Disertacijoje nagrinėjami Markovo grandinės Monte-Karlo (MCMC) adaptavimo metodai, skirti efektyviems skaitiniams duomenų analizės sprendimų priėmimo su iš anksto nustatytu patikimumu algoritmams sudaryti. Suformuluoti ir išspręsti hierarchiniu būdu sudarytų daugiamačių skirstinių (asimetrinio t skirstinio, Puasono-Gauso modelio, stabiliojo simetrinio vektoriaus dėsnio) parametrų vertinimo uždaviniai. Adaptuotai MCMC procedūrai sukurti yra pritaikytas nuoseklaus Monte-Karlo imčių generavimo metodas, įvedant statistinį stabdymo kriterijų ir imties tūrio reguliavimą. Statistiniai uždaviniai išspręsti šiuo metodu leidžia atskleisti aktualias MCMC metodų skaitmeninimo problemų ypatybes. MCMC algoritmų efektyvumas tiriamas pasinaudojant disertacijoje sudarytu statistinio modeliavimo metodu. Atlikti eksperimentai su sportininkų duomenimis ir sveikatos industrijai priklausančių įmonių finansiniais duomenimis patvirtino, kad metodo skaitinės savybės atitinka teorinį modelį. Taip pat sukurti metodai ir algoritmai pritaikyti sociologinių duomenų analizės modeliui sudaryti. Atlikti tyrimai parodė, kad adaptuotas MCMC algoritmas leidžia gauti nagrinėjamų skirstinių parametrų įvertinius per mažesnį grandžių skaičių ir maždaug du kartus sumažinti skaičiavimų apimtį. Disertacijoje sukonstruoti algoritmai gali būti pritaikyti stochastinio pobūdžio sistemų tyrimui ir kitiems statistikos uždaviniams spręsti MCMC metodu. / Markov chain Monte Carlo adaptive methods by creating computationally effective algorithms for decision-making of data analysis with the given accuracy are analyzed in this dissertation. The tasks for estimation of parameters of the multivariate distributions which are constructed in hierarchical way (skew t distribution, Poisson-Gaussian model, stable symmetric vector law) are described and solved in this research. To create the adaptive MCMC procedure, the sequential generating method is applied for Monte Carlo samples, introducing rules for statistical termination and for sample size regulation of Markov chains. Statistical tasks, solved by this method, reveal characteristics of relevant computational problems including MCMC method. Effectiveness of the MCMC algorithms is analyzed by statistical modeling method, constructed in the dissertation. Tests made with sportsmen data and financial data of enterprises, belonging to health-care industry, confirmed that numerical properties of the method correspond to the theoretical model. The methods and algorithms created also are applied to construct the model for sociological data analysis. Tests of algorithms have shown that adaptive MCMC algorithm allows to obtain estimators of examined distribution parameters in lower number of chains, and reducing the volume of calculations approximately two times. The algorithms created in this dissertation can be used to test the systems of stochastic type and to solve other statistical... [to full text]

Markov chain Monte Carlo adaptive methods by creating computationally effective algorithms for decision-making of data analysis with the given accuracy are analyzed in this dissertation. The tasks for estimation of parameters of the multivariate distributions which are constructed in hierarchical way (skew t distribution, Poisson-Gaussian model, stable symmetric vector law) are described and solved in this research. To create the adaptive MCMC procedure, the sequential generating method is applied for Monte Carlo samples, introducing rules for statistical termination and for sample size regulation of Markov chains. Statistical tasks, solved by this method, reveal characteristics of relevant computational problems including MCMC method. Effectiveness of the MCMC algorithms is analyzed by statistical modeling method, constructed in the dissertation. Tests made with sportsmen data and financial data of enterprises, belonging to health-care industry, confirmed that numerical properties of the method correspond to the theoretical model. The methods and algorithms created also are applied to construct the model for sociological data analysis. Tests of algorithms have shown that adaptive MCMC algorithm allows to obtain estimators of examined distribution parameters in lower number of chains, and reducing the volume of calculations approximately two times. The algorithms created in this dissertation can be used to test the systems of stochastic type and to solve other statistical... [to full text] / Disertacijoje nagrinėjami Markovo grandinės Monte-Karlo (MCMC) adaptavimo metodai, skirti efektyviems skaitiniams duomenų