Many techniques have been developed to analyze time series. Spectral analysis and cycle regression analysis represent two such techniques. This study combines these two powerful tools to produce two new algorithms; the spectral algorithm and the one-pass algorithm. This research encompasses four objectives. The first objective is to link spectral analysis with cycle regression analysis to determine an initial estimate of the sinusoidal period. The second objective is to determine the best spectral window and truncation point combination to use with cycle regression for the initial estimate of the sinusoidal period. The third is to determine whether the new spectral algorithm performs better than the old T-value algorithm in estimating sinusoidal parameters. The fourth objective is to determine whether the one-pass algorithm can be used to estimate all significant harmonics simultaneously.

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The paper analyses the degree of output persistence in GDP in order to empirically discriminate between the Solow growth model, the perfect competition endogenous growth model, the imperfect competition endogenous growth model, and the subcase of a multiple equilibria model of endogenous growth for the case of Austria. We find that a temporary shock in the growth rate of output induces a permanent and larger effect on the level of GDP. This leads us to refute the Solow growth model and the perfect competition model. We find strong empirical support for the imperfect competition growth model, but cannot fully rule out the possibility of multiple equilibria growth rates. / Series: Department of Economics Working Paper Series

The mid-Holocene climactic eruption of Mount Mazama in Oregon, USA (Volcanic Explosivity Index, VEI-7) was among the largest eruptions globally during the Holocene. Despite evidence for possible hemispheric climatic impacts, the age of the eruption is not well-constrained and little is known about the environmental impacts of distal tephra deposition with previous studies showing no clear consensus. Further, the eruption occurred during a time of global climatic warming, raising questions about the impacts of tephra deposition in the context of longer-term change. Thus the aim of this thesis is to investigate the terrestrial and aquatic impacts of distal tephra deposition from the climactic eruption of Mount Mazama approximately 7700 years ago, and to reconstruct Holocene environmental change in the Pacific Northwest of North America. The Mazama tephra forms an important isochronous marker horizon. A refined age of 7682-7584 cal. years BP (95.4% probability range) for the eruption was acquired through Bayesian statistical modelling of 81 previously published radiocarbon age estimations. Through high resolution palaeoecological and statistical analyses (stratigraphy, tephra geochemistry, radiocarbon dating, pollen, diatoms and ordination) the aquatic and terrestrial impacts of tephra deposition are assessed. Records were examined from the centre and fringe of Moss Lake, Washington to elucidate regional and local effects on vegetation and to determine whether the observed aquatic impacts were consistent throughout the lake, or whether the diatoms were responding to other factors, such as climate or catchment changes. Tephra deposition from the climactic eruption of Mount Mazama caused a statistically significant local terrestrial impact with changes to open habitat vegetation (Cyperaceae and Poaceae) and changes in aquatic macrophytes (Myriophyllum spicatum, Equisetum) and alga (Pediastrum), but there was no significant regional impact of distal tephra deposition. Statistical testing suggests the regional changes observed were climate-driven, evidenced by longer-term, underlying environmental change. Tephra deposition had a statistically significant impact on the aquatic system with decreases of epiphytic taxa (Fragilaria brevistriata and Staurosira venter) and increases of epipelic (Brachysira brebissonii) and tychoplanktonic taxa (Aulacoseira sp.) indicating a change in habitat and an increase of the Si:P ratio, lasting approximately 150 years. Variance partitioning demonstrated tephra to be a significant environmental variable; however, directional change exerted most influence and interactions between variables are evident. This study clearly demonstrates that there are complex interactions between drivers of change which is evidenced through time series analysis of the diatom Holocene record, revealing periodicities of approximately 2000 years, 1300 years, and 450 years attributed to solar variation and ocean-atmosphere interactions. Overall, tephra had a significant local effect on the environment, but no significant impact on the region independent of underlying environmental changes. More studies of similar nature are needed to evaluate the wider regional significance of the localised impacts shown at Moss Lake.

The objective of this thesis is to propose fundamental concepts, analytical and numerical tools, and approaches to characterize, understand, and better observe complex systems. The scientific contribution of this thesis can be separated in tree topics. In the first one, we show how to theoretically estimate the Mutual Information Rate (MIR), the amount of mutual information transmitted per unit of time between two time-series. We then show how a quantity derived from it can be successfully used to infer the network structure of a complex system. The proposed inference methodology shows to be robust in the presence of additive noise, different time-series lengths, and heterogeneous node dynamics and coupling strengths. It also shows to be superior in performance for networks formed by nodes possessing different time-scales, as compared to inference methods based on mutual information (MI). In the second topic, a deep analysis of causality from the space-time properties of the observed probabilistic space is performed. We show the existence of special regions in the state space which indicate variable ranges responsible for most of the information exchanged between two variables. We define a new causality measure named CaMI that explores a property we have understood: in order to detect if there is a flow of information from X to Y, one only needs to check the positiveness of the MI between trajectories in X and Y, however assuming that the observational resolution in Y is larger than in X. Moreover, we show how the assessment of causality can be done when we consider partitions with arbitrary, but equal rectangular cells in the probabilist space, what naturally facilitates the calculation of CaMI. In the third topic, we develop a symbolic coefficient of observability that allows us to understand what is the reduced set of accessible variables to observe a complex system, such that it can be fully reconstructed from the set of observed variables, regardless of its dimension. Using this symbolic coefficient, we explain how it is possible to compare different complex systems from the point of view of observability and how to construct systems of any dimensionality that can be fully observed by only one variable.

