People at risk for blood clots are often treated with anticoagulants, warfarin is such an anticoagulant. The dose's effect is measured by comparing the time for blood to clot to a control time called an INR value. Previous anticoagulant studies have addressed agreement between fingerstick (POC) devices and the standard laboratory, however these studies rely on mathematical formulas as criteria for clinical evaluations, i.e. clinical evaluation vs. precision and bias. Fourteen such criteria were found in the literature. There exists little consistency among these criteria for assessing clinical agreement, furthermore whether these methods of assessing agreement are reasonable estimates of clinical decision-making is unknown and has yet to be validated. One previous study compared actual clinical agreement by having two physicians indicate a dosing decision based on patient history and INR values. This analysis attempts to justify previously used mathematical criteria for clinical agreement. Generalized additive models with smoothing spline estimates were calculated for each of the 14 criteria and compared to the smoothing spline estimate for the method using actual physician decisions (considered the "gold standard"). The area between the criteria method spline and the gold standard method spline served as the comparison, using bootstrapping for statistical inference. Although some of the criteria methods performed better than others, none of them matched the gold standard. This stresses the need for clinical assessment of devices.

<p>Collections of large volumes of rich and complex data has become ubiquitous in recent years, posing new challenges in methodological and theoretical statistics alike. Today, statisticians are tasked with developing flexible methods capable of adapting to the degree of complexity and noise in increasingly rich data gathered across a variety of disciplines and settings. This has spurred the need for novel multivariate regression techniques that can efficiently capture a wide range of naturally occurring predictor-response relations, identify important predictors and their interactions and do so even when the number of predictors is large but the sample size remains limited. </p><p>Meanwhile, efficient model fitting tools must evolve quickly to keep pace with the rapidly growing dimension and complexity of data they are applied to. Aided by the tremendous success of modern computing, Bayesian methods have gained tremendous popularity in recent years. These methods provide a natural probabilistic characterization of uncertainty in the parameters and in predictions. In addition, they provide a practical way of encoding model structure that can lead to large gains in statistical estimation and more interpretable results. However, this flexibility is often hindered in applications to modern data which are increasingly high dimensional, both in the number of observations $n$ and the number of predictors $p$. Here, computational complexity and the curse of dimensionality typically render posterior computation inefficient. In particular, Markov chain Monte Carlo (MCMC) methods which remain the workhorse for Bayesian computation (owing to their generality and asymptotic accuracy guarantee), typically suffer data processing and computational bottlenecks as a consequence of (i) the need to hold the entire dataset (or available sufficient statistics) in memory at once; and (ii) having to evaluate of the (often expensive to compute) data likelihood at each sampling iteration. </p><p>This thesis divides into two parts. The first part concerns itself with developing efficient MCMC methods for posterior computation in the high dimensional {\em large-n large-p} setting. In particular, we develop an efficient and widely applicable approximate inference algorithm that extends MCMC to the online data setting, and separately propose a novel stochastic search sampling scheme for variable selection in high dimensional predictor settings. The second part of this thesis develops novel methods for structured sparsity in the high-dimensional {\em large-p small-n} regression setting. Here, statistical methods should scale well with the predictor dimension and be able to efficiently identify low dimensional structure so as to facilitate optimal statistical estimation in the presence of limited data. Importantly, these methods must be flexible to accommodate potentially complex relationships between the response and its associated explanatory variables. The first work proposes a nonparametric additive Gaussian process model to learn predictor-response relations that may be highly nonlinear and include numerous lower order interaction effects, possibly in different parts of the predictor space. A second work proposes a novel class of Bayesian shrinkage priors for multivariate regression with a tensor valued predictor. Dimension reduction is achieved using a low-rank additive decomposition for the latter, enabling a highly flexible and rich structure within which excellent cell-estimation and region selection may be obtained through state-of-the-art shrinkage methods. In addition, the methods developed in these works come with strong theoretical guarantees.</p> / Dissertation

