipos para estabilidade e tolerância simultânea (G11 e G5). / Common Bean (Phaseolus vulgaris L.) is one of the main food sources in Brazil, aggregated both by cultural and nutritional values. Its cultivation can be found both small and large producers, and at different technological levels, and extends throughout the national territory. The productive potential of the bean plant is closely related to plant health, with pathogens being the main cause of falls in production. The objective of this work was to verify the genotype x environment interaction, proceeding with the environmental stratification of sowing times in order to recommend the best season that allows discrimination between the genotypes and the selection of the most adapted and stable genotypes in relation to the attack of angular spot and blight bacterial in cerrado of low altitude. The experiments were conducted in the period of: June, August, October and December of 2015 and March and April of 2016, in the Fazenda de Ensino, Pesquisa e Extensão da Faculdade de Engenharia de Ilha Solteira (FEIS), located in the municipality of Selvíria-MS . The experimental design was randomized blocks, where 20 genotypes were used, 5 of them commercial, IAC - Una, IAC - Imperador, IAC - Formoso, IAC - Milênio, IAC - Alvorada; and 15 from the FEIS breeding program. For the "environment" variation source, the six sowing times were used. The evaluated characters were: incidence of Bacterial and Angular Spotting. The genetic parameters and variance components were obtained by the REML / BLUP procedure. The methodologies MHPRVG (Harmonic Mean of Relative Performance of Genetic Values), AMMI (Additive Main effects and Multiplicative Interaction) and Dendogram based on Mahalanobis distance were used to study environmental stratification, stability and adaptability. The results indicated that there was interaction between genotypes and environments, generating favorable environments and not favoring the incidence of Angular Spot and Bacterial Crust. It was possible to detect the environments (seasons) that provided good discrimination of the genotypes (A1, June 2015 and A6, April 2016) and the best genotypes for stability and simultaneous tolerance (G11 and G5).

Submitted by Erika Demachki (erikademachki@gmail.com) on 2014-11-19T18:42:25Z No. of bitstreams: 2 Dissertação - Maria Mônica Domingues Franco Cintra - 2005.pdf: 1037835 bytes, checksum: 80236bdcbef3b93f40c18480bbef4a73 (MD5) license_rdf: 23148 bytes, checksum: 9da0b6dfac957114c6a7714714b86306 (MD5) / Approved for entry into archive by Erika Demachki (erikademachki@gmail.com) on 2014-11-19T18:43:06Z (GMT) No. of bitstreams: 2 Dissertação - Maria Mônica Domingues Franco Cintra - 2005.pdf: 1037835 bytes, checksum: 80236bdcbef3b93f40c18480bbef4a73 (MD5) license_rdf: 23148 bytes, checksum: 9da0b6dfac957114c6a7714714b86306 (MD5) / Made available in DSpace on 2014-11-19T18:43:06Z (GMT). No. of bitstreams: 2 Dissertação - Maria Mônica Domingues Franco Cintra - 2005.pdf: 1037835 bytes, checksum: 80236bdcbef3b93f40c18480bbef4a73 (MD5) license_rdf: 23148 bytes, checksum: 9da0b6dfac957114c6a7714714b86306 (MD5) Previous issue date: 2005-05-20 / Coordenação de Aperfeiçoamento de Pessoal de Nível Superior - CAPES / Two experiments were conducted in the experimental area of the Universidade Federal de Goiás for assessing the genetic divergence among 21 genotypes of turmeric cultivation in two years (EI) and 33 genotypes in cultivation of one year (EII) based on multivariate analyses, order to select divergent genotypes and find out the Mara Rosa producers use the same genotype. The accessions came from Goiás, Minas Gerais and São Paulo. The experiments were conducted in the 2001/2003 period (EI) and 2003/2004 (EII) using a randomized