The aim of this paper is to outline the possibility of application of statistical methods for business valuation. This paper provides a basic overview of the subject in particular mathematical statistical point of view. The first chapter contains an introduction to the field of business valuation are presented valuation areas where it is possible to use different statistical methods. In the following parts of my work it is possible to find a description of methods and a brief description of the problem. The work is mainly focused on the analysis of time series. At the end of the theoretical part of the time series analysis problems of application of regression models are mentioned as well as the difficulties of its application practice. Potential solutions of these problems are mentioned. The final chapter is devoted to practical demonstration of application of the proposed methods on real data for a selected company. The work presents unique suitability of statistical methods in business valuation and demonstrates their practical application.

My thesis focuses on emission of bank loans and its analysis between 2005 and 2015 in the Czech Republic. This thesis informs a reader about Czech banking sector which is the main source of external financing in non-financial enterprises as well as among households. Emission of bank loans to these two sectors is very important for the czech economy that's why one chapter describes this issue. Also the various aspects such as global financial crisis, joining the Czech Republic to the Europian Union, interest rates and their influence on loan emission are shown. Thesis offers some facts about financial cycle and its indicator which has certain forecasting abilities.

Background: The use of antimicrobials has been presented as a significant risk factor for Clostridium difficile infection (CDI). Nevertheless, it remains unclear which antimicrobials are more likely to be associated with CDI. It is mandatory for acute trusts to report the numbers of diagnosed CDI cases to the Health Protection Agency (HPA). There is no national system to collect and analyse antimicrobial usage data from the trusts. The company IMS collects antimicrobial usage data from the trusts for creating marketing research statistics. Therefore, it was hypothesised that data collected from the HPA and from IMS could be used to undertake an ecological study about the association between CDI cases and antimicrobial use in English trusts. Methods: A trust-level Antimicrobial Usage Database provided by IMS and a database, including the numbers of CDI cases for patients aged 65 years old and above, provided by the HPA, were utilised in this work. These referred to 26 out of the 29 NW SHA trusts (that managed 64 hospitals) for the quarters between 2005 and 2008 inclusive. A sample of antimicrobial usage data collected directly from trusts was used to investigate potential limitations in using the Antimicrobial Usage Database for the purpose of this work. Multilevel models were used to study antimicrobial usage and the number of CDI cases over time. These models were also used to investigate the association between the CDI cases and antimicrobial usage in the trusts. The trends of trust antimicrobial usage over time were compared with DH recommendations for the prevention of CDI through antimicrobial prescribing published in 1994, 2005 and 2008. Results: Discrepancies between the antimicrobial usage recorded in the IMS database and in a sample of antimicrobial usage data collected from trusts were found for 31 out of 155 antimicrobial usage records; only 1 of these referred to an antimicrobial with high usage. Eight out of the 23 antimicrobial groups and 10 out of 63 antimicrobials were presented as having high usage. The antimicrobial usage over time increased significantly for 7 antimicrobial groups, decreased significantly for 2 groups and remained constant for 54 groups. The number of CDI cases reported for patients aged 65 years old and above decreased significantly over the time. Trust antimicrobial usage over time changed in the opposite direction compared to the DH recommendations published in 1994, 2004 and 2008, respectively, for 2 out of 11, 3 out of 12 and 3 out of 14 antimicrobial groups/antimicrobials. The increased usage of 5 antimicrobial groups was significantly associated with an increase in the number of CDI cases and an increased usage of 4 antimicrobial groups was significantly associated with a decreased number of CDI cases. Within the antimicrobial groups that were significantly associated with an increased number of CDI cases, the usage of 8 individual antimicrobials was significantly associated with the CDI cases. Discussion/Conclusion: Collecting antimicrobial usage over time for large groups of trusts is very time consuming and requires extensive data manipulation. The similarity of the results of this study with those of previously published studies suggest that HPA and IMS data may be used to investigate the association between CDI cases and antimicrobial usage in English trusts.