Les électrolytes liquides, composés d’un sel dissous dans un solvant, interviennent dans la composition des batteries et font l’objet de nombreuses études afin d’améliorer leurs performances et leur sécurité. Parmi toutes les propriétés essentielles d’un électrolyte, la plus importante est sa conductivité ionique, qui influe sur les performances de la batterie. Pour un sel donné, la conductivité est elle-même principalement déterminée par les propriétés physico-chimiques du solvant comme sa constante diélectrique ou sa viscosité. L’objectif de cette étude est de développer des modèles permettant d’estimer des propriétés d’intérêt des électrolytes liquides, afin d’offrir un gain de temps aux chimistes, qui pourront éliminer les compositions inadéquates du point de vue de telle ou telle propriété. La première partie de cette étude présente une méthode pour estimer la conductivité d’un électrolyte, constitué d’un sel LiPF6 dans un mélange de solvants. Cette méthode s’appuie sur de nouvelles équations, pour estimer les paramètres de l’équation de Casteel-Amis, à partir de propriétés physico-chimiques du mélange de solvants, dont la constante diélectrique. La seconde partie présente a par ailleurs permis de développer une méthode pour estimer la constante diélectrique d’un solvant pur, à partir de sa structure chimique. Cette méthode s’appuie sur de nouveaux modèles additifs qui permettent d’estimer les paramètres de l’équation de Kirkwood-Fröhlich. Parmi ces modèles, deux d’entre eux permettent l’estimation de la densité et de l’indice de réfraction d’un composé liquide à température ambiante. L’ensemble des modèles développés sont utilisables via une interface utilisateur. / Liquid electrolytes, composed of a salt dissolved in a solvent, are used in the composition of batteries and are the subject of numerous studies to improve their performance and safety. Of all the essential properties of an electrolyte, the most important is its ionic conductivity, which influences the battery's performance. For a given salt, the conductivity itself is mainly determined by the physico-chemical properties of the solvent such as its dielectric constant or its viscosity. The objective of this study is to develop models to estimate properties of interest of liquid electrolytes, in order to offer time savings to chemists, who will be able to eliminate inadequate compositions from the point of view of such or such property. The first part of this study presents a method to estimate the conductivity of an electrolyte, consisting of a LiPF6 salt in a solvent mixture. This method is based on new equations, to estimate the parameters of the Casteel-Amis equation, based on the physico-chemical properties of the solvent mixture, including the dielectric constant. The second part also presents a method to estimate the dielectric constant of a pure solvent, based on its chemical structure. This method is based on new additive models that estimate the parameters of the Kirkwood-Fröhlich equation. Two of these models estimate the density and refractive index of a liquid compound at room temperature. All the models developed can be used via a user interface.

Thesis (M.Sc. (Computer Science))--North-West University, Potchefstroom Campus, 2008.