analizės sprendimų priėmimo su iš anksto nustatytu patikimumu algoritmams sudaryti. Suformuluoti ir išspręsti hierarchiniu būdu sudarytų daugiamačių skirstinių (asimetrinio t skirstinio, Puasono-Gauso modelio, stabiliojo simetrinio vektoriaus dėsnio) parametrų vertinimo uždaviniai. Adaptuotai MCMC procedūrai sukurti yra pritaikytas nuoseklaus Monte-Karlo imčių generavimo metodas, įvedant statistinį stabdymo kriterijų ir imties tūrio reguliavimą. Statistiniai uždaviniai išspręsti šiuo metodu leidžia atskleisti aktualias MCMC metodų skaitmeninimo problemų ypatybes. MCMC algoritmų efektyvumas tiriamas pasinaudojant disertacijoje sudarytu statistinio modeliavimo metodu. Atlikti eksperimentai su sportininkų duomenimis ir sveikatos industrijai priklausančių įmonių finansiniais duomenimis patvirtino, kad metodo skaitinės savybės atitinka teorinį modelį. Taip pat sukurti metodai ir algoritmai pritaikyti sociologinių duomenų analizės modeliui sudaryti. Atlikti tyrimai parodė, kad adaptuotas MCMC algoritmas leidžia gauti nagrinėjamų skirstinių parametrų įvertinius per mažesnį grandžių skaičių ir maždaug du kartus sumažinti skaičiavimų apimtį. Disertacijoje sukonstruoti algoritmai gali būti pritaikyti stochastinio pobūdžio sistemų tyrimui ir kitiems statistikos uždaviniams spręsti MCMC metodu.

Processing of Bolu-Himmetoglu (Type I Kerogen) and Ankara-Beypazari (Type II Kerogen) oil shales by flotation techniques were investigated for achieving clean solid fuel substitutes. Materials characterization was done through mineralogical, XRD and FTIR analyses. Flotation responses of the samples were tested with non-ionizing and ionizing collectors of cationic and anionic types. The effects of the collector dosage and pulp pH on cleaning were determined. Other important flotation parameters, conditioning time, flotation time, pulp density, particle size and frother dosage were encountered using a statistical approach, through a full two level factorial experimental design. An advanced flotation procedure, assisted by ultrasonic application, was developed for further improvement in flotation performance. The effects of cleaning on thermal characterstics and combustion kinetics were evaluated with Differential Scanning Calorimetry and ASTM methods while the changes in the emission profiles were assessed using Effluent Gas Analysis. Himmetoglu sample was characterized as a carbonate and organic rich humic oil shale with XRD and FTIR analyses while Beypazari oil shale involved significant carbonate and clay minerals and exhibited a fulvic character with a poor organics content. Reverse flotation with amine acetates provided the most effective means of cleaning with Himmetoglu oil shale. Ash was decreased from 34.76 % to 23.52 % with a combustible recovery of 83.57 % using 800 g/ton Flotigam CA at natural pH and the calorific value increased from 4312 kcal/kg to 5010 kcal/kg. Direct flotation with amines was most effective for Beypazari oil shale cleaning. Using Armoflote 17, ash was reduced from 69.88 % to 53.10 % with 58.64 % combustible recovery using 800 g/ton Armoflote 17 at natural pulp pH and the calorific value of the sample increased from 876 kcal/kg to 2046 kcal/kg. Following optimization, ash of Himmetoglu oil shale decreased to 16.81 % with 84.10 % combustible recovery and calorific value increased to 5564 kcal/kg. For Beypazari oil shale ash decreased to % 48.42 with 59.17 % combustible recovery and the calorific value increased to 2364 kcal/kg. Ultrasonic pre-treatment before flotation further decreased the ash of Himmetoglu sample to 11.82 % with 82.66 % combustible recovery at 15 minutes pre-conditioning time and 50 % power level. For Beypazari oil shale, ash decreased to 34.76 % with 64.78 % combustible recovery after 15 minutes pre-treatment time at 70 % power level. Comparative XRD spectra and SEM analyses revealed that the extent of mineral matter removal relied on the flotation performance. The thermal indicators considerably improved after cleaning and the extent of improvement increased with a decrease in the ash of the concentrates. Kinetic analysis showed the favorable effect of inorganics removal on the effectiveness and easiness of combustion and activation energies decreased after cleaning for both oil shales. The contributions of cleaning on the effectiveness of combustion were also revealed by the increases in the emission rates and total CO2 and CO emission amounts. CO2 emissions due to mineral matter decomposition and harmful SO2 emissions apparently decreased as a consequence of the cleaning of the undesired inorganic contituents and potentially cleaning components. Results of the cleaning and thermal analysis sudies revealed that it was possible to achieve a clean energy source alternative from oil shales through flotation and a significant potential can be anticipated for future use of oil shales as a cost effective and environmental friendly solid fuel substitute in view of Turkey&amp / #8217 / s great oil shale reserves.

