Electrohysterogram signal component cataloging with spectral and time-frequency methods

The Electrohysterogram (EHG) is a new instrument for pregnancy monitoring. It measures the uterine muscle electrical signal, which is closely related with uterine contractions. The EHG is described as a viable alternative and a more precise instrument than the currently most widely used method for the description of uterine contractions: the external tocogram. The EHG has also been indicated as a promising tool in the assessment of preterm delivery risk. This work intends to contribute towards the EHG characterization through the inventory of its components which are: • Contractions; • Labor contractions; • Alvarez waves; • Fetal movements; • Long Duration Low Frequency Waves; The instruments used for cataloging were: Spectral Analysis, parametric and non-parametric, energy estimators, time-frequency methods and the tocogram annotated by expert physicians. The EHG and respective tocograms were obtained from the Icelandic 16-electrode Electrohysterogram Database. 288 components were classified. There is not a component database of this type available for consultation. The spectral analysis module and power estimation was added to Uterine Explorer, an EHG analysis software developed in FCT-UNL. The importance of this component database is related to the need to improve the understanding of the EHG which is a relatively complex signal, as well as contributing towards the detection of preterm birth. Preterm birth accounts for 10% of all births and is one of the most relevant obstetric conditions. Despite the technological and scientific advances in perinatal medicine, in developed countries, prematurity is the major cause of neonatal death. Although various risk factors such as previous preterm births, infection, uterine malformations, multiple gestation and short uterine cervix in second trimester, have been associated with this condition, its etiology remains unknown [1][2][3].

Orthogonal models, structure crossing, nesting and inference

We intend to study the algebraic structure of the simple orthogonal models to use them, through binary operations as building blocks in the construction of more complex orthogonal models. We start by presenting some matrix results considering Commutative Jordan Algebras of symmetric matrices, CJAs. Next, we use these results to study the algebraic structure of orthogonal models, obtained by crossing and nesting simpler ones. Then, we study the normal models with OBS, which can also be orthogonal models. We intend to study normal models with OBS (Orthogonal Block Structure), NOBS (Normal Orthogonal Block Structure), obtaining condition for having complete and suffcient statistics, having UMVUE, is unbiased estimators with minimal covariance matrices whatever the variance components. Lastly, see ([Pereira et al. (2014)]), we study the algebraic structure of orthogonal models, mixed models whose variance covariance matrices are all positive semi definite, linear combinations of known orthogonal pairwise orthogonal projection matrices, OPOPM, and whose least square estimators, LSE, of estimable vectors are best linear unbiased estimator, BLUE, whatever the variance components, so they are uniformly BLUE, UBLUE. From the results of the algebraic structure we will get explicit expressions for the LSE of these models.

Mapping the Pasture Steppe in Bayankhongor, Mongolia: comparison of classification methods, using Landsat-8 and geophysical data

Grasslands in semi-arid regions, like Mongolian steppes, are facing desertification and degradation processes, due to climate change. Mongolia’s main economic activity consists on an extensive livestock production and, therefore, it is a concerning matter for the decision makers. Remote sensing and Geographic Information Systems provide the tools for advanced ecosystem management and have been widely used for monitoring and management of pasture resources. This study investigates which is the higher thematic detail that is possible to achieve through remote sensing, to map the steppe vegetation, using medium resolution earth observation imagery in three districts (soums) of Mongolia: Dzag, Buutsagaan and Khureemaral. After considering different thematic levels of detail for classifying the steppe vegetation, the existent pasture types within the steppe were chosen to be mapped. In order to investigate which combination of data sets yields the best results and which classification algorithm is more suitable for incorporating these data sets, a comparison between different classification methods were tested for the study area. Sixteen classifications were performed using different combinations of estimators, Landsat-8 (spectral bands and Landsat-8 NDVI-derived) and geophysical data (elevation, mean annual precipitation and mean annual temperature) using two classification algorithms, maximum likelihood and decision tree. Results showed that the best performing model was the one that incorporated Landsat-8 bands with mean annual precipitation and mean annual temperature (Model 13), using the decision tree. For maximum likelihood, the model that incorporated Landsat-8 bands with mean annual precipitation (Model 5) and the one that incorporated Landsat-8 bands with mean annual precipitation and mean annual temperature (Model 13), achieved the higher accuracies for this algorithm. The decision tree models consistently outperformed the maximum likelihood ones.

