A pesquisa tem por finalidade avaliar dois modelos econométricos de mudanças de preços, que podem ser utilizados em estratégias de arbitragem estatística, o probit ordenado e o de decomposição, estimando seus parâmetros em quatro pregões de mini contratos de dólar futuro negociados na bolsa de valores brasileira. O estudo da negociação em alta frequência com a utilização de dados de transação a transação revela informações relativas à microestrutura de mercado que o ferramental mais tradicional não é capaz de desvendar. Uma das razões é que modelos tradicionais trabalham com variações de preço como variáveis contínuas, enquanto que ao considerar as variações de preço uma variável contínua e não uma variável discreta, como nos modelos aqui avaliados. Este trabalho acrescenta à literatura sobre microestrutura de mercado ao aplicar os modelos estudados em um ativo distinto daqueles avaliados nos papers originais, voltados ao exame do mercado de ações. Esta pesquisa concluiu que os modelos probit ordenado e de decomposição podem ser utilizados para previsão de mini contratos de dólar futuro e que o modelo de decomposição apresenta parâmetros mais significantes. Também concluiu-se que, no modelo probit ordenado, as variáveis de volume e time duration não se apresentaram relevantes na determinação do preço desse contrato e que a quantidade de defasagens utilizadas nos parâmetros estimados pode variar dentre os pregões. / The research aims to evaluate two econometric models of price change, which can be used in strategies of statistical arbitrage, the ordered probit model and the decomposition model, estimating its parameters in four trading sessions of mini US dollar futures contracts traded on the Brazilian Stock Exchange. The study of high frequency trading with the use of trade-by-trade price movements reveals information related to the market microstructure that the more traditional econometric tools are not able to solve when considering the price changes as a continuous variable and not a discrete one, like in the models evaluated here. This work adds to the literature on market microstructure by applying the models studied in an asset different from those evaluated in the original papers, aimed at examining the stock market. This research concluded that the ordered probit and decomposition models can be used to predict mini US dollar futures price changes and that the decomposition model presents more significant parameters. It was also concluded that, in the ordered probit model, the volume and time duration variables were not relevant in determining the price of this contract and that the number of lags used to estimate parameters can vary among the trading sessions.

What consequences arise as a result of repeated control of the legislature by the same party or coalition? Are incumbent parties less likely to lose an election the longer they remain in power? Furthermore, as parties remain in power longer and longer, do the factors which electoral scholars have proposed influence elections have less of an impact on election outcomes? The purpose of this project is to examine the electoral impact of repeated control of the legislature by the same party or ruling coalition. In this project, I argue that the length of time an incumbent party or coalition has maintained control of the legislature is a critical consideration for scholars interested in studying elections. In doing so, I hope to develop a better understanding of elections, the factors which influence election, and the mechanisms by which these factors affect election outcomes. Central to this project is the phenomenon I call party duration. I define party duration as the number of years the incumbent party has maintained control of the legislature in unicameral legislatures or the lower house in bicameral legislatures. This is the party that has secured enough seats to control the legislature independently in cases where a single party controls the legislature, or the party that serves as the largest party in the ruling coalition that controls the legislature in cases where a single party does not control the legislature by itself. Using cross-sectional time-series analysis to study a novel dataset, I show that not only does increasing party duration decreases the likelihood that an incumbent party will lose an election, controlling for various other factors, but I find evidence that party duration also affects the effect of other variables which influence elections. Specifically, I focus on the impact that the length of party duration has on the effect of economic conditions on the incumbent party's performance in elections. These findings highlight the importance of party duration, a variable which has previously not received attention from electoral scholars