En agriculture, la météo est le principal facteur de variabilité d’une année sur l’autre. Cette thèse vise à construire des modèles statistiques à grande échelle qui estiment l’impact des conditions météorologiques sur les rendements agricoles. Le peu de données agricoles disponibles impose de construire des modèles simples avec peu de prédicteurs, et d’adapter les méthodes de sélection de modèles pour éviter le sur-apprentissage. Une grande attention a été portée sur la validation des modèles statistiques. Des réseaux de neurones et modèles à effets mixtes (montrant l’importance des spécificités locales) ont été comparés. Les estimations du rendement de maïs aux États-Unis en fin d’année ont montré que les informations de températures et de précipitations expliquent en moyenne 28% de la variabilité du rendement. Dans plusieurs états davantage météo-sensibles, ce score passe à près de 70%. Ces résultats sont cohérents avec de récentes études sur le sujet. Les prévisions du rendement au milieu de la saison de croissance du maïs sont possibles à partir de juillet : dès juillet, les informations météorologiques utilisées expliquent en moyenne 25% de la variabilité du rendement final aux États-Unis et près de 60% dans les états plus météo-sensibles comme la Virginie. Les régions du nord et du sud-est des États-Unis sont les moins bien prédites. Le rendements extrêmement faibles ont nécessité une méthode particulière de classification : avec seulement 4 prédicteurs météorologiques, 71% des rendements très faibles sont bien détectés en moyenne. L’impact du changement climatique sur les rendements jusqu’en 2060 a aussi été étudié : le modèle construit nous informe sur la rapidité d’évolution des rendements dans les différents cantons des États-Unis et localisent ceux qui seront le plus impactés. Pour les états les plus touchés (au sud et sur la côte Est), et à pratique agricole constante, le modèle prévoit des rendements près de deux fois plus faibles que ceux habituels, en 2060 sous le scénario RCP 4.5 du GIEC. Les états du nord seraient peu touchés. Les modèles statistiques construits peuvent aider à la gestion sur le cours terme (prévisions saisonnières) ou servent à quantifier la qualité des récoltes avant que ne soient faits les sondages post-récolte comme une aide à la surveillance (estimation en fin d’année). Les estimations pour les 50 prochaines années participent à anticiper les conséquences du changement climatique sur les rendements agricoles, pour définir des stratégies d’adaptation ou d’atténuation. La méthodologie utilisée dans cette thèse se généralise aisément à d’autres cultures et à d’autres régions du monde. / In agriculture, weather is the main factor of variability between two consecutive years. This thesis aims to build large-scale statistical models that estimate the impact of weather conditions on agricultural yields. The scarcity of available agricultural data makes it necessary to construct simple models with few predictors, and to adapt model selection methods to avoid overfitting. Careful validation of statistical models is a major concern of this thesis. Neural networks and mixed effects models are compared, showing the importance of local specificities. Estimates of US corn yield at the end of the year show that temperature and precipitation information account for an average of 28% of yield variability. In several more weather-sensitive states, this score increases to nearly 70%. These results are consistent with recent studies on the subject. Mid-season maize crop yield forecasts are possible from July: as of July, the meteorological information available accounts for an average of 25% of the variability in final yield in the United States and close to 60% in more weather-sensitive states like Virginia. The northern and southeastern regions of the United States are the least well predicted. Predicting years for which extremely low yields are encountered is an important task. We use a specific method of classification, and show that with only 4 weather predictors, 71% of the very low yields are well detected on average. The impact of climate change