Ecosystem-level changes have taken place in the Mediterranean Sea over the last decades due to both anthropogenic interferences and natural perturbations. Compared to the western Mediterranean Sea, influences of these factors especially on flora and fauna characteristics are much more dramatic and intense in the eastern part, particularly in the northeastern Levant Sea where the study area is located. In this study, life history traits of some core species (both native and immigrant) occupying the continental shelf of the northeastern Levant Sea were studied in this changing ecosystem to improve limited ecological understanding of the demersal fish assemblages of the northeastern Levant Sea. For this purpose, the annual patterns in allocation and utilization of energy in demersal fish species, temporal and bathymetrical trends in fish distribution with respect to biological requirements of the species and strategies adapted by the species in growth, reproduction and energy storage were investigated by examining growth parameters, biological indices and abundance and biomass variations. Influences of environmental variables on spatiotemporal distribution and biological characteristic of Mullus barbatus were also explored by generalized additive models. Biological data were collected at monthly intervals between May 2007 and May 2010 by trawl sampling while sample collection of environmental variables (temperature and salinity) was performed from December 2008 to May 2010. Results of this study reveal that the components of the demersal fish assemblage in the region fulfill their biological activities within a short period of time when the highest productivity is reached in the area. Moreover, results indicate that within this short period of time, some native components of the demersal fish assemblages studied (Mullus barbatus and Pagellus erythrinus) exhibit strategies such as fast growth, early maturation, short reproduction season, secondary spawners to cope with the environmental peculiarities. On the other hand, the successful exotic colonizers develop strategies as well but these successful immigrants also use time (Lagocephalus suezensis) and space (depth) (Upeneus pori) slot that the native species avoid. In some of the species examined (Mullus barbatus and Lagocephalus suezensis), growth is fast, sexual maturity is early, reproduction period is short, and reproduction potential is high. With the peculiar environmental condition, these life history traits are attributed to the &ldquo / r-strategy&rdquo / of the species. In this study, generalized additive models of Mullus barbatus explain 81.5 % variations in Gonadosomatic Index (GSI), 55.2 % in Hepatosomatic Index (HSI) and 43.9 % in Condition Factor (K). The time component in the GAM model captures the same cyclic pattern observed in GSI of Mullus barbatus. Besides, The GAM results suggest that the highest GSI values associated with the bottom water temperature are between 18 &ndash / 19 &deg / C while the partial effect of bottom salinity is at 38.7 psu. A positive effect of depth on GSI of the species starts after 60 meters depth and increasing trend continues until 125 meters depth and then decreases. The HSI results are almost identical to GSI outputs indicating that the effects of the parameters concerned act in a similar manner. The results of the GAM models failed to explain influence of environmental parameters on vertical and seasonal distribution of adult Mullus barbatus. However 83.5 % variances were explained in distribution of juveniles. The salinity and temperature have the highest impact on the distribution of juveniles among the parameters evaluated. The results indicate that the occurrence of Atlantic Water in the area has a positive influence on M. barbatus, particularly on the recruits through either by its low salinity or by another factor associated with this water mass. The vertical distribution range are set by the high temperatures (&gt / 27 &deg / C) at the shallow depths during summer and the low temperatures on the shelf break zone (&lt / 16 &deg / C). A comparison of vertical abundance distribution of Mullus barbatus and the vertical temperature variations indicate that the species may tolerate up to 27 &deg / C and then individuals move to the deeper depths so that to the cooler waters when the temperature exceeds their tolerance limit. As well as the life history traits adopted by the species, there are some other factors providing advantages to the species. The fisheries regulations, particularly the time limits applied in the area are in favor of the species especially of pre-recruits. In the study area the pre-recruitment phase and summer YOY aggregations in shallow waters of most species studied in this thesis take place during a time when the fishing season is closed.

Thesis (M.Sc. (Computer Science))--North-West University, Potchefstroom Campus, 2008.

Thesis (M.Sc. (Computer Science))--North-West University, Potchefstroom Campus, 2008.

A estimação e previsão da volatilidade de ativos são de suma importância para os mercados financeiros. Temas como risco e incerteza na teoria econômica moderna incentivaram a procura por métodos capazes de modelar uma variância condicional que evolui ao longo do tempo. O objetivo principal desta dissertação é comparar alguns métodos de regressão global e local quanto à extração da volatilidade dos índices Ibovespa e Standard and Poor´s 500. Para isto, são realizadas estimações e previsões com os modelos GARCH paramétricos e com os modelos aditivos semi-paramétricos. Os primeiros, tradicionalmente utilizados na estimação de segundos momentos condicionais, têm sua capacidade sugerida em diversos artigos. Os segundos provêm alta flexibilidade e descrições visualmente informativas das relações entre as variáveis, tais como assimetrias e não linearidades. Sendo assim, testar o desempenho dos últimos frente às estruturas paramétricas consagradas apresenta-se como uma investigação apropriada. A realização das comparações ocorre em períodos selecionados de alta volatilidade no mercado financeiro internacional (crises), sendo a performance dos modelos medida dentro e fora da amostra. Os resultados encontrados sugerem a capacidade dos modelos semi-paramétricos em estimar e prever a volatilidade dos retornos dos índices nos momentos analisados. / Volatility estimation and forecasting are very important matters for the financial markets. Themes like risk and uncertainty in modern economic theory have encouraged the search for methods that allow for the modeling of time varying variances. The main objective of this dissertation is to compare global and local regressions in terms of their capacity to extract the volatility of Ibovespa and Standard and Poor 500 indexes. To achieve this aim, parametric GARCH and semiparametric additive models estimation and forecasting are performed. The first ones, traditionally applied in the estimation of conditional second moments, have their capacity suggested in many papers. The second ones provide high flexibility and visually informative descriptions of the relationships between the variables, like asymmetries and nonlinearities. Therefore, testing the last ones´ performance against the acknowledged parametric structures is an appropriate investigation. Comparisons are made in selected periods of high volatility in the international financial market (crisis), measuring the models´ performance inside and outside sample. The results that were found suggest the capacity of semiparametric models to estimate and forecast the Indexes returns´ volatility at the analyzed moments.