Phreatic overgrowths on speleothems (POS) are one of many sea-level proxies available to Quaternary geologists in Mallorca; these carbonate encrustations form at the air-water interface in cave passages flooded with brackish water. POS are ideal for reconstruction of western Mediterranean sea level because they are widespread in Mallorca's caves, can be precisely dated by U-series methods, constrain sea-level stands to sub-meter elevation, and are well preserved and accessible in the subterranean environment. This research investigates the POS depositional environment, which is relatively understudied compared to the other proxies used for sea-level reconstructions. This disparity has led to assumptions on many aspects of the POS precipitation. Further, POS are typically composed of calcite, but sometimes the metastable polymorph aragonite is present instead. Two caves were studied because of the presence of a modern POS horizon of aragonite and calcite: Cova des Pas de Vallgornera (Vallgornera) and Coves del Drac (Drac), respectively. High-resolution air and water physical parameters were collected for the first time, along with monthly water samples for stable isotope and elemental analysis. This 16-month record was supplemented with detailed geochemical studies throughout the project, including water-column profiles and CO2 sampling campaigns. The water level in both caves preserves the semi-diurnal Mediterranean Sea tide signal, with a lag of approximately four hours. The fluctuation in both caves is slightly attenuated, and the direct effects of barometric pressure and precipitation could not be discerned from the primary control of tidal pumping. Calculations based on salinity and isotope analysis show that less than 20% of the solution in each cave is seawater. Degassing of CO2 from the cave water to air was documented at both locations, with sporadic calcium carbonate supersaturation. These conditions are strongly dependent on annual cave ventilation, which becomes active during winter when cold, dense tropospheric air sinks into the subsurface. In addition to seasonal thermo-circulation, fluctuating water level displaces cave air and likely initiates tropospheric exchange throughout the year. This process primarily occurs through fissures in thin overlying bedrock at Vallgornera and through the large entrance in Drac. Higher elemental ratios (Mg:Ca, Sr:Ca, Mg:Sr) known to enhance aragonite precipitation or inhibit calcite precipitation were recorded in Vallgornera's water. A linear correlation with salinity was not observed, so higher ratios in Vallgornera must be contributed from differences in lithology, bedrock weathering intensity, or nearby rising thermal waters. In summary, this research confirms POS strengths as sea-level proxies from geochemical and hydrological perspectives. These carbonate encrustations are precipitated at the air-water interface, which fluctuates as an attenuated expression of Mediterranean Sea tide. Degassing of CO2 from the cave water to air, which promotes calcium carbonate supersaturation, is the major control on POS deposition, and is facilitated by winter ventilation and likely water-level fluctuations. CO2 degassing (and theoretically precipitation of POS) in isotopic equilibrium is possible in caves with restricted ventilation (small/sealed entrances, small passages). Aragonite may be precipitated instead of calcite because of local modifications to the geochemical system from bedrock weathering or contributions from deep groundwater.

This study focuses on how the construction of a paleoclimate time series influences the interpretation in the frequency domain. Three time series are examined: a New Caledonian coral (Amedee Island), a Chinese speleothem (Dongge Cave), and New Mexican trees (El Malpais). This study presents a monthly resolved coral Sr/Ca time series from New Caledonia that reconstructs sea surface temperature (SST; 1648-1999). The chronology is based on density-band counting, cross-correlating Sr/Ca, and ²³°Th dating. The intracolony coral Sr/Ca variations are coherent on interannual to centennial time scales and are reproducible for >300 years. The SST reconstruction reveals estimated cooling trend (~0.4°C) from 1741-1815, a colder nineteenth century (~0.6°C), and a warming trend (~0.6°C) in the twentieth century. Spectral and wavelet analysis reveals significant inter-decadal periodicities (~14-21 years/cycle) that modulate with time, and nearly persistent multi-decadal periodicities (~25-33 years/cycle) that do not exhibit coherence with the Inter-decadal Pacific Oscillation. The multi-decadal periodicities may be a harmonic of the inter-decadal periodicities or may represent an independent mode not previously recognized. The Dongge Cave time series is based on uneven time intervals between data points (∆t) requiring interpolation to a constant ∆t for analysis with traditional spectral methods. A comparison of the even and uneven ∆t spectra using the Lomb-Scargle transform reveals the interpolated spectrum contains suppressed periodicities (<20 years/cycle), in contrast to the uninterpolated spectrum, resulting in a steeper slope in the red noise model thus influencing significance testing. The El Malpais time series is an average of tree-ring width series. Spectral analysis of the entire time series identified significant periodicities. However, significance varies between three temporal subsets, in which the number of series varies; therefore, these periodicities may be a function of the number of series or may represent a real temporal variability. Cross-spectral analysis of the El Malpais and Dongge Cave time series reveals significant coherence; however, cross-wavelet analysis, which examines localized frequencies in the time domain, reveals a lack of correlation; therefore, coherence in the frequency domain does not indicate correlation in the time domain.