on yields up to 2060 is also studied: the model we build provides information on the speed of evolution of yields in different counties of the United States. This highlights areas that will be most affected. For the most affected states (south and east coast), and with constant agricultural practice, the model predicts yields nearly divided by two in 2060, under the IPCC RCP 4.5 scenario. The northern states would be less affected. The statistical models we build can help for management on the short-term (seasonal forecasts) or to quantify the quality of the harvests before post-harvest surveys, as an aid to the monitoring (estimate at the end of the year). Estimations for the next 50 years help to anticipate the consequences of climate change on agricultural yields, and to define adaptation or mitigation strategies. The methodology used in this thesis is easily generalized to other cultures and other regions of the world.

Made available in DSpace on 2016-06-02T19:53:33Z (GMT). No. of bitstreams: 1 FATTIBENE_Marcos_2015.pdf: 1035875 bytes, checksum: 9f4308478818fe20ad4a239e96c1bb67 (MD5) Previous issue date: 2015-01-27 / The credit analysis for individuals has traditionally relied on three pillars: documentary proof of income and residence; refers to negative credit bureaus as SERASA and SCPC and the use of forecasting models based on the hypothesis that similar profiles in the future will reproduce the same credit behavior of the past, such as the "credit scores" (HAND; HENLEY, 2007) . This approach has been adequate, while being susceptible to moments of economic crisis or to fast profile changing of the target market, as occurred in the U.S. subprime in 2008. This study aims to point out ways to use Social Networks informational content, where individuals express and record their opinions, preferences, and especially get evident their network of relationships, in the credit analysis context. It was made evident the feasibility to investigate the assumption that an individual's proximity to other appropriate profile payers, or vice versa, influences the repayment rate. To illustrate such a conclusion, a real social network, enriched with credit data obtained by statistical simulation, was used. Three models of data weighting and three other based on multiple linear regression models were developed. In general the results were not statistically significant, by need to use a non-brazilian social network, as well synthetic data bureau score, since real information was not available in this country. It was shown a way to investigate the hypothesis that the informational content of a social network may generate greater efficiency into credit analysis when added to decision-making, operational and control systems of this segment. / A análise de crédito para pessoas físicas tem tradicionalmente se apoiado em três pilares: comprovação documental de renda e de residência; consulta a birôs negativos de crédito, como SERASA Experian e SCPC e a utilização de modelos de projeção baseados na hipótese que perfis semelhantes reproduzirão no futuro o comportamento de crédito do passado, como por exemplo, os credit scores (HAND ; HENLEY, 2007). Tal abordagem tem se mostrado adequada, sendo, entretanto suscetível a momentos de crise econômica ou mudança rápida do perfil do mercado alvo, a exemplo do ocorrido no mercado imobiliário dos EUA no ano de 2008. O presente trabalho propõe-se indicar alternativas para a utilização do teor informacional presente nas Redes Sociais, onde os indivíduos registram suas opiniões, preferências e especialmente evidenciam sua rede de relacionamentos, no contexto da análise de risco de crédito. Evidenciaram-se formas de averiguação da premissa que proximidade de um indivíduo a outros com perfil de bons pagadores, ou vice-versa, influencia a taxa de adimplência. Para se ilustrar tais sugestões, foi utilizada uma rede social real, enriquecida com dados de crédito obtidos por simulação estatística. Foram elaborados três modelos de ponderação de dados e três modelos baseados em regressão linear múltipla. Em geral os resultados não foram estatisticamente significantes, dada a necessidade de uso de rede social estrangeira como também da geração de dados sintéticos de score de birô de crédito, dada a indisponibilidade de informações reais no País. Porém, ficou evidenciada a viabilidade da averiguação da hipótese de que o conteúdo informacional contido em redes sociais pode ampliar a eficiência do sistema de análise de crédito, se incorporado aos sistemas decisórios, operativos e de controle.

