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Regressão não-paramétrica com erros correlacionados via ondaletas.; Non-parametric regression with correlated errors using wavelets

Porto, Rogério de Faria
Fonte: Biblioteca Digitais de Teses e Dissertações da USP Publicador: Biblioteca Digitais de Teses e Dissertações da USP
Tipo: Tese de Doutorado Formato: application/pdf
Publicado em 03/10/2008 PT
Relevância na Pesquisa
46.38%
Nesta tese, são obtidas taxas de convergência a zero, do risco de estimação obtido com regressão não-paramétrica via ondaletas, quando há erros correlacionados. Quatro métodos de regressão não-paramétrica via ondaletas, com delineamento desigualmente espaçado são estudados na presença de erros correlacionados, oriundos de processos estocásticos. São apresentadas condições sobre os erros e adaptações aos procedimentos necessárias à obtenção de taxas de convergência quase minimax, para os estimadores. Sempre que possível são obtidas taxas de convergência para os estimadores no domínio da função, sob condições bastante gerais a respeito da função a ser estimada, do delineamento e da correlação dos erros. Mediante estudos de simulação, são avaliados os comportamentos de alguns métodos propostos quando aplicados a amostras finitas. Em geral sugere-se usar um dos procedimentos estudados, porém aplicando-se limiares por níveis. Como a estimação da variância dos coecientes de detalhes pode ser problemática em alguns casos, também se propõe um procedimento iterativo semi-paramétrico geral para métodos que utilizam ondaletas, na presença de erros em séries temporais.; In this thesis, rates of convergence to zero are obtained for the estimation risk...

Estimação parametrica e semi-parametrica em misturas uniforme-beta generalizada : uma aplicação em dados de microarranjos; Parametric and semi-parametric estimation in uniform-generalized beta mixtures : application in microarray data

Gabriel Coelho Gonçalves de Abreu
Fonte: Biblioteca Digital da Unicamp Publicador: Biblioteca Digital da Unicamp
Tipo: Dissertação de Mestrado Formato: application/pdf
Publicado em 17/01/2007 PT
Relevância na Pesquisa
46.32%
A análise de dados de expressão gênica tem sido de grande importância nas mais variadas áreas do desenvolvimento humano, como agricultura, melhoramento animal e medicina. Apesar dos avanços na área de estatística genética, a análise desse tipo de dados pode ser complexa e de difícil execução. Os investimentos já feitos nos últimos anos em pesquisa laboratorial podem levar a resultados concretos (melhoramento genético, vacinas genéticas, patentes) em pouco tempo, sob a correta interpretação dos resultados. Como a análise é feita em milhares de genes, existem problemas de comparações múltiplas, excedendo substancialmente o valor nominal de cada teste. Atualmente, em biologia, o problema de testes múltiplos se tornou uma norma, e não uma excessão. Assim, soluções sugeridas englobam o controle da taxas de erro, como o FDR (False discovery rate). O estudo da distribuição empírica dos p-valores, obtidos através dos testes estatísticos, pode ser realizado sob um modelo de mistura finita de distribuições beta. Sugere-se a utilização da distribuição beta generalizada com três parâmetros, mais flexível que a beta padrão. Faz-se um estudo da estimação paramétrica e semi-paramétrica no modelo proposto. São feitos estudos de simulação e aplicação a dados reais; The analysis of gene expression data has been of great importance in many fields of human knowledge...

Total Mass TCI driven by Parametric Estimation

silva, mm; sousa, c; sebastiao, r; gama, j; mendonca, t; rocha, p; esteves, s
Fonte: Universidade do Porto Publicador: Universidade do Porto
Tipo: Artigo de Revista Científica
ENG
Relevância na Pesquisa
46.11%
This paper presents the Total Mass Target Controlled Infusion algorithm. The system comprises an On Line tuned Algorithm for Recovery Detection (OLARD) after an initial bolus administration and a Bayesian identification method for parametric estimation based on sparse measurements of the accessible signal. To design the drug dosage profile, two algorithms are here proposed. During the transient phase, an Input Variance Control (IVC) algorithm is used. It is based on the concept of TCI and aims to steer the drug effect to a predefined target value within an a priori fixed interval of time. After the steady state phase is reached the drug dose regimen is controlled by a Total Mass Control (TMC) algorithm. The mass control law for compartmental systems is robust even in the presence of parameter uncertainties. The whole system feasibility has been evaluated for the case of Neuromuscular Blockade (NMB) level and was tested both in simulation and in real cases.

