O estudo comparou o desempenho preditivo dos modelos de previsão de redes neurais e de suavização exponencial, empregando dados diários do preço da arroba do boi gordo futuro (BM&FBOVESPA) entre janeiro de 2010 até dezembro de 2015. Os resultados mostram que modelos relativamente mais complexos como redes neurais não necessariamente apresentam melhor desempenho se comparados a modelos mais simples, e também mostram que a classificação relativa muda conforme variam as medidas de ajuste e/ou horizonte de previsão além de vantagens associadas a combinação de diversos modelos. / This study compared the predictive performance between neural network models and exponential smoothing, using daily data of live cattle future price (BM&FBOVESPA) from January 2010 to December 2015. The results show that relatively more complex models like neural networks do not necessarily display better performance compared to simpler ones. Results also shows that relative classification changes with respect to adjust measures and/or forecast horizons changes besides advantages achieved by model combinaion.

abstract: This dissertation report follows a three-paper format, with each paper having a different but related focus. In Paper 1 I discuss conceptual analysis of mathematical ideas relative to its place within cognitive learning theories and research studies. In particular, I highlight specific ways mathematics education research uses conceptual analysis and discuss the implications of these uses for interpreting and leveraging results to produce empirically tested learning trajectories. From my summary and analysis I develop two recommendations for the cognitive researchers developing empirically supported learning trajectories. (1) A researcher should frame his/her work, and analyze others’ work, within the researcher’s image of a broadly coherent trajectory for student learning and (2) that the field should work towards a common understanding for the meaning of a hypothetical learning trajectory. In Paper 2 I argue that prior research in online learning has tested the impact of online courses on measures such as student retention rates, satisfaction scores, and GPA but that research is needed to describe the meanings students construct for mathematical ideas researchers have identified as critical to their success in future math courses and other STEM fields. This paper discusses the need for a new focus in studying online mathematics learning and calls for cognitive researchers to begin developing a productive methodology for examining the meanings students construct while engaged in online lessons. Paper 3 describes the online Precalculus course intervention we designed around measurement imagery and quantitative reasoning as themes that unite topics across units. I report results relative to the meanings students developed for exponential functions and related ideas (such as percent change and growth factors) while working through lessons in the intervention. I provide a conceptual analysis guiding its design and discuss pre-test and pre-interview results, post-test and post-interview results, and observations from student behaviors while interacting with lessons. I demonstrate that the targeted meanings can be productive for students, show common unproductive meanings students possess as they enter Precalculus, highlight challenges and opportunities in teaching and learning in the online environment, and discuss needed adaptations to the intervention and future research opportunities informed by my results. / Dissertation/Thesis / Doctoral Dissertation Mathematics Education 2018

Wong, Tat Wing. / Thesis (M.Phil.)--Chinese University of Hong Kong, 2011. / Includes bibliographical references (p. 53-55). / Abstracts in English and Chinese. / Chapter 1 --- Introduction --- p.1 / Chapter 2 --- Problem Formulation --- p.5 / Chapter 2.1 --- Phase-type distribution --- p.5 / Chapter 2.1.1 --- A generalization of the exponential distribution --- p.5 / Chapter 2.1.2 --- Properties of the phase-type distribution --- p.6 / Chapter 2.2 --- Phase-type jump diffusion model --- p.8 / Chapter 2.2.1 --- Jump diffusion model --- p.8 / Chapter 2.2.2 --- The stock price model --- p.9 / Chapter 2.3 --- Stock Loans --- p.10 / Chapter 3 --- General Properties of Stock Loans --- p.12 / Chapter 3.1 --- Preliminary results --- p.12 / Chapter 3.2 --- Characterization of the function V(x) --- p.15 / Chapter 4 --- Valuation / Chapter 4.1 --- Hyperexponential jumps --- p.25 / Chapter 4.1.1 --- Solution of the linear system --- p.29 / Chapter 4.1.2 --- Solution of the optimal exercise boundary --- p.30 / Chapter 4.2 --- Phase-type jumps --- p.33 / Chapter 4.3 --- The case for G'(1)≥ 0 --- p.36 / Chapter 5 --- Future Research Direction --- p.38 / Chapter 5.1 --- The fast mean-reverting stochastic volatility model --- p.38 / Chapter 5.2 --- Asymptotic expansion of stock loan --- p.39 / Chapter 5.2.1 --- The zeroth order term --- p.41 / Chapter 5.2.2 --- The first order term --- p.43 / Chapter 6 --- Conclusion --- p.52 / Bibliography --- p.53