Nonparametric estimation of time-dependent ROC curves conditional on a continuous covariate

The receiver-operating characteristic (ROC) curve is the most widely used measure for evaluating the performance of a diagnostic biomarker when predicting a binary disease outcome. The ROC curve displays the true positive rate (or sensitivity) and the false positive rate (or 1-specificity) for different cut-off values used to classify an individual as healthy or diseased. In time-to-event studies, however, the disease status (e.g. death or alive) of an individual is not a fixed characteristic, and it varies along the study. In such cases, when evaluating the performance of the biomarker, several issues should be taken into account: first, the time-dependent nature of the disease status; and second, the presence of incomplete data (e.g. censored data typically present in survival studies). Accordingly, to assess the discrimination power of continuous biomarkers for time-dependent disease outcomes, time-dependent extensions of true positive rate, false positive rate, and ROC curve have been recently proposed. In this work, we present new nonparametric estimators of the cumulative/dynamic time-dependent ROC curve that allow accounting for the possible modifying effect of current or past covariate measures on the discriminatory power of the biomarker. The proposed estimators can accommodate right-censored data, as well as covariate-dependent censoring. The behavior of the estimators proposed in this study will be explored through simulations and illustrated using data from a cohort of patients who suffered from acute coronary syndrome.

Nonparametric estimation of the survival function for ordered multivariate failure time data: a comparative study

In longitudinal studies of disease, patients may experience several events through a follow-up period. In these studies, the sequentially ordered events are often of interest and lead to problems that have received much attention recently. Issues of interest include the estimation of bivariate survival, marginal distributions and the conditional distribution of gap times. In this work we consider the estimation of the survival function conditional to a previous event. Different nonparametric approaches will be considered for estimating these quantities, all based on the Kaplan-Meier estimator of the survival function. We explore the finite sample behavior of the estimators through simulations. The different methods proposed in this article are applied to a data set from a German Breast Cancer Study. The methods are used to obtain predictors for the conditional survival probabilities as well as to study the influence of recurrence in overall survival.

A structured inventory of spiders (Arachnida, Araneae) in natural and artificial forest gaps at Porto Urucu, Western Brazilian Amazonia

A preliminary survey of the spider fauna in natural and artificial forest gap formations at “Porto Urucu”, a petroleum/natural gas production facility in the Urucu river basin, Coari, Amazonas, Brazil is presented. Sampling was conducted both occasionally and using a protocol composed of a suite of techniques: beating trays (32 samples), nocturnal manual samplings (48), sweeping nets (16), Winkler extractors (24), and pitfall traps (120). A total of 4201 spiders, belonging to 43 families and 393 morphospecies, were collected during the dry season, in July, 2003. Excluding the occasional samples, the observed richness was 357 species. In a performance test of seven species richness estimators, the Incidence Based Coverage Estimator (ICE) was the best fit estimator, with 639 estimated species. To evaluate differences in species richness associated with natural and artificial gaps, samples from between the center of the gaps up to 300 meters inside the adjacent forest matrix were compared through the inspection of the confidence intervals of individual-based rarefaction curves for each treatment. The observed species richness was significantly higher in natural gaps combined with adjacent forest than in the artificial gaps combined with adjacent forest. Moreover, a community similarity analysis between the fauna collected under both treatments demonstrated that there were considerable differences in species composition. The significantly higher abundance of Lycosidae in artificial gap forest is explained by the presence of herbaceous vegetation in the gaps themselves. Ctenidae was significantly more abundant in the natural gap forest, probable due to the increase of shelter availability provided by the fallen trees in the gaps themselves. Both families are identified as potential indicators of environmental change related to the establishment or recovery of artificial gaps in the study area.

Notes on necrophagous flies (Diptera: Calyptratae) associated to fish carrion in Colombian Amazon

In recent years, there has been an increasing number of studies on carrion fly communities due to their medical importance and as a consequence of the large number of studies on forensic entomology. Surprisingly few studies have adressed with the asynantropic flies of the Amazon, and none were done in Colombia. A faunistic study of asynantropic flies of the families Calliphoridae, Sarcophagidae, Muscidae and Fannidae in three different landscapes of the Colombian Amazon is presented, trapping effectiveness is assessed, and the first record of Mesembrinella batesi (Aldrich, 1922) and Fannia femoralis (Stein, 1897) from Colombia is reported.

Estimação da função de sobrevivência em dados de eventos recorrentes com censura pela direita

Dissertação de mestrado em Estatística

О состоятельных оценках статистических структур Фишера

ნაშრომში დამტკიცებულია აუცილებელი და საკმარისი პირობები, თუ როდის უშვებს ფიშერის სტატისტიკური სტრუქტურა პარამეტრების ძალდებულ შეფასებას.