Evidence on Gender Wage Discrimination in Portugal: parametric and semi-parametric approaches

Galego, Aurora; Pereira, João
Fonte: Universidade de Évora Publicador: Universidade de Évora
Tipo: Trabalho em Andamento
ENG
Relevância na Pesquisa
46.35%
In this paper we use two alternative approaches to study the extent of gender wage discrimination in Portugal. Both methods involve the estimation of wage equations for males and females and the Blinder [1973] and Oaxaca [1973] decomposition. However, to take into account possible sample selection bias, we consider both parametric and semi-parametric methods. First, we consider a parametric approach that relies on distributional assumptions about the distribution of the error terms in the model (Vella (1992, 1998) and Wooldridge (1998)). Within this approach, if the distributional assumption is not satisfied, the parameters’ estimates may be inconsistent. Secondly, we apply Li and Wooldridge [2002] semi-parametric estimator, which does not assume any known distribution on the joint distribution of the errors of the wage equation and of the sample selection equation; the distribution has an unknown form and is estimated through non-parametric kernel techniques.We employ micro data for Portugal from the European Community Household Panel (ECHP). The results from both approaches provide evidence in favour of the existence of gender wage discrimination in Portugal. However, the extent of labour market discrimination decreases when sample selection bias corrections are taken into account.

Parametric vs. semi-parametric estimation of the male-female wage gap: an application to France [November 2005]

Breunig, Robert; Rospabe, Sandrine
Fonte: Universidade Nacional da Austrália Publicador: Universidade Nacional da Austrália
Tipo: Working/Technical Paper Formato: 950348 bytes; 350 bytes; application/pdf; application/octet-stream
EN_AU
Relevância na Pesquisa
56.31%
We use a semi-parametric method to decompose the difference in male and female wage densities into two parts–one explained by characteristics and one which is attributable to differences in returns to characteristics. We learn substantially more about the gender wage gap in France through this analysis that we do through parametric techniques which we also employ for comparative purposes. In particular, we find that there are no unexplained differences in male and female earnings distributions in the bottom fifth of the data. Occupation and part-time status are the most important determinants of the wage gap for all workers. In the semi-parametric estimates we find that education plays no role in the wage gap once we account for occupation and part-time status.; no

Parametric vs. semi-parametric estimation of the male-female wage gap: an application to France [March 2007]

Breunig, Robert; Rospabe, Sandrine
Fonte: Centre for Economic Policy Research, RSSS, ANU Publicador: Centre for Economic Policy Research, RSSS, ANU
Tipo: Working/Technical Paper
EN
Relevância na Pesquisa
56.31%
We use a semi-parametric method to decompose the difference in male and female wage densities into two parts-one explained by characteristics and one which is attributable to differences in returns to characteristics. We demonstrate that one learns substantially more about the gender wage gap in France through this analysis than through standard parametric techniques. In particular, we find that there are no unexplained differences in male and female earning distributions in the bottom fifth of the data. Occupation and part-time status are the most important determinants of the wage gap for all workers. In the semi-parametric estimates we find that education plays no role in the wage gap once we account for occupation and part-time status.; no

Fundamental numerical schemes for parameter estimation in computer vision.

Scoleri, Tony
Fonte: Universidade de Adelaide Publicador: Universidade de Adelaide
Tipo: Tese de Doutorado
Publicado em //2008
Relevância na Pesquisa
46.14%
An important research area in computer vision is parameter estimation. Given a mathematical model and a sample of image measurement data, key parameters are sought to encapsulate geometric properties of a relevant entity. An optimisation problem is often formulated in order to find these parameters. This thesis presents an elaboration of fundamental numerical algorithms for estimating parameters of multi-objective models of importance in computer vision applications. The work examines ways to solve unconstrained and constrained minimisation problems from the view points of theory, computational methods, and numerical performance. The research starts by considering a particular form of multi-equation constraint function that characterises a wide class of unconstrained optimisation tasks. Increasingly sophisticated cost functions are developed within a consistent framework, ultimately resulting in the creation of a new iterative estimation method. The scheme operates in a maximum likelihood setting and yields near-optimal estimate of the parameters. Salient features of themethod are that it has simple update rules and exhibits fast convergence. Then, to accommodate models with functional dependencies, two variant of this initial algorithm are proposed. These methods are improved again by reshaping the objective function in a way that presents the original estimation problem in a reduced form. This procedure leads to a novel algorithm with enhanced stability and convergence properties. To extend the capacity of these schemes to deal with constrained optimisation problems...