Este trabalho contém resultados sobre a existência, unicidade e comportamento assintótico de soluções para uma equação de viga não linear do tipo Kirchhoff, \'u IND. tt\' \'+ \'DELTA\' POT. 2\' u - M(\'INT.IND. OMEGA\' | \'NABLA\' u| 2 dx) \'DELTA\' u+ f (\'u IND. t\' ) +g(u) = h em × R +, onde \'R POT. N\' é um domínio limitado com fronteira regular \\GAMA. Essa equação é um modelo matemático para pequenas vibrações transversais de vigas ou placas extensíveis. O termo não local M(\'INT.IND. OMEGA\' | \\NABLA u |2 dx) u está relacionado à variação de tensão na viga devida à sua extensibilidade. O termo f (\'u IND. t\' ) representa uma dissipação para o sistema e g(u) representa a força exercida pelo meio. A função h representa uma força externa adicional. Consideramos o problema com as condições de fronteira u|×R + = \'INT. u SUP. \'INT. v\' | \\\'GAMA\' ×\'R +\' = 0, que corresponde ao modelo de vigas fixadas pelo bordo \\\'GAMA\'. Discutiremos o caso em que a dissipação é linear e o caso em que é não linear. Mostraremos que em ambos os casos o sistema dinâmico associado ao problema possui um atrator global. Entretanto, para o caso em que a dissipação é linear, obtemos num espaço de fase mais regular, a existência de um conjunto inércia de dimensão finita, que atrai exponencialmente todos os limitados deste espaço / This work contains some results on the existence, uniqueness and asymptotic behavior of solutions for a nonlinear beam equation of Kirchhoff type, \'u IND. tt\' + \' DELTA POT. 2\' u+ M(\'INT. IND.\' |u| 2 dx) u + g(\'u IND. t\') + f (u) = h; where \'R POT. N\' is a bounded domain with smooth boundary . This equation is a model for small vibrations of extensible beams. The nonlocal term M(\' INT. IND.\' |u| 2 dx) u is related to the variation of tensions in the beam due to its extensibility. The term f (\'u IND. t\') represents a damping mechanism for the system and g(u) represents the force exerted by the foundation. The function h represents an additional external force. We consider the problem with boundary condition u|×R+ = \' u SUP. \' |×R+ = 0, which corresponds to the model of clamped beams. We discuss the cases where the dissipation is linear and the case nonlinear. We show that in both cases, the dynamical system associated to the problem has a global attractor. However, when the dissipation is linear, we obtain, in a more regular space, the existence of an inertial set of finite dimension, which attracts exponentially all bounded sets of this space

Recovering a linear combination of exponential signals characterized by parameters is highly significant in many MR imaging applications such as parameter mapping and spectroscopy. The parameters carry useful clinical information and can act as biomarkers for various cardiovascular and neurological disorders. However, their accurate estimation requires a large number of high spatial resolution images, resulting in long scan time. One of the ways to reduce scan time is by acquiring undersampled measurements. The recovery of images is usually posed as an optimization problem, which is regularized by functions enforcing sparsity, smoothness or low rank structure. Recently structured matrix priors have gained prominence in many MRI applications because of their superior performance over the aforementioned conventional priors. However, none of them are designed to exploit the smooth exponential structure of the 3D dataset. In this thesis, we exploit the exponential structure of the signal at every pixel location and the spatial smoothness of the parameters to derive a 3D annihilation relation in the Fourier domain. This relation translates into a product of a Hankel/Toeplitz structured matrix, formed from the k-t samples, and a vector of filter coefficients. We show that this matrix has a low rank structure, which is exploited to recover the images from undersampled measurements. We demonstrate the proposed method on the problem of MR parameter mapping. We compare the algorithm with the state-of-the-art methods and observe that the proposed reconstructions and parameter maps have fewer artifacts and errors. We extend the structured low rank framework to correct field inhomogeneity artifacts in MR images. We introduce novel approaches for field map compensation for data acquired using Cartesian and non-Cartesian trajectories. We adopt the time segmentation approach and reformulate the artifact correction problem into a recovery of time series of images from undersampled measurements. Upon recovery, the first image of the series will correspond to the distortion-free image. With the above re-formulation, we can assume that the signal at every pixel follows an exponential signal characterized by field map and the damping constant R2*. We exploit the smooth exponential structure of the 3D dataset to derive a low rank structured matrix prior, similar to the parameter mapping case. We demonstrate the algorithm on spherical MR phantom and human data and show that the artifacts are greatly reduced compared to the uncorrected images. Finally, we develop a structured matrix recovery framework to accelerate cardiac breath-held MRI. We model the cardiac image data as a 3D piecewise constant function. We assume that the zeros of a 3D trigonometric polynomial coincides with the edges of the image data, resulting in a Fourier domain annihilation relation. This relation can be compactly expressed in terms of a structured low rank matrix. We exploit this low rank property to recover the cardiac images from undersampled measurements. We demonstrate the superiority of the proposed technique over conventional sparsity and smoothness based methods. Though the model assumed here is not exponential, yet the proposed algorithm is closely related to that developed for parameter mapping. The direct implementation of the algorithms has a high memory demand and computational complexity due to the formation and storage of a large multi-fold Toeplitz matrix. Till date, the practical utility of such algorithms on high dimensional datasets has been limited due to the aforementioned reasons. We address these issues by introducing novel Fourier domain approximations which result in a fast and memory efficient algorithm for the above-mentioned applications. Such approximations allow us to work with large datasets efficiently and eliminate the need to store the Toeplitz matrix. We note that the algorithm developed for exponential recovery is general enough to be applied to other applications beyond MRI.

