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Thesis (MSc (Mathematics))--Stellenbosch University, 2008. / LULU smoothers is a class of nonlinear smoothers and they are compositions of the maximum and minimum operators. By analogy to the discrete Fourier transform and the discrete wavelet transform, one can use LULU smoothers to create a nonlinear multiresolution analysis of a sequence with pulses. This tool is known as the Discrete Pulse Transform (DPT). Some research have been done into the distributional properties of the LULU smoothers. There exist results on the distribution transfers of the basic LULU smoothers, which are the building blocks of the discrete pulse transform. The output distributions of further smoothers used in the DPT, in terms of input distributions, has been a challenging problem. We motivate the use of these smoothers by first considering linear filters as well as the median smoother, which has been very popular in signal and image processing. We give an overview of the attractive properties of the LULU smoothers after which we tackle their output distributions. The main result is the proof of a recursive formula for the output distribution of compositions of LULU smoothers in terms of a given input distribution.

Este trabalho consiste na análise do comportamento do método WAMG (Wavelet-Based Algebraic Multigrid), método numérico de resolução de sistemas de equações lineares desenvolvido no LMAG-Laboratório de Eletromagnetismo Aplicado, com relação a diversos suavizadores. O fato dos vetores que compõem os operadores matriciais Pronlongamento e Restrição do método WAMG serem ortonormais viabiliza uma série de análises teóricas e de dados experimentais, permitindo visualizar características não permitidas nos outros métodos Multigrid (MG), englobando o Multigrid Geométrico (GMG) e o Multigrid Algébrico (AMG). O método WAMG V-Cycle com Filtro Haar é testado em uma variedade de sistemas de equações lineares variando o suavizador, o coeficiente de relaxação nos suavizadores Damped Jacobi e Sobre Relaxação Sucessiva (SOR), e a configuração de pré e pós-suavização. Entre os suavizadores testados, estão os métodos iterativos estacionários Damped Jacobi, SOR, Esparsa Aproximada a Inversa tipo Diagonal (SPAI-0) e métodos propostos com a característica de suavização para-otimizada. A título de comparação, métodos iterativos não estacionários são testados também como suavizadores como Gradientes Conjugados, Gradientes Bi-Conjugados e ICCG. Os resultados dos testes são apresentados e comentados. / This work is comprised of WAMG (Wavelet-Based Algebraic Multigrid) method behavioral analysis based on variety of smoothers, numerical method based on linear equation systems resolution developed at LMAG (Applied Electromagnetism Laboratory). Based on the fact that the vectors represented by WAMG Prolongation and Restriction matrix operators are orthonormals allows the use of a variety of theoretical and practical analysis, and therefore gain visibility of characteristics not feasible through others Multigrid (MG) methods, such as Geometric Multigrid (GMG) and Algebraic Multigrid (AMG). WAMG V-Cycle method with Haar Filter is tested under a variety of linear equation systems, by varying smoothers, relaxation coefficient at Damped Jacobi and Successive Over Relaxation (SOR) smoothers, and pre and post smoothers configurations. The tested smoothers are stationary iterative methods such as Damped Jacobi, SOR, Diagonal type-Sparse Approximate Inverse (SPAI-0) and suggested ones with optimized smoothing characteristic. For comparison purposes, the Conjugate Gradients, Bi-Conjugate Gradient and ICCG non-stationary iterative methods are also tested as smoothers. The testing results are formally presented and commented.