This thesis presents the results of research into the use of factor models for stationary economic time series. Two basic scenarios are considered. The first is a situation where a large number of observations are available on a relatively small number variables, and a dynamic factor model is specified. It is shown that a dynamic factor model may be derived as a representation of a VARMA model of reduced spectral rank observed subject to measurement error. In some cases the resulting factor model corresponds to a minimal state-space representation of the VARMA plus noise model. Identification is discussed and proved for a fairly general class of dynamic factor model, and a frequency domain estimation procedure is proposed which has the advantage of generalising easily to models with rich dynamic structures. The second scenario is one where both the number of variables and the number of observations jointly diverge to infinity. The principal components estimator is considered in this case, and consistency is proved under assumptions which allow for much more error cross-correlation than the previously published theorems. Ancillary results include finite sample/variables bounds linking population principal components to population factors, and consistency results for principal components in a dual limit framework under a `gap' condition on the eigenvalues. A new factor model, named the Grouped Variable Approximate Factor Model, is introduced. This factor model allows for arbitrarily strong correlation between some of the errors, provided that the variables corresponding to the strongly correlated errors may be arranged into groups. An approximate instrumental variables estimator is proposed for the model and consistency is proved.

Thesis (Ph.D.)--McMaster University, 2006. / Supervisor: Susan J. Elliott. Includes bibliographical references (leaves 166-171).

This dissertation addresses the problem of distributed anomaly detection in Wireless Sensor Networks (WSN). A challenge of designing such systems is that the sensor nodes are battery powered, often have different capabilities and generally operate in dynamic environments. Programming such sensor nodes at a large scale can be a tedious job if the system is not carefully designed. Data modeling in distributed systems is important for determining the normal operation mode of the system. Being able to model the expected sensor signatures for typical operations greatly simplifies the human designer’s job by enabling the system to autonomously characterize the expected sensor data streams. This, in turn, allows the system to perform autonomous anomaly detection to recognize when unexpected sensor signals are detected. This type of distributed sensor modeling can be used in a wide variety of sensor networks, such as detecting the presence of intruders, detecting sensor failures, and so forth. The advantage of this approach is that the human designer does not have to characterize the anomalous signatures in advance. The contributions of this approach include: (1) providing a way for a WSN to autonomously model sensor data with no prior knowledge of the environment; (2) enabling a distributed system to detect anomalies in both sensor signals and temporal events online; (3) providing a way to automatically extract semantic labels from temporal sequences; (4) providing a way for WSNs to save communication power by transmitting compressed temporal sequences; (5) enabling the system to detect time-related anomalies without prior knowledge of abnormal events; and, (6) providing a novel missing data estimation method that utilizes temporal and spatial information to replace missing values. The algorithms have been designed, developed, evaluated, and validated experimentally in synthesized data, and in real-world sensor network applications.