Non-parametric Estimation of Conditional Moments for Sensitivity Analysis

RATTO MARCO; PAGANO ANDREA; YOUNG Peter C.
Fonte: ELSEVIER SCI LTD Publicador: ELSEVIER SCI LTD
Tipo: Articles in Journals Formato: Printed
ENG
Relevância na Pesquisa
46.23%
In this paper we deal with the non-parametric estimation of conditional moments, useful for applications in global sensitivity analysis (GSA) and in the more general emulation framework. The estimation is based on the State Dependent Parameter (SDP) modelling approach. The estimation of conditional moments of order larger than one allows to identify a wider spectrum of parameter sensitivities with respect to the variance based main effects, like shifts in the variance, skewness or kurtosis of the model output, adding valuable information for the analyst at a small computational cost.; JRC.G.9-Econometrics and applied statistics

Gaussian semi-parametric estimation of fractional cointegration

Velasco, Carlos
Fonte: Blackwell Publicador: Blackwell
Tipo: Artigo de Revista Científica Formato: application/pdf
Publicado em /05/2003 ENG
Relevância na Pesquisa
46.27%
We analyse consistent estimation of the memory parameters of a nonstationary fractionally cointegrated vector time series. Assuming that the cointegrating relationship has substantially less memory than the observed series, we show that a multi-variate Gaussian semi-parametric estimate, based on initial consistent estimates and possibly tapered observations, is asymptotically normal. The estimates of the memory parameters can rely either on original (for stationary errors) or on differenced residuals (for nonstationary errors) assuming only a convergence rate for a preliminary slope estimate. If this rate is fast enough, semi-parametric memory estimates are not affected by the use of residuals and retain the same asymptotic distribution as if the true cointegrating relationship were known. Only local conditions on the spectral densities around zero frequency for linear processes are assumed. We concentrate on a bivariate system but discuss multi-variate generalizations and show the performance of the estimates with simulated and real data.

Non-parametric methods for circular-circular and circular-linear

Carnicero, José Antonio; Wiper, Michael P.; Ausín, Concepción
Fonte: Universidade Carlos III de Madrid Publicador: Universidade Carlos III de Madrid
Tipo: Trabalho em Andamento Formato: application/pdf
Publicado em /04/2011 ENG
Relevância na Pesquisa
46.2%
We present a non-parametric approach for the estimation of the bivariate distribution of two circular variables and the modelling of the joint distribution of a circular and a linear variable. We combine nonparametric estimates of the marginal densities of the circular and linear components with the use of class of nonparametric copulas, known as empirical Bernstein copulas, to model the dependence structure. We derive the necessary conditions to obtain continuous distributions defined on the cylinder for the circular-linear model and on the torus for the circular-circular model. We illustrate these two approaches with two sets of real environmental data

Robust Parametric Functional Component Estimation Using a Divergence Family

Silver, Justin
Fonte: Universidade Rice Publicador: Universidade Rice
Tipo: Thesis; Text Formato: application/pdf
ENG
Relevância na Pesquisa
56.37%
The classical parametric estimation approach, maximum likelihood, while providing maximally efficient estimators at the correct model, lacks robustness. As a modification of maximum likelihood, Huber (1964) introduced M-estimators, which are very general but often ad hoc. Basu et al. (1998) developed a family of density-based divergences, many of which exhibit robustness. It turns out that maximum likelihood is a special case of this general class of divergence functions, which are indexed by a parameter alpha. Basu noted that only values of alpha in the [0,1] range were of interest -- with alpha = 0 giving the maximum likelihood solution and alpha = 1 the L2E solution (Scott, 2001). As alpha increases, there is a clear tradeoff between increasing robustness and decreasing efficiency. This thesis develops a family of robust location and scale estimators by applying Basu's alpha-divergence function to a multivariate partial density component model (Scott, 2004). The usefulness of alpha values greater than 1 will be explored, and the new estimator will be applied to simulated cases and applications in parametric density estimation and regression.