Assemblages of drosophilids (Diptera, Drosophilidae) in mangrove forests: community ecology and species diversity

Brazilian fauna of drosophilids has been researched in various ecosystems, but those in mangrove forests remain overlooked in Brazil and elsewhere. The present study attempts to characterise the assemblages of drosophilids of this environment, based on 28 collections taken in three mangrove areas in Santa Catarina Island, southern Brazil. The three mangroves surveyed were different in their surroundings, which varied from highly urbanised areas to conservation areas with natural vegetation. Overall, 69 species were collected, and no remarkable difference was detected in species composition and abundances or in the richness, evenness and heterogeneity between sites. The species abundance distribution observed fitted to a theoretical lognormal distribution in the three mangroves. The species richness scored and the performance of the species richness estimators showed an unexpectedly high diversity, considering the very low floristic diversity and the harsh conditions of the environment. Regarding species composition and abundances, the drosophilid mangrove assemblages were shown to be more similar to those found in open environments, with a marked dominance of exotic species. Finally, considering the apparent lack of feeding and breeding sites, we suggest that mangrove forests are acting as sink habitats for the drosophilids populations.

A proposal for a new specification for a conditionally heteroskedastic variance model: the Quadratic Moving-Average Conditional Heteroskedasticity and an application to the D. Mark-U.S. dollar Exchange Rate

Ever since the appearance of the ARCH model [Engle(1982a)], an impressive array of variance specifications belonging to the same class of models has emerged [i.e. Bollerslev's (1986) GARCH; Nelson's (1990) EGARCH]. This recent domain has achieved very successful developments. Nevertheless, several empirical studies seem to show that the performance of such models is not always appropriate [Boulier(1992)]. In this paper we propose a new specification: the Quadratic Moving Average Conditional heteroskedasticity model. Its statistical properties, such as the kurtosis and the symmetry, as well as two estimators (Method of Moments and Maximum Likelihood) are studied. Two statistical tests are presented, the first one tests for homoskedasticity and the second one, discriminates between ARCH and QMACH specification. A Monte Carlo study is presented in order to illustrate some of the theoretical results. An empirical study is undertaken for the DM-US exchange rate.

Estimation of dynamic latent variable models using simulated nonparametric moments

Given a model that can be simulated, conditional moments at a trial parameter value can be calculated with high accuracy by applying kernel smoothing methods to a long simulation. With such conditional moments in hand, standard method of moments techniques can be used to estimate the parameter. Since conditional moments are calculated using kernel smoothing rather than simple averaging, it is not necessary that the model be simulable subject to the conditioning information that is used to define the moment conditions. For this reason, the proposed estimator is applicable to general dynamic latent variable models. Monte Carlo results show that the estimator performs well in comparison to other estimators that have been proposed for estimation of general DLV models.

Implementación de técnicas de estimación y sincronización para sistemas OFDM: aplicación directa sobre la implementación de la capa física del estándar DVB-T

El sistema de modulación ODFM es utilizado en diversas aplicaciones de banda ancha, tanto en comunicaciones por cable como en aplicaciones inalámbricas. Presenta numerosas ventajas frente a sistemas de banda ancha de portadora única ya que permite una alta eficiencia espectral, una fácil ecualización y una reducción del ISI. Por el contrario, presenta dificultades inherentes a su estructura, que son de vital importancia solventar, entre las cuales se encuentran los altos requisitos de sincronización. Este proyecto presenta métodos de sincronización de tiempo y frecuencia implementados y evaluados sobre una plataforma software basada en Matlab®, que recoge el sistema completo de transmisión basado fielmente en el estándar DVB-T. Tras una presentación de los principios de la modulación OFDM, en este documento se presenta un estudio detallado de este sistema de transmisión y su implementación, formando conjuntamente una plataforma de simulación para la evaluación de los estimadores implementados.

Valuing American Derivatives by Least Squares Methods

Least Squares estimators are notoriously known to generate sub-optimal exercise decisions when determining the optimal stopping time. The consequence is that the price of the option is underestimated. We show how variance reduction methods can be implemented to obtain more accurate option prices. We also extend the Longsta¤ and Schwartz (2001) method to price American options under stochastic volatility. These are two important contributions that are particularly relevant for practitioners. Finally, we extend the Glasserman and Yu (2004b) methodology to price Asian options and basket options.

Optimal Martingales and American Option Pricing

Pricing American options is an interesting research topic since there is no analytical solution to value these derivatives. Different numerical methods have been proposed in the literature with some, if not all, either limited to a specific payoff or not applicable to multidimensional cases. Applications of Monte Carlo methods to price American options is a relatively new area that started with Longstaff and Schwartz (2001). Since then, few variations of that methodology have been proposed. The general conclusion is that Monte Carlo estimators tend to underestimate the true option price. The present paper follows Glasserman and Yu (2004b) and proposes a novel Monte Carlo approach, based on designing "optimal martingales" to determine stopping times. We show that our martingale approach can also be used to compute the dual as described in Rogers (2002).