Orientador: Celso Pascoli Bottura / Dissertação (mestrado) - Universidade Estadual de Campinas, Faculdade de Engenharia Elétrica e de Computação / Made available in DSpace on 2018-08-27T02:15:32Z (GMT). No. of bitstreams: 1 Alegria_ElvisOmarJara_M.pdf: 5581682 bytes, checksum: cd5b08b04c7ba4bcd505ab00e5335ffc (MD5) Previous issue date: 2015 / Resumo: Este trabalho é sobre a identificação recursiva em tempo real das dependências parâmetro-estado em modelos de regressão de series temporais estocásticas. O descobrimento dessas dependências é útil para obter uma nova, e mais acurada, estrutura do modelo. Os métodos recursivos convencionais de estimação de parâmetros variantes no tempo, não conseguem bons resultados quando os modelos apresentam parâmetros dependentes do estado (SDP) pois eles tem comportamento altamente não linear e inclusive caótico. Nossa proposta está baseada no estudo de Peter Young para SDPs no caso Off-Line. É discutido o método que ele propõe para reduzir a entropia das séries nos modelos com SDP e para isto se apresenta umas transformações dos dados. São propostas mudanças no seu algoritmo Off-Line que o fazem mais rápido, eficiente e manejável para a implementação do modo On-Line. Finalmente, três exemplos numéricos são mostrados para validar as nossas propostas e a sua aplicação na área de detecção de falhas paramétricas. Todas as funções foram implementadas no MATLAB e conformam um toolbox para identificação de SDP em modelos de regressão / Abstract: This work is about the identification of the dependency among parameters and states in regression models of stochastic time series. The discovery of that dependency can be useful to obtain a more accurate model structure. Conventional recursive algorithms for estimation of Time Variable Parameters do not provide good results in models with state-dependent parameters (SDP) because these may have highly non-linear and even chaotic behavior. This work is based on Peter Young's studies about Off-Line SDP. Young's methods to data entropy reduction are discussed and some data transformations are proposed for this. Later, are proposed some changes on the Off-Line algorithm in order to improve its velocity, accuracy, and tractability to generate the On-Line version. Finally, three numeric examples to validate our proposal are shown. All the functions were implemented in MATLAB and conform a Toolbox to the SDP identification in regression models / Mestrado / Automação / Mestre em Engenharia Elétrica

Cell-to-cell variations and intracellular processes such as cytoskeletal organization and organelle dynamics exhibit massive heterogeneity. Advances in imaging and optics have enabled researchers to access spatiotemporal information in living cells efficiently. Even though current imaging technologies allow us to acquire an unprecedented amount of cell images, it is challenging to extract valuable information from the massive and complex dataset to interpret heterogeneous biological processes. Machine learning (ML), referring to a set of computational tools to acquire knowledge from data, provides promising solutions to meet this challenge. In this dissertation, we developed ML pipelines for deconvolution of subcellular protrusion heterogeneity from live cell imaging and molecular diagnostic from lens-free digital in-line holography (LDIH) imaging. Cell protrusion is driven by spatiotemporally fluctuating actin assembly processes and is morphodynamically heterogeneous at the subcellular level. Elucidating the underlying molecular dynamics associated with subcellular protrusion heterogeneity is crucial to understanding the biology of cellular movement. Traditional ensemble averaging methods without characterizing the heterogeneity could mask important activities. Therefore, we established an ACF (auto-correlation function) based time series clustering pipeline called HACKS (deconvolution of heterogeneous activities in coordination of cytoskeleton at the subcellular level) to identify distinct subcellular lamellipodial protrusion phenotypes with their underlying actin regulator dynamics from live cell imaging. Using our method, we discover “accelerating protrusion”, which is driven by the temporally ordered coordination of Arp2/3 and VASP activities. Furthermore, deriving the merits of ML, especially Deep Learning (DL) to learn features automatically, we advanced our pipeline to learn fine-grained temporal features by integrating the prior ML analysis results with bi-LSTM (bi-direction long-short term memory) autoencoders to dissect variable-length time series protrusion heterogeneity. By applying it to subcellular protrusion dynamics in pharmacologically and metabolically perturbed epithelial cells, we discovered fine differential response of protrusion dynamics specific to each perturbation. This provides an analytical framework for detailed and quantitative understanding of molecular mechanisms hidden in their heterogeneity. Lens-free digital in-line holography (LDIH) is a promising microscopic tool that overcomes several drawbacks (e.g., limited field of view) of traditional lens-based microscopy. Numerical reconstruction for hologram images from large-field-of-view LDIH is extremely time-consuming. Until now, there are no effective manual-design features to interpret the lateral and depth information from complex diffraction patterns in hologram images directly, which limits LDIH utility for point-of-care applications. Inherited from advantages of DL to learn generalized features automatically, we proposed a deep transfer learning (DTL)-based approach to process LDIH images without reconstruction in the context of cellular analysis. Specifically, using the raw holograms as input, the features extracted from a well-trained network were able to classify cell categories according to the number of cell-bounded microbeads, which performance was comparable with that of object images as input. Combined with the developed DTL approach, LDIH could be realized as a low-cost, portable tool for point-of-care diagnostics. In summary, this dissertation demonstrate that ML applied to cell imaging can successfully dissect subcellular heterogeneity and perform cell-based diagnosis. We expect that our study will be able to make significant contributions to data-driven cell biological research.