Non-parametric estimation of conditional moments for sensitivity analysis

Ratto, Marco; Pagano, A; Young, Peter C
Fonte: Elsevier Publicador: Elsevier
Tipo: Artigo de Revista Científica
Relevância na Pesquisa
56.36%
In this paper, we consider the non-parametric estimation of conditional moments, which is useful for applications in global sensitivity analysis (GSA) and in the more general emulation framework. The estimation is based on the state-dependent parameter (S

Second order statistics characterization of Hawkes processes and non-parametric estimation

Bacry, Emmanuel; Muzy, Jean-Francois
Fonte: Universidade Cornell Publicador: Universidade Cornell
Tipo: Artigo de Revista Científica
Relevância na Pesquisa
46.42%
We show that the jumps correlation matrix of a multivariate Hawkes process is related to the Hawkes kernel matrix through a system of Wiener-Hopf integral equations. A Wiener-Hopf argument allows one to prove that this system (in which the kernel matrix is the unknown) possesses a unique causal solution and consequently that the second-order properties fully characterize a Hawkes process. The numerical inversion of this system of integral equations allows us to propose a fast and efficient method, which main principles were initially sketched in [Bacry and Muzy, 2013], to perform a non-parametric estimation of the Hawkes kernel matrix. In this paper, we perform a systematic study of this non-parametric estimation procedure in the general framework of marked Hawkes processes. We describe precisely this procedure step by step. We discuss the estimation error and explain how the values for the main parameters should be chosen. Various numerical examples are given in order to illustrate the broad possibilities of this estimation procedure ranging from 1-dimensional (power-law or non positive kernels) up to 3-dimensional (circular dependence) processes. A comparison to other non-parametric estimation procedures is made. Applications to high frequency trading events in financial markets and to earthquakes occurrence dynamics are finally considered.; Comment: 25 pages...

Empirical non-parametric estimation of the Fisher Information

Berisha, Visar; Hero, Alfred O.
Fonte: Universidade Cornell Publicador: Universidade Cornell
Tipo: Artigo de Revista Científica
Relevância na Pesquisa
46.27%
The Fisher information matrix (FIM) is a foundational concept in statistical signal processing. The FIM depends on the probability distribution, assumed to belong to a smooth parametric family. Traditional approaches to estimating the FIM require estimating the probability distribution function (PDF), or its parameters, along with its gradient or Hessian. However, in many practical situations the PDF of the data is not known but the statistician has access to an observation sample for any parameter value. Here we propose a method of estimating the FIM directly from sampled data that does not require knowledge of the underlying PDF. The method is based on non-parametric estimation of an $f$-divergence over a local neighborhood of the parameter space and a relation between curvature of the $f$-divergence and the FIM. Thus we obtain an empirical estimator of the FIM that does not require density estimation and is asymptotically consistent. We empirically evaluate the validity of our approach using two experiments.; Comment: 12 pages

Some Theoretical Results Concerning non-Parametric Estimation by Using a Judgment Post-stratification Sample

Dastbaravarde, Ali; Arghami, Nasser Reza; Sarmad, Majid
Fonte: Universidade Cornell Publicador: Universidade Cornell
Tipo: Artigo de Revista Científica
Relevância na Pesquisa
46.18%
In this paper, some of the properties of non-parametric estimation of the expectation of g(X) (any function of X), by using a Judgment Post-stratification Sample (JPS), are discussed. A class of estimators (including the standard JPS estimator and a JPS estimator proposed by Frey and Feeman (2012, Comput. Stat. Data An.)) is considered. The paper provides mean and variance of the members of this class, and examines their consistency and asymptotic distribution. Specifically, the results are for the estimation of population mean, population variance and CDF. We show that any estimators of the class may be less efficient than Simple Random Sampling (SRS) estimator for small sample sizes. We prove that the relative efficiency of some estimators in the class with respect to Balanced Ranked Set Sampling (BRSS) estimator tends to 1 as the sample size goes to infinity. Furthermore, the standard JPS mean estimator and, Frey and Feeman JPS mean estimator are specifically studied and we show that two estimator have the same asymptotic distribution. For the standard JPS mean estimator, in perfect ranking situations, optimum values of H (the ranking class size), for different sample sizes, are determined non-parametrically for populations that are not heavily skewed or thick tailed. We show that the standard JPS mean estimator may be more efficient than BRSS for large sample sizes...