Artificial reefs have been deployed throughout the world’s oceans to act as habitat and fishing enhancement tools. To expand current research on the role of artificial reefs in the marine community, ordination and multivariate regression methods were used here to analyze survey data of natural and artificial reefs. The reefs, located in the Northern Gulf of Mexico (NGOM) and on the West Florida Shelf (WFS), had been previously surveyed from 2004 to 2015 using remote operated vehicle and stationary video techniques. This study tested the hypothesis that similar functional roles are accounted for at both natural and artificial reef sites even if species composition varies. Secondly, it examines the role of environment and fisheries in determining the assemblages. Artificial reefs tended to host communities that were as biodiverse as natural reefs, although not necessarily composed of the same species. Results of an ordination confirmed that as the classification was broadened from the level of species, to family, to functional group, the assemblages on each reef type (natural vs. artificial and NGOM vs WFS) appeared more similar. Dominant groups were present at all levels of classification and included the families Lutjanidae and Carangidae, as well as functional groups Red Snapper and Small Reef Fish. Both natural and artificial reefs tended to be dominated by one of the following: Lutjanidae, Carangidae, or Small Reef Fish, although a continuous gradient was found across the extremes of natural versus artificial reefs. Generalized Additive Models were developed to examine the influence of reef type, location, environment and fishing intensity covariates. Results indicated that for both natural and artificial reefs, the abundance of families and functional groups can be influenced by environmental factors. In both cases, there is strong spatial autocorrelation suggesting connectivity with neighboring reefs.

Machine learning has revolutionized recommendation systems by employing ranking models for personalized item suggestions. However, the complexity of learning-to-rank (LTR) models poses challenges in understanding the underlying reasons contributing to the ranking outcomes. This lack of transparency raises concerns about potential errors, biases, and ethical implications. To address these issues, interpretable LTR models have emerged as a solution. Currently, the state-of-the-art for interpretable LTR models is led by generalized additive models (GAMs). However, ranking GAMs face limitations in terms of computational intensity and handling high-dimensional data. To overcome these drawbacks, post-hoc methods, including local interpretable modelagnostic explanations (LIME), have been proposed as potential alternatives. Nevertheless, a quantitative evaluation comparing post-hoc methods efficacy to state-of-the-art ranking GAMs remains largely unexplored. This study aims to investigate the capabilities and limitations of LIME in an attempt to approximate a complex ranking model using a surrogate model. The proposed methodology for this study is an experimental approach. The neural ranking GAM, trained on two benchmark information retrieval datasets, serves as the ground truth for evaluating LIME’s performance. The study adapts LIME in the context of ranking by translating the problem into a classification task and asses three different sampling strategies against the prevalence of imbalanced data and their influence on the correctness of LIME’s explanations. The findings of this study contribute to understanding the limitations of LIME in the context of ranking. It analyzes the low similarity between the explanations of LIME and those generated by the ranking model, highlighting the need to develop more robust sampling strategies specific to ranking. Additionally, the study emphasizes the importance of developing appropriate evaluation metrics for assessing the quality of explanations in ranking tasks.