The separation of natural and anthropogenically caused climatic changes is an important task of contemporary climate research. For this purpose, a detailed knowledge of the natural variability of the climate during warm stages is a necessary prerequisite. Beside model simulations and historical documents, this knowledge is mostly derived from analyses of so-called climatic proxy data like tree rings or sediment as well as ice cores. In order to be able to appropriately interpret such sources of palaeoclimatic information, suitable approaches of statistical modelling as well as methods of time series analysis are necessary, which are applicable to short, noisy, and non-stationary uni- and multivariate data sets. Correlations between different climatic proxy data within one or more climatological archives contain significant information about the climatic change on longer time scales. Based on an appropriate statistical decomposition of such multivariate time series, one may estimate dimensions in terms of the number of significant, linear independent components of the considered data set. In the presented work, a corresponding approach is introduced, critically discussed, and extended with respect to the analysis of palaeoclimatic time series. Temporal variations of the resulting measures allow to derive information about climatic changes. For an example of trace element abundances and grain-size distributions obtained near the Cape Roberts (Eastern Antarctica), it is shown that the variability of the dimensions of the investigated data sets clearly correlates with the Oligocene/Miocene transition about 24 million years before present as well as regional deglaciation events. Grain-size distributions in sediments give information about the predominance of different transportation as well as deposition mechanisms. Finite mixture models may be used to approximate the corresponding distribution functions appropriately. In order to give a complete description of the statistical uncertainty of the parameter estimates in such models, the concept of asymptotic uncertainty distributions is introduced. The relationship with the mutual component overlap as well as with the information missing due to grouping and truncation of the measured data is discussed for a particular geological example. An analysis of a sequence of grain-size distributions obtained in Lake Baikal reveals that there are certain problems accompanying the application of finite mixture models, which cause an extended climatological interpretation of the results to fail. As an appropriate alternative, a linear principal component analysis is used to decompose the data set into suitable fractions whose temporal variability correlates well with the variations of the average solar insolation on millenial to multi-millenial time scales. The abundance of coarse-grained material is obviously related to the annual snow cover, whereas a significant fraction of fine-grained sediments is likely transported from the Taklamakan desert via dust storms in the spring season. / Die Separation natürlicher und anthropogen verursachter Klimaänderungen ist eine bedeutende Aufgabe der heutigen Klimaforschung. Hierzu ist eine detaillierte Kenntnis der natürlichen Klimavariabilität während Warmzeiten unerlässlich. Neben Modellsimulationen und historischen Aufzeichnungen spielt hierfür die Analyse von sogenannten Klima-Stellvertreterdaten eine besondere Rolle, die anhand von Archiven wie Baumringen oder Sediment- und Eisbohrkernen erhoben werden. Um solche Quellen paläoklimatischer Informationen vernünftig interpretieren zu können, werden geeignete statistische Modellierungsansätze sowie Methoden der Zeitreihenanalyse benötigt, die insbesondere auf kurze, verrauschte und instationäre uni- und multivariate Datensätze anwendbar sind. Korrelationen zwischen verschiedenen Stellvertreterdaten eines oder mehrerer klimatologischer Archive enthalten wesentliche Informationen über den Klimawandel auf großen Zeitskalen. Auf der Basis einer geeigneten Zerlegung solcher multivariater Zeitreihen lassen sich Dimensionen schätzen als die Zahl der signifikanten, linear unabhängigen Komponenten des Datensatzes. Ein entsprechender Ansatz wird in der vorliegenden Arbeit vorgestellt, kritisch diskutiert und im Hinblick auf die Analyse von paläoklimatischen Zeitreihen weiterentwickelt. Zeitliche Variationen der entsprechenden Maße erlauben Rückschlüsse auf klimatische Veränderungen. Am Beispiel von Elementhäufigkeiten und Korngrößenverteilungen des Cape-Roberts-Gebietes in der Ostantarktis wird gezeigt, dass die Variabilität der Dimension der untersuchten Datensätze klar mit dem Übergang vom Oligozän zum Miozän vor etwa 24 Millionen Jahren sowie regionalen Abschmelzereignissen korreliert. Korngrößenverteilungen in Sedimenten erlauben Rückschlüsse auf die Dominanz verschiedenen Transport- und Ablagerungsmechanismen. Mit Hilfe von Finite-Mixture-Modellen lassen sich gemessene Verteilungsfunktionen geeignet approximieren. Um die statistische Unsicherheit der Parameterschätzung in solchen Modellen umfassend zu beschreiben, wird das Konzept der asymptotischen Unsicherheitsverteilungen eingeführt. Der Zusammenhang mit dem Überlapp der einzelnen Komponenten und aufgrund des Abschneidens und Binnens der gemessenen Daten verloren gehenden Informationen wird anhand eines geologischen Beispiels diskutiert. Die Analyse einer Sequenz von Korngrößenverteilungen aus dem Baikalsee zeigt, dass bei der Anwendung von Finite-Mixture-Modellen bestimmte Probleme auftreten, die eine umfassende klimatische Interpretation der Ergebnisse verhindern. Stattdessen wird eine lineare Hauptkomponentenanalyse verwendet, um den Datensatz in geeignete Fraktionen zu zerlegen, deren zeitliche Variabilität stark mit den Schwankungen der mittleren Sonneneinstrahlung auf der Zeitskala von Jahrtausenden bis Jahrzehntausenden korreliert. Die Häufigkeit von grobkörnigem Material hängt offenbar mit der jährlichen Schneebedeckung zusammen, während feinkörniges Material möglicherweise zu einem bestimmten Anteil durch Frühjahrsstürme aus der Taklamakan-Wüste herantransportiert wird.