Tanto na área pública como na área privada, as necessidades de financiamento são diretamente proporcionais às decisões de investimento. Para cada unidade monetária a ser investida há a necessidade de se obter fundos para o financiamento desse investimento. Quando são levantadas questões sobre o assunto –necessidades de financiamento- e essas questões são associadas às finanças municipais, surge uma lacuna para a qual, até o momento, não há estudos e/ou pesquisas que forneçam uma resposta sobre como medir o risco de crédito dos municípios brasileiros. A busca dessa resposta é o objetivo deste trabalho. A pesquisa bibliográfica forneceu o aporte teórico, tanto em finanças e crédito, como no uso de modelos econométricos. A análise de modelos de insolvência, aplicados a empresas, contribuiu para orientar os modelos que poderiam ser testados e possivelmente orientados para a análise do risco de crédito dos municípios. A Lei de Responsabilidade Fiscal (LRF), como uma primeira medida para iniciar o processo de gestão responsável, e, provavelmente, em um futuro próximo, a obrigatoriedade de divulgação dos demonstrativos financeiros e auditorias independentes sejam também componentes obrigatórios na gestão municipal, como também a adoção de “ratings” municipais, contribuíram para a motivação do desenvolvimento de um modelo de risco de crédito de municípios . Após a obtenção dos dados financeiros dos municípios brasileiros (no sitio da Secretaria do Tesouro Nacional), dos dados demográficos (disponibilizados em CD pelo Instituto Brasileiro de Geografia e Estatística - Base de informações municipais 3), da opinião de diversos especialistas sobre seu conceito em relação ao risco de crédito apresentado por diversos municípios, do tratamento desses dados e da constituição de um banco de dados integrando todas as informações selecionadas, e aplicando-se a análise estatística discriminante ao banco de dados obtido, obteve-se um modelo estatístico com um nível de acerto aproximado de 70% / As many on public area as on private area, the financing needs are relative to investment decisions. For each monetary unit to be invested there is need to obtain funds to financing. When questions are made about this issues –financing needs- and those questions are associated to municipal finances, one hiatus appears at this moment, wich there wasn’t studies or researches to be able to provide a reply or a solution on the subject to measure the brazilians municipal credit risk. The search for this solution is the subject of the present work. The bibliographic research provide the theoretical base, as many in finances and credit, as econometrics modeling. The bankrupt modeling analysis applied to companies, contributed to orient the templates that could be tested and possibly oriented to municipal credit risk analysis. A special Law of Fiscal Responsibility (LRF), is the first rule to begin the responsible management process, and probably, in the near future, the obligation of disclosure the financial statements, and independent audits that may be the mandatory components on municipal management, as well as the adoption or acceptance of municipal ratings contributed to the motivation to development of one model of municipal credit risk. After the attainment of brazilian cities financial information, from the official National Treasure site, demographic data (available in CD of Brazilian Institut of Geography & Statistics’ database of municipal information), and about expertise’s judgments on the subject of concept in relation to credit risk presented for many cities, about the treatment of these information and the creation of a database that grant the full integration of selected information, applying the discriminant function analysiys to the database obtained, resulted a statistic model that hit a target level with approximatly 70%.

