The home range encompasses an animal’s movements as it goes about its normal activity, and several home range estimators have been developed. I evaluated the performance of the Minimum Convex Polygon, Bivariate Normal, and several kernel home range estimators in a geographical information system environment using simulations and a large database of O. pumilio mark-recapture locations. A fixed 90% kernel estimator using Least-Square Cross-Validation (to select the bandwidth) outperformed other methods of estimating home range size and was effective with relatively few capture points. Home range size, core area size, intrasexual overlap, and movement rates among coordinates were higher in female frogs than in male frogs. These measures likely reflect behavioral differences related to territoriality (males only) and parental care (both sexes). The simple Biological Index of Vagility (BIV) generated movement values that scaled well with home range size while revealing more information than home range estimates alone.

Atualmente, otimizar uma arquitetura ou um sistema de software não significa, necessariamente, aumentar o seu desempenho computacional. Devido a popularização de sistemas embutidos energizados por bateria, um item de grande importância a ser otimizado é o consumo de energia. De forma a obedecer às restrições de consumo, pesquisadores têm concentrado seus esforços na criação de ferramentas que possibilitam a modelagem, a otimização e a estimação do consumo de energia. Nos últimos anos, FPGAs têm apresentado um grande desenvolvimento nos quesitos densidade, velocidade e capacidade de armazenamento. Essas características tornaram possível a construção de sistemas complexos formados por um ou mais processadores soft-core. Esse tipo de processador permite uma personalização detalhada de suas características arquiteturais, possibilitando uma melhor adequação às restrições de tempo e espaço em um projeto. O objetivo deste trabalho é construir um estimador de potência para softwares que têm como alvo o processador soft-core Nios II da Altera, permitindo saber com antecedência quanta energia será consumida devido à execução de programas e aplicações de robótica móvel. O modelo implementado neste trabalho foi testado com vários benchmarks padronizados e os resultados obtidos provaram ser bastante adequados para estimar a energia consumida por um programa, obtendo erros de estimação máximos de 4,78% / Nowadays, optimization of hardware and software systems does not necessarily mean increasing their computational performance. Due to the popularization of battery-operated embedded systems, energy comsumption has become a very critical issue. Several tools have been created to model, optimize, and estimate energy consumption, allowing power constraints to be achieved. Lately, FPGAs have presented great advancements on density, speed and storage capacity. Such characteristics made possible the implementation of complex systems comprising one or more soft-core processors. This kind of processors allows detailed customization of its architectural features, enabling timinig, and area constraints of a design to be reached. The aim of this work is to build a power estimator to predict the energy comsumption of a software running on the Altera Nios II soft-core processor. The implemented estimation model presented on this dissertation has been tested with several standard benchmarks and the results obtained have proven to be suitable for estimating the energy consumption of a software with a maximum error of 4.78%

The efficiency of physical capital accumulation plays a critical role for economic growth. This paper aims at examine if economic freedom promotes economic growth via physical capital accumulation. This is done by estimating a production function by replacing the inputs with institutional indices. The first input is GDP per capital growth rate which serve as a proxy for institutional aspects and the other input are the economic freedom indexes which will serve as proxies for physical capital accumulation. This is done by applying the augmented mean group (AMG) estimator that is designed for dealing with macro panel data analysis, including twenty developing countries where the economies have experienced a rapid growth, and the time period are between 1995 and 2017. The theoretical framework is based on the Solow growth model, institutional theory and marginal efficiency of capital (MEC). The results show that economic freedom promotes economic growth via physical capital accumulation where GDP per capital growth rate served as a proxy for institutional aspects and economic freedom indexes as proxies for physical capital accumulation. The results also show that the AMG estimator is the best fit for macro panel data analysis since it are designed for dealing with heterogeneity.

The paper presents a posteriori error estimators for the stationary Stokes problem. We consider anisotropic finite element discretizations (i.e. elements with very large aspect ratio) where conventional, isotropic error estimators fail. Our analysis covers two- and three-dimensional domains, conforming and nonconforming discretizations as well as different elements. This large variety of settings requires different approaches and results in different estimators. Furthermore many examples of finite element pairs that are covered by the analysis are presented. Lower and upper error bounds form the main result with minimal assumptions on the elements. The lower error bound is uniform with respect to the mesh anisotropy with the exception of nonconforming 3D discretizations made of pentahedra or hexahedra. The upper error bound depends on a proper alignment of the anisotropy of the mesh which is a common feature of anisotropic error estimation. In the special case of isotropic meshes, the results simplify, and upper and lower error bounds hold unconditionally. Some of the corresponding results seem to be novel (in particular for 3D domains), and cover element pairs of practical importance. The numerical experiments confirm the theoretical predictions and show the usefulness of the anisotropic error estimators.