Este trabalho consiste na análise do comportamento do método WAMG (Wavelet-Based Algebraic Multigrid), método numérico de resolução de sistemas de equações lineares desenvolvido no LMAG-Laboratório de Eletromagnetismo Aplicado, com relação a diversos suavizadores. O fato dos vetores que compõem os operadores matriciais Pronlongamento e Restrição do método WAMG serem ortonormais viabiliza uma série de análises teóricas e de dados experimentais, permitindo visualizar características não permitidas nos outros métodos Multigrid (MG), englobando o Multigrid Geométrico (GMG) e o Multigrid Algébrico (AMG). O método WAMG V-Cycle com Filtro Haar é testado em uma variedade de sistemas de equações lineares variando o suavizador, o coeficiente de relaxação nos suavizadores Damped Jacobi e Sobre Relaxação Sucessiva (SOR), e a configuração de pré e pós-suavização. Entre os suavizadores testados, estão os métodos iterativos estacionários Damped Jacobi, SOR, Esparsa Aproximada a Inversa tipo Diagonal (SPAI-0) e métodos propostos com a característica de suavização para-otimizada. A título de comparação, métodos iterativos não estacionários são testados também como suavizadores como Gradientes Conjugados, Gradientes Bi-Conjugados e ICCG. Os resultados dos testes são apresentados e comentados. / This work is comprised of WAMG (Wavelet-Based Algebraic Multigrid) method behavioral analysis based on variety of smoothers, numerical method based on linear equation systems resolution developed at LMAG (Applied Electromagnetism Laboratory). Based on the fact that the vectors represented by WAMG Prolongation and Restriction matrix operators are orthonormals allows the use of a variety of theoretical and practical analysis, and therefore gain visibility of characteristics not feasible through others Multigrid (MG) methods, such as Geometric Multigrid (GMG) and Algebraic Multigrid (AMG). WAMG V-Cycle method with Haar Filter is tested under a variety of linear equation systems, by varying smoothers, relaxation coefficient at Damped Jacobi and Successive Over Relaxation (SOR) smoothers, and pre and post smoothers configurations. The tested smoothers are stationary iterative methods such as Damped Jacobi, SOR, Diagonal type-Sparse Approximate Inverse (SPAI-0) and suggested ones with optimized smoothing characteristic. For comparison purposes, the Conjugate Gradients, Bi-Conjugate Gradient and ICCG non-stationary iterative methods are also tested as smoothers. The testing results are formally presented and commented.

Sequential Monte Carlo (SMC) and Markov chain Monte Carlo (MCMC) methods provide computational tools for systematic inference and learning in complex dynamical systems, such as nonlinear and non-Gaussian state-space models. This thesis builds upon several methodological advances within these classes of Monte Carlo methods.Particular emphasis is placed on the combination of SMC and MCMC in so called particle MCMC algorithms. These algorithms rely on SMC for generating samples from the often highly autocorrelated state-trajectory. A specific particle MCMC algorithm, referred to as particle Gibbs with ancestor sampling (PGAS), is suggested. By making use of backward sampling ideas, albeit implemented in a forward-only fashion, PGAS enjoys good mixing even when using seemingly few particles in the underlying SMC sampler. This results in a computationally competitive particle MCMC algorithm. As illustrated in this thesis, PGAS is a useful tool for both Bayesian and frequentistic parameter inference as well as for state smoothing. The PGAS sampler is successfully applied to the classical problem of Wiener system identification, and it is also used for inference in the challenging class of non-Markovian latent variable models.Many nonlinear models encountered in practice contain some tractable substructure. As a second problem considered in this thesis, we develop Monte Carlo methods capable of exploiting such substructures to obtain more accurate estimators than what is provided otherwise. For the filtering problem, this can be done by using the well known Rao-Blackwellized particle filter (RBPF). The RBPF is analysed in terms of asymptotic variance, resulting in an expression for the performance gain offered by Rao-Blackwellization. Furthermore, a Rao-Blackwellized particle smoother is derived, capable of addressing the smoothing problem in so called mixed linear/nonlinear state-space models. The idea of Rao-Blackwellization is also used to develop an online algorithm for Bayesian parameter inference in nonlinear state-space models with affine parameter dependencies. / CNDM / CADICS

Thesis title: Flexibility, Robustness and Discontinuity in Nonparametric Regression Approaches Author: Mgr. Matúš Maciak, M.Sc. Department: Department of Probability and Mathematical Statistics, Charles University in Prague Supervisor: Prof. RNDr. Marie Hušková, DrSc. huskova@karlin.mff.cuni.cz Abstract: In this thesis we focus on local polynomial estimation approaches of an unknown regression function while taking into account also some robust issues like a presence of outlying observa- tions or heavy-tailed distributions of random errors as well. We will discuss the most common method used for such settings, so called local polynomial M-smoothers and we will present the main statistical properties and asymptotic inference for this method. The M-smoothers method is especially suitable for such cases because of its natural robust flavour, which can nicely deal with outliers as well as heavy-tailed distributed random errors. Another important quality we will focus in this thesis on is a discontinuity issue where we allow for sudden changes (discontinuity points) in the unknown regression function or its derivatives respectively. We will propose a discontinuity model with different variability structures for both independent and dependent random errors while the discontinuity points will be treated in a...