Pour répondre à la demande mondiale croissante en huile et en protéines, le tournesol apparaît comme une culture très compétitive grâce à la diversification de ses débouchés et son attractivité environnementale et nutritionnelle. Pourtant, les teneurs en huile et protéines sont soumises à des effets génotypiques et environnementaux qui les rendent fluctuantes et difficilement prédictibles. Nous argumentons qu’une meilleure connaissance des effets les plus importants et leurs interactions devrait permettre de mieux prédire ces teneurs. Deux approches de modélisation ont été développées. Dans la première, trois modèles statistiques ont été construits puis comparés à un modèle simple existant. L’approche dynamique est basée sur l’analyse des relations source-puits au champ et en serre (2011 et 2012) pendant le remplissage. Les performances et domaines de validité des deux types de modélisation sont comparés. / Considering the growing global demand for oil and protein, sunflower appears as a highly competitive crop, thanks to the diversification of its markets and environmental attractiveness and health. Yet the protein and oil contents are submitted to genotypic and environmental effects that make them fluctuating and hardly predictable. We argue that a better knowledge of most important effects and their interactions should permit to improve prediction. Two modeling approaches are proposed: statistical one, where we compared three types of statistical models with a simple existing one. The dynamic approach is based on source-sink relationships analysis (field and greenhouse experiments in 2011 and 2012) during grain filling. Performances of both modeling types and their validity domain are compared.

Surfactants are used in a wide assortment of "down-the-drain" consumer products, yet they are often discharged in wastewater treatment plant effluent into receiving water, potentially causing environmental harm. The objective of this project was to predict surfactant toxic units and in-stream nutrients in the upper Trinity River watershed. Surface and pore water samples were collected in late summer 2005. General chemistries and surfactant toxic units were calculated. GIS models of anthropogenic and natural factors were collected and analyzed according to subwatersheds. Multiple regression analyses using the Maximum R2 improvement method were performed to predict surfactant toxic units and in-stream nutrients using GIS and in-stream values. Both geospatial and in-stream parameters generated multiple regression models for surfactant surface and pore water toxic units, as well as in-stream nutrients, with high R2 values. Thus, GIS and in-stream parameter modeling have the potential to be reliable and inexpensive method of predicting surfactant toxic units and nutrient loading in the upper Trinity River watershed.

Indiana University-Purdue University Indianapolis (IUPUI) / Reliable detection of low-frequency single nucleotide variants (SNVs) carries great significance in many applications. In cancer genetics, the frequencies of somatic variants from tumor biopsies tend to be low due to contamination with normal tissue and tumor heterogeneity. Circulating tumor DNA monitoring also faces the challenge of detecting low-frequency variants due to the small percentage of tumor DNA in blood. Moreover, in population genetics, although pooled sequencing is cost-effective compared with individual sequencing, pooling dilutes the signals of variants from any individual. Detection of low frequency variants is difficult and can be cofounded by multiple sources of errors, especially next-generation sequencing artifacts. Existing methods are limited in sensitivity and mainly focus on frequencies around 5%; most fail to consider differential, context-specific sequencing artifacts. To face this challenge, we developed a computational and experimental framework, RareVar, to reliably identify low-frequency SNVs from high-throughput sequencing data. For optimized performance, RareVar utilized a supervised learning framework to model artifacts originated from different components of a specific sequencing pipeline. This is enabled by a customized, comprehensive benchmark data enriched with known low-frequency SNVs from the sequencing pipeline of interest. Genomic-context-specific sequencing error model was trained on the benchmark data to characterize the systematic sequencing artifacts, to derive the position-specific detection limit for sensitive low-frequency SNV detection. Further, a machine-learning algorithm utilized sequencing quality features to refine SNV candidates for higher specificity. RareVar outperformed existing approaches, especially at 0.5% to 5% frequency. We further explored the influence of statistical modeling on position specific error modeling and showed zero-inflated negative binomial as the best-performed statistical distribution. When replicating analyses on an Illumina MiSeq benchmark dataset, our method seamlessly adapted to technologies with different biochemistries. RareVar enables sensitive detection of low-frequency SNVs across different sequencing platforms and will facilitate research and clinical applications such as pooled sequencing, cancer early detection, prognostic assessment, metastatic monitoring, and relapses or acquired resistance identification.

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