This thesis examines the volatility forecasting performance of six commonly used forecasting models; the simple moving average, the exponentially weighted moving average, the ARCH model, the GARCH model, the EGARCH model and the GJR-GARCH model. The dataset used in this report are three different Nordic equity indices, OMXS30, OMXC20 and OMXH25. The objective of this paper is to compare the volatility models in terms of the in-sample and out-of-sample fit. The results were very mixed. In terms of the in-sample fit, the result was clear and unequivocally implied that assuming a heavier tailed error distribution than the normal distribution and modeling the conditional mean significantly improves the fit. Moreover a main conclusion is that yes, the more complex models do provide a better in-sample fit than the more parsimonious models. However in terms of the out-of-sample forecasting performance the result was inconclusive. There is not a single volatility model that is preferred based on all the loss functions. An important finding is however not only that the ranking differs when using different loss functions but how dramatically it can differ. This illuminates the importance of choosing an adequate loss function for the intended purpose of the forecast. Moreover it is not necessarily the model with the best in-sample fit that produces the best out-of-sample forecast. Since the out-of-sample forecast performance is so vital to the objective of the analysis one can question whether the in-sample fit should even be used at all to support the choice of a specific volatility model.

Minimax D-optimal designs for regression models with heteroscedastic errors are studied and constructed. These designs are robust against possible misspecification of the error variance in the model. We propose a flexible assumption for the error variance and use a minimax approach to define robust designs. As usual it is hard to find robust designs analytically, since the associated design problem is not a convex optimization problem. However, the minimax D-optimal design problem has an objective function as a difference of two convex functions. An effective algorithm is developed to compute minimax D-optimal designs under the least squares estimator and generalized least squares estimator. The algorithm can be applied to construct minimax D-optimal designs for any linear or nonlinear regression model with heteroscedastic errors. In addition, several theoretical results are obtained for the minimax D-optimal designs. / Graduate

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Age, period, and cohort are three temporal dimensions that can make unique contributions to social and epidemiological changes that occur in populations over time. However, while the theoretical underpinnings for each temporal dimension are well established, the statistical techniques to assess the distinctive contributions of age, period and cohort are controversial. Unless questionable assumptions are imposed on the data, traditional linear regression models are incapable of estimating the independent contribution of each temporal dimension due to the linear dependence between age, period and cohort (A=P-C). Two recently developed methods, Hierarchical Age-PeriodCohort (HAPC) and Intrinsic Estimator (IE) models, enable researchers to estimate how all three temporal dimensions contribute to an outcome of interest without resorting to such assumptions. However, some simulation studies suggest that these new methods provide biased estimates of each temporal dimension. In this dissertation, I investigated whether practitioners can avoid biased results by first understanding the structure of the data. In Chapters 2 and 3, I examined whether visual plots of descriptive statistics and model selection statistics could identify various types of data structures through a series of simulation analyses. The results showed that preliminary data analysis is useful for identifying data structures that are compatible with the assumptions of HAPC and IE models. Moreover, when the data satisfied assumptions such as three-dimensionality and slight deviations from perfect functional forms, both HAPC and IE models tended to provide unbiased estimates of age, period and cohort effects. In Chapter 4, I provided a step-by-step demonstration for applying HAPC models by investigating the unique contributions of age, period and cohort to educational inequalities in the health of a large sample of U.S. adults. This study found that age and cohort effects contribute most to variability in health, and also that cross-validation is a useful way to incorporate HAPC models when preliminary analyses do not definitively show that the data structure is three dimensional.

The Forest Inventory and Analysis (FIA) program of the United States Department of Agriculture Forest Service collects forest inventory data that provide estimates with reasonable accuracy at the national scale. However, for smaller domains, these estimates are often not as accurate due to the small sample size. Small area estimation improves the accuracy of the estimates at smaller domains by relying on auxiliary information. This study compared direct (FIA estimates), indirect (multiple linear regression), and composite estimators (Fay-Herriot) using auxiliary information derived from Landsat and Global Ecosystem Dynamics Investigation (GEDI) to obtain county-level estimates of forest attributes namely total and merchantable volume (m3 ha-1), aboveground biomass (Mg ha-1), basal area (m2 ha-1), and Lorey’s mean height (m). Compared with FIA estimates, the composite estimator reduced error by 75-78% for all the variables of interest. This shows that a reasonable amount of precision can be achieved with auxiliary information from Landsat and GEDI, improving FIA estimates at the county level.

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