Non-parametric Estimation approach in statistical investigation of nuclear spectra

Jafarizadeh, M. A.; Fouladi, N.; Sabri, H.; Maleki, B. Rashidian
Fonte: Universidade Cornell Publicador: Universidade Cornell
Tipo: Artigo de Revista Científica
Publicado em 10/12/2011
Relevância na Pesquisa
46.18%
In this paper, Kernel Density Estimation (KDE) as a non-parametric estimation method is used to investigate statistical properties of nuclear spectra. The deviation to regular or chaotic dynamics, is exhibited by closer distances to Poisson or Wigner limits respectively which evaluated by Kullback-Leibler Divergence (KLD) measure. Spectral statistics of different sequences prepared by nuclei corresponds to three dynamical symmetry limits of Interaction Boson Model(IBM), oblate and prolate nuclei and also the pairing effect on nuclear level statistics are analyzed (with pure experimental data). KD-based estimated density function, confirm previous predictions with minimum uncertainty (evaluated with Integrate Absolute Error (IAE)) in compare to Maximum Likelihood (ML)-based method. Also, the increasing of regularity degrees of spectra due to pairing effect is reveal.; Comment: 22 pages, 6 figures

Entropy-based parametric estimation of spike train statistics

Vasquez, J. C.; Cessac, B.; Viéville, T.
Fonte: Universidade Cornell Publicador: Universidade Cornell
Tipo: Artigo de Revista Científica
Relevância na Pesquisa
46.27%
We consider the evolution of a network of neurons, focusing on the asymptotic behavior of spikes dynamics instead of membrane potential dynamics. The spike response is not sought as a deterministic response in this context, but as a conditional probability : "Reading out the code" consists of inferring such a probability. This probability is computed from empirical raster plots, by using the framework of thermodynamic formalism in ergodic theory. This gives us a parametric statistical model where the probability has the form of a Gibbs distribution. In this respect, this approach generalizes the seminal and profound work of Schneidman and collaborators. A minimal presentation of the formalism is reviewed here, while a general algorithmic estimation method is proposed yielding fast convergent implementations. It is also made explicit how several spike observables (entropy, rate, synchronizations, correlations) are given in closed-form from the parametric estimation. This paradigm does not only allow us to estimate the spike statistics, given a design choice, but also to compare different models, thus answering comparative questions about the neural code such as : "are correlations (or time synchrony or a given set of spike patterns...

Parametric estimation of a one-dimensional ballistic random walk in a Markov environment

Andreoletti, Pierre; Loukianova, Dasha; Matias, Catherine
Fonte: Universidade Cornell Publicador: Universidade Cornell
Tipo: Artigo de Revista Científica
Relevância na Pesquisa
46.18%
We focus on the parametric estimation of the distribution of a Markov environment from the observation of a single trajectory of a one-dimensional nearest-neighbor path evolving in this random environment. In the ballistic case, as the length of the path increases, we prove consistency, asymptotic normality and efficiency of the maximum likelihood estimator. Our contribution is two-fold: we cast the problem into the one of parameter estimation in a hidden Markov model (HMM) and establish that the bivariate Markov chain underlying this HMM is positive Harris recurrent. We provide different examples of setups in which our results apply, in particular that of DNA unzipping model, and we give a simple synthetic experiment to illustrate those results.

Parametric and semiparametric estimation of sample selection models: an empirical application to the female labour force in Portugal

Coelho, Danilo; Veiga, Helena; Veszteg, Róbert Ferenc
Fonte: Conselho Superior de Investigações Científicas Publicador: Conselho Superior de Investigações Científicas
Tipo: Documento de trabajo
ENG
Relevância na Pesquisa
46.27%
This paper analyses the Portuguese female labour market using a sample selection model. Our results are presented as a contrast to those in Martins (2001). We offer both parametric and semiparametric results applying a different approach: the normal kernel with local smoothing. Several estimates differ from Martins’ in sign and/or in significance. According to our results, for example, the husband’s wage has positive effect on the wife’s participation in the labour market.

Parametric estimation of capital costs for establishing a coal mine: South Africa case study

Mohutsiwa,M.; Musingwini,C.
Fonte: Journal of the Southern African Institute of Mining and Metallurgy Publicador: Journal of the Southern African Institute of Mining and Metallurgy
Tipo: Artigo de Revista Científica Formato: text/html
Publicado em 01/08/2015 EN
Relevância na Pesquisa
56.27%
Capital cost estimates are important in decisions on whether a project will be approved, mothballed, or abandoned. In South Africa, junior coal miners do not have extensive databases of historical projects from which to estimate capital costs. The purpose of this paper is to establish formulae that can be used for estimating capital costs of developing coal mines in a coal-producing country, using South Africa as a case study. The costs are estimated to an error of magnitude level of -30% to +50%, which is suitable for a concept study level, using a parametric estimation technique. The study uses data from completed coal mining projects from selected coal-producing countries. Three formulae are developed and presented for estimating capital costs of underground bord and pillar, surface shovel and truck, and dragline operations.