block design with four replications. The assessment of EI was based on production descriptors as total wet weight of rhizomes, dry weight and content of curcumin. The assessment of EII was based on shoot descriptors as tiller number, leaf number, leaf area, height and also production descriptors. Data were subjected to analysis of variance and means were compared by the Scott-Knott test at 1% and 5% probability. We used the Mahalanobis distance as the dissimilarity measure, and for groups delineation the method of Tocher. All analyzes were performed using the program GENES. In the analyzes it can be concluded that Mara Rosa farmers did not use the same genotype and there is variability in the selection of genotypes. The evaluated characteristics are strongly correlated, which justifies the use of measures of dissimilarities using the Mahalanobis distance. Multivariate techniques were effective for the study of genetic diversity and separated the accessions into groups. Methods of estimation of genetic diversity in turmeric accessions were equivalent. Curcumin content and dry weight were descriptors that contributed to divergence in EI. In EII were plants number and tillers number. It was found the increase in curcumin levels of after two years of cultivation. By Mahalanobis distances could be indicated the five most productive genotypes for breeding program and divergence genetic analysis showed the genotypes more indicated for hybridizations. / Conduziu-se dois experimentos na área experimental da Universidade Federal de Goiás para a avaliação da divergência genética entre 21 genótipos de açafrão em cultivo de dois anos (EI) e de 33 genótipos em cultivo de um ano (EII) com base em procedimentos multivariados, visando selecionar genótipos divergentes e mais produtivos e definir se os produtores de Mara Rosa utilizam um mesmo genótipo. Os acessos são de Goiás, Minas Gerais e São Paulo. Os experimentos foram conduzidos no período de 2001/2003 (EI) e 2003/2004 (EII) utilizando delineamento de blocos ao acaso com quatro repetições. A avaliação de EI foi baseada em descritores agronômicos relacionados à produção como: peso fresco total dos rizomas, peso seco, teor de curcumina, entre outros. A avaliação de EII foi baseada em descritores da parte aérea ( número de perfilhos, número de folhas, área foliar, altura média, entre outros) e também nos descritores de produção. Dados obtidos foram submetidos à análise de variância e as médias comparadas pelo teste de Scott-Knott a 1% e 5% de probabilidade. Utilizou-se a distância generalizada de Mahalanobis como medida de dissimilaridade e, na delimitação dos grupos, o método de otimização de Tocher. Todas as análises foram realizadas utilizando o Programa GENES. Nas análises pode-se concluir que agricultores de Mara Rosa não utilizam um mesmo genótipo e há variabilidade para a seleção de genótipos. As características avaliadas são fortemente correlacionadas, o que justifica o uso das medidas de dissimilaridades usando a distância de Mahalanobis. Técnicas multivariadas foram eficientes para o estudo da divergência genética e permitiram a separação dos acessos em grupos. Métodos de estimação da divergência genética em acessos de açafrão, através das distâncias generalizadas de Mahalanobis ou das variáveis canônicas foram equivalentes. Teor de curcumina e peso seco foram os descritores que mais contribuíram para a divergência genética em EI. Em EII foram número de plantas e número de perfilhos. Constatou-se o incremento no teor de curcumina após dois anos de cultivo. Pelas médias e distâncias de Mahalanobis pôde-se indicar os cinco genótipos mais produtivos para o programa de melhoramento e resultados da análise de divergência mostraram os genótipos mais indicados para futuras hibridizações.