In many scientific studies, the response variable bears a generalized nonlinear regression relationship with a certain covariate of interest, which may, however, be confounded by other covariates with unknown functional form. We propose a new class of models, the partly parametric generalized additive model (PPGAM) for doing generalized nonlinear regression with the confounding covariate effects adjusted nonparametrically. To avoid the curse of dimensionality, the PPGAM specifies that, conditional on the covariates, the response distribution belongs to the exponential family with the mean linked to an additive predictor comprising a nonlinear parametric function that is of main interest, plus additive, smooth functions of other covariates. The PPGAM extends both the generalized additive model (GAM) and the generalized nonlinear regression model. We propose to estimate a PPGAM by the method of penalized likelihood. We derive some asymptotic properties of the penalized likelihood estimator, including consistency and asymptotic normality of the parametric estimator of the nonlinear regression component. We propose a model selection criterion for the PPGAM, which resembles the BIC. We illustrate the new methodologies by simulations and real applications. We have developed an R package PPGAM that implements the methodologies expounded herein.

In the development of any product to perform a specific function the first concern of the engineer is to design for satisfactory operation. Engineers originally approached the reliability problem by using excessive safety factors to be assured the structure or material would withstand the calculated loads and stresses. The engineer also learned from operating or testing the equipment until failures occurred and then redesigning as mistakes became apparent. These methods were time consuming and often resulted in bulky over designed products. These approaches became impractical with the advent of new technological advancements. The accelerated industrial development of aircraft, missiles, and modern electronics coupled with a need for a drastic reduction in weight and size magnified the problem. As products became more complex the problem of building a reliable product was intensified. An appreciation for the increase in complexity can be gained from considering that in a period of fifteen years the requirements for electronic tubes on a U.S. Navy destroyer changed from sixty to thirty-six hundred (14). During World War II new equipment was developed that had to be operational for extended period of time if the military mission was to be accomplished. The addition of a time requirement added to the already difficult problem caused by the increasing complexity of equipment. It soon became obvious that new techniques had to be developed that would assist the manufacturer in designing a reliable product.

Consider either (i) f = f1 ⊠ f2 for two Maass cusp forms for SLm(ℤ) and SLm′(ℤ), respectively, with 2 ≤ m ≤ m′, or (ii) f= f1 ⊠ f2 ⊠ f3 for three weight 2k holomorphic cusp forms for SL2(ℤ). Let λf(n) be the normalized coefficients of the associated L-function L(s, f), which is either (i) the Rankin-Selberg L-function L(s, f1 ×f2), or (ii) the Rankin triple product L-function L(s, f1 ×f2 ×f3). First, we derive a Voronoi-type summation formula for λf (n) involving the Meijer G-function. As an application we obtain the asymptotics for the smoothly weighted average of λf (n) against e(αnβ), i.e. the asymptotics for the associated resonance sums. Let ℓ be the degree of L(s, f). When β = 1/ℓ and α is close or equal to ±ℓq 1/ℓ for a positive integer q, the average has a main term of size |λf (q)|X 1/2ℓ+1/2 . Otherwise, when α is fixed and 0 < β < 1/ℓ it is shown that this average decays rapidly. Similar results have been established for individual SLm(ℤ) automorphic cusp forms and are due to the oscillatory nature of the coefficients λf (n).

Two methods of analyzing multi-dimensional frequency data are detailed. The Second Order Exponential (SOE) model is applicable for dichotomous classifications. The distribution has two sets of parameters, ϴi's and ϴj's. The ϴi's are interpreted as the log of the odds of the marginal probabilities if no two factor relationships exist. Or if all ϴij are not zero, then the ϴi's are analogous to a main effect in a 2m factorial analysis, (m = number of factors or classifications). The ϴif's may be interpreted as a measure and direction of the two factor relationships. These ϴij are analogous to partial or adjusted phi-coefficients. The second method discussed assumes a multinomial distribution and the statistics are developed from an Information Theoretic Approach. Each hypothesis is tested using twice the minimum discrimination information statistic (m.d.i.s), 2I. From the null hypothesis it is possible to estimate unique cell probabilities by an iterative metod. Then 2 is equal to 2 (sample frequencies) log (sample frequencies) - 2 (expected frequencies) log (expected frequencies). (141 pages)

In this thesis a method for direct evaluation of Generalized B-splines (GB-splines) via the representation of these curves as piecewise functions is presented. A local structure is introduced that makes the GB-spline curves more amenable to the integration used in constructing bases of higher degree. This basis is used to perform direct computation of piecewise representation of GB-spline bases and curves. Algorithms for refinement using these local structures are also developed.