Einleitung & Problemstellung: Beschwerden nach Beschleunigungsverletzungen der Halswirbelsäule sind oft nur unzureichend einzuordnen und diagnostizierbar. Eine eindeutige Diagnostik ist jedoch für eine entsprechende Therapie wie auch möglicherweise entstehende versicherungsrechtliche Forderungen notwendig. Die Entwicklung eines geeigneten Diagnoseverfahrens liegt damit im Interesse von Betroffenen wie auch Kostenträgern. Neben Störungen der Weichteilgewebe ist fast immer die Funktion der Halsmuskulatur in Folge eines Traumas beeinträchtigt. Dabei wird vor allem die sensorische Funktion der HWS-Muskulatur, die an der Regulation des Gleichgewichts beteiligt ist, gestört. In Folge dessen kann angenommen werden, dass es zu einer Beeinträchtigung der Gleichgewichtsregulation kommt. Die Zielstellung der Arbeit lautete deshalb, die möglicherweise gestörte Gleichgewichtsregulation nach einem Trauma im HWS-Bereich apparativ zu erfassen, um so die Verletzung eindeutig diagnostizieren zu können. Methodik: Unter Verwendung eines posturographischen Messsystems mit Kraftmomentensensorik wurden bei 478 Probanden einer Vergleichsgruppe und bei 85 Probanden eines Patientenpools Kraftmomente unter der Fußsohle als Äußerung der posturalen Balanceregulation aufgezeichnet. Die gemessenen Balancezeitreihen wurden nichtlinear analysiert, um die hohe Variabilität der Gleichgewichtsregulation optimal zu beschreiben. Über die dabei gewonnenen Parameter kann überprüft werden, ob sich spezifische Unterschiede im Schwankungsverhalten anhand der plantaren Druckverteilung zwischen HWS-Traumatisierten und den Probanden der Kontrollgruppe klassifizieren lassen. Ergebnisse: Die beste Klassifizierung konnte dabei über Parameter erzielt werden, die das Schwankungsverhalten in Phasen beschreiben, in denen die Amplitudenschwankungen relativ gering ausgeprägt waren. Die Analysen ergaben signifikante Unterschiede im Balanceverhalten zwischen der Gruppe HWS-traumatisierter Probanden und der Vergleichsgruppe. Die höchsten Trennbarkeitsraten wurden dabei durch Messungen im ruhigen beidbeinigen Stand mit geschlossenen Augen erzielt. Diskussion: Das posturale Balanceverhalten wies jedoch in allen Messpositionen eine hohe individuelle Varianz auf, so dass kein allgemeingültiges Schwankungsmuster für eine Gruppengesamtheit klassifiziert werden konnte. Eine individuelle Vorhersage der Gruppenzugehörigkeit ist damit nicht möglich. Die verwendete Messtechnik und die angewandten Auswerteverfahren tragen somit zwar zu einem Erkenntnisgewinn und zur Beschreibung des Gleichgewichtsverhaltens nach HWS-Traumatisierung bei. Sie können jedoch zum derzeitigen Stand für den Einzelfall keinen Beitrag zu einer eindeutigen Bestimmung eines Schleudertraumas leisten. / Introduction & Problem definition: Disorders after acceleration injuries of the cervical spine can often be classified and diagnosed only inadequately. But an explicit diagnosis is necessary as a basis for an adequate therapy as well as for possibly arising demands pursuant to insurance law. The development of suitable diagnosis methods is in the interest of patients as well as the cost units. Apart from disorders of the soft tissues there are almost always impairments of the function of the neck musculature. Particularly the sensory function of the cervical spine musculature, which participates in the regulation of the equilibrium, is disturbed by that. As a result in can be assumed that the postural control is also disturbed. Therefore the aim of this study was to examine the possibly disturbed postural motor balance after a whiplash injury of the cervical spine with the help of apparatus-supported methods to be able to unambigiously diagnose. Methods: postural measuring system based on the force-moment sensortechnique was used to record