Poster exhibited at GPSC Student Showcase, February 24th, 2016, University of Arizona. / Motivation: The conventional approach to personalized medicine relies on molecular data analytics across multiple patients. The path to precision medicine lies with molecular data analytics that can discover interpretable single-subject signals (N-of-1). We developed a global framework, N-of-1-pathways, for a mechanistic-anchored approach to single-subject gene expression data analysis. We previously employed a metric that could prioritize the statistical significance of a deregulated pathway in single subjects, however, it lacked in quantitative interpretability (e.g. the equivalent to a gene expression fold-change). Results: In this study, we extend our previous approach with the application of statistical Mahalanobis distance (MD) to quantify personal pathway-level deregulation. We demonstrate that this approach, N-of-1-pathways Paired Samples MD (N-OF-1-PATHWAYS-MD), detects deregulated pathways (empirical simulations), while not inflating false-positive rate using a study with biological replicates. Finally, we establish that N-OF-1-PATHWAYS-MD scores are, biologically significant, clinically relevant and are predictive of breast cancer survival (P<0.05, n¼80 invasive car- cinoma; TCGA RNA-sequences). Conclusion: N-of-1-pathways MD provides a practical approach towards precision medicine. The method generates the magnitude and the biological significance of personal deregulated pathways results derived solely from the patient’s transcriptome. These pathways offer the opportunities for deriving clinically actionable decisions that have the potential to complement the clinical interpret- ability of personal polymorphisms obtained from DNA acquired or inherited polymorphisms and mutations. In addition, it offers an opportunity for applicability to diseases in which DNA changes may not be relevant, and thus expand the ‘interpretable ‘omics’ of single subjects (e.g. personalome).

Distance is a central concept in international business research, yet there is debate about the construct as well as its operationalization. In this editorial, we address three of the most important recurring questions posed by authors, editors, and reviewers by examining the theory, methods, and data of distance research. We discuss (1) how to theorize on distance, and (2) what method and (3) what data to use when constructing a distance index. We develop practical recommendations grounded in theory, illustrating and supporting them by calculating cross-country distance indices for all available country pairs and two of the most used distance indices: cultural and institutional distance. We show that whereas a specific method to calculate distance may matter to some extent, the choice for a specific cultural or institutional framework to measure cultural or institutional distance has a major impact on country pair distances. Overall, this editorial highlights the importance of matching data and method to the theoretical argument.

In this thesis, we consider the clustering of time series data; specifically, time series that can be modeled in the state space framework. Of primary focus is the pairwise discrepancy between two state space time series. The state space model can be formulated in terms of two equations: the state equation, based on a latent process, and the observation equation. Because the unobserved state process is often of interest, we develop discrepancy measures based on the estimated version of the state process. We compare these measures to discrepancies based on the observed data. In all, seven novel discrepancies are formulated. First, discrepancies derived from Kullback-Leibler (KL) information and Mahalanobis distance (MD) measures are proposed based on the observed data. Next, KL information and MD discrepancies are formulated based on the composite marginal contributions of the smoothed estimates of the unobserved state process. Furthermore, an MD is created based on the joint contributions of the collection of smoothed estimates of the unobserved state process. The cross trajectory distance, a discrepancy heavily influenced by both observed and smoothed data, is proposed as well as a Euclidean distance based on the smoothed state estimates. The performance of these seven novel discrepancies is compared to the often used Euclidean distance based on the observed data, as well as a KL information discrepancy based on the joint contributions of the collection of smoothed state estimates (Bengtsson and Cavanaugh, 2008). We find that those discrepancy measures based on the smoothed estimates of the unobserved state process outperform those discrepancy measures based on the observed data. The best performance was achieved by the discrepancies founded upon the joint contributions of the collection of unobserved states, followed by the discrepancies derived from the marginal contributions. We observed a non-trivial degradation in clustering performance when estimating the parameters of the state space model. To improve estimation, we propose an iterative estimation and clustering routine based on the notion of finding a series' most similar counterparts, pooling them, and estimating a new set of parameters. Under ideal circumstances, we show that the iterative estimation and clustering algorithm can potentially achieve results that approach those obtained in settings where parameters are known. In practice, the algorithm often improves the performance of the model-based clustering measures. We apply our methods to two examples. The first application pertains to the clustering of time course genetic data. We use data from Cho et al. (1998) where a time course experiment of yeast gene expression was performed in order to study the yeast mitotic cell cycle. We attempt to discover the phase to which 219 genes belong. The second application seeks to answer whether or not influenza and pneumonia mortality can be explained geographically. Data from a collection of cities across the U.S. are acquired from the Morbidity and Mortality Weekly Report (MMWR). We cluster the MMWR data without geographic constraints, and compare the results to clusters defined by MMWR geographic regions. We find that influenza and pneumonia mortality cannot be explained by geography.