the postural balance regulation of 478 test persons and 85 patients which had suffered a whiplash injury. Data analysis was accomplished by linear as well as by nonlinear time series methods in order to characterise the balance regulation in an optimal way. Thus it can be determined whether there can be classified specific differences in the plantar pressure distribution covering patients with a whiplash injury and the test persons of the control group. Results: The best classification could be achieved by parameters which describe the variation of the postural balance regulation in phases in which the differences of the amplitudes of the plantar pressure distribution were relatively small. The analyses showed significant differences in the postural motor balance between the group of patients with whiplash injuries and the control group. The most significant differences (highest discriminate rates) could be observed by measurements in both-legged position with closed eyes. Discussion: Although the results achieved support the hypothesis mentioned above, is must be conceded that the postural motor balance showed a high individual variation in all positions of measurement. Therefore no universal variation model could be classified for the entirety of either group. This way an individual forecast of the group membership is impossible. As a result the measurement technology being used and the nonlinear time series analyses can contribute to the gain of knowledge and to the description of the regulation of postural control after whiplash injury. But at present they cannot contribute to an explicit determination of a whiplash injury for a particular case.

The Greenland Ice Sheet is the largest ice sheet in the northern hemisphere. Ongoing melting of the ice sheet, resulting in increased mass loss relative to the longer term trend, has raised concerns about the stability of the ice sheet. Melt water generated at the surface is temporarily stored in supra-glacial lakes on the ice sheet. Connections between melt water generation, storage and ice sheet dynamics highlight the importance of the surface hydrological system. In this thesis different methods are used that improve our ability to observe the supra-glacial lake system on the west Greenland Ice Sheet. This region of the Greenland Ice Sheet has the most extensive supra-glacial hydrological system with a dense network of streams connecting lakes that can exceed several square kilometres in area. Synthetic Aperture Radar (SAR) and visible-near infrared (VNIR) images are used to explore the potential of different sensor systems for regular observations of the supra-glacial lakes. SAR imagery is found to be a useful complement to VNIR data. VNIR data from moderate resolution sensors are preferred as these provide high temporal resolution data, ameliorating problems with cloud cover. The dynamic nature of the lakes makes automated classification difficult and manual mapping has been widely used. Here a new method is proposed that improves on existing methods by automating the identification and classification of lakes, and by introducing a flexible system that can capture the full range of lake forms. Applying our new method we are better able to analyse the evolution of lakes over a number of melt seasons. We find that lakes initiate after approximately 40 positive degree days. Most lakes exist for less than 20 days before draining, or later in the season, and less often, freezing over. Using the automated method developed in this thesis lakes have been mapped in imagery from 2001–2010 at approximately five day intervals. / <p>At the time of the doctoral defense, the following papers were unpublished and had a status as follows: Paper 3: Manuscript. Paper 4: Manuscript. Paper 5: Manuscript.</p>