Dissertations / Theses on the topic 'Quadratic estimates'
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Feneuil, Joseph. "Analyse harmonique sur les graphes et les groupes de Lie : fonctionnelles quadratiques, transformées de Riesz et espaces de Besov." Thesis, Université Grenoble Alpes (ComUE), 2015. http://www.theses.fr/2015GREAM040/document.
Full textThis thesis is devoted to results in real harmonic analysis in discrete (graphs) or continuous (Lie groups) geometric contexts.Let $\Gamma$ be a graph (a set of vertices and edges) equipped with a discrete laplacian $\Delta=I-P$, where $P$ is a Markov operator.Under suitable geometric assumptions on $\Gamma$, we show the $L^p$ boundedness of fractional Littlewood-Paley functionals. We introduce $H^1$ Hardy spaces of functions and of $1$-differential forms on $\Gamma$, giving several characterizations of these spaces, only assuming the doubling property for the volumes of balls in $\Gamma$. As a consequence, we derive the $H^1$ boundedness of the Riesz transform. Assuming furthermore pointwise upper bounds for the kernel (Gaussian of subgaussian upper bounds) on the iterates of the kernel of $P$, we also establish the $L^p$ boundedness of the Riesz transform for $10$, $1\leq p\leq+\infty$ and $1\leq q\leq +\infty$.These results hold for polynomial as well as for exponential volume growth of balls
Bibinger, Markus. "Estimating the quadratic covariation from asynchronous noisy high-frequency observations." Doctoral thesis, Humboldt-Universität zu Berlin, Mathematisch-Naturwissenschaftliche Fakultät II, 2011. http://dx.doi.org/10.18452/16365.
Full textA nonparametric estimation approach for the quadratic covariation of Itô processes from high-frequency observations with an additive noise is developed. It is proved that a closely related sequence of statistical experiments is locally asymptotically normal (LAN) in the Le Cam sense. By virtue of this property optimal convergence rates and efficiency bounds for asymptotic variances of estimators can be concluded. The proposed nonparametric estimator is founded on a combination of two modern estimation methods devoted to an additive observation noise on the one hand and asynchronous observation schemes on the other hand. We reinvent this Hayashi-Yoshida estimator in a new illustration that can serve as a synchronization method which is possible to adapt for the combined approach. A stable central limit theorem is proved focusing especially on the impact of non-synchronicity on the asymptotic variance. With this preparations on hand, the generalized multiscale estimator for the noisy and asynchronous setting arises. This convenient method for the general model is based on subsampling and multiscale estimation techniques that have been established by Mykland, Zhang and Aït-Sahalia. It preserves valuable features of the synchronization methodology and the estimators to cope with noise perturbation. The central result of the thesis is that the estimation error of the generalized multiscale estimator converges with optimal rate stably in law to a centred mixed normal limiting distribution on fairly general regularity assumptions. For the asymptotic variance a consistent estimator based on time transformed histograms is given making the central limit theorem feasible. In an application study a practicable estimation algorithm including a choice of tuning parameters is tested for its features and finite sample size behaviour. We take account of recent advances on the research field by other authors in comparisons and notes.
Stocker, Toni Clemens. "On the asymptotic properties of the OLS estimator in regression models with fractionally integrated regressors and errors." [S.l. : s.n.], 2008. http://nbn-resolving.de/urn:nbn:de:bsz:352-opus-57370.
Full textBinard, Carole. "Estimation de fonctions de régression : sélection d'estimateurs ridge, étude de la procédure PLS1 et applications à la modélisation de la signature génique du cancer du poumon." Thesis, Nice, 2016. http://www.theses.fr/2016NICE4015.
Full textThis thesis deals with the estimation of a regression function providing the best relationship betweenvariables for which we have some observations. In a first part, we complete a simulation study fortwo automatic selection methods of the ridge parameter. From a more theoretical point of view, wethen present and compare two selection methods of a multiparameter, that is used in an estimationprocedure of a regression function on [0,1]. In a second part, we study the quality of the PLS1estimator through its quadratic risk and, more precisely, the variance term in its bias/variancedecomposition. In a third part, a statistical study is carried out in order to explain the geneticsignature of cancer cells thanks to the genetic signatures of cellular subtypes which compose theassociated tumor stroma
Haddouche, Mohamed Anis. "Estimation d'une matrice d'échelle." Thesis, Normandie, 2019. http://www.theses.fr/2019NORMR058/document.
Full textNumerous results on the estimation of a scale matrix in multivariate analysis are obtained under Gaussian assumption (condition under which it is the covariance matrix). However in such areas as Portfolio management in finance, this assumption is not well adapted. Thus, the family of elliptical symmetric distribution, which contains the Gaussian distribution, is an interesting alternative. In this thesis, we consider the problem of estimating the scale matrix _ of the additif model Yp_m = M + E, under theoretical decision point of view. Here, p is the number of variables, m is the number of observations, M is a matrix of unknown parameters with rank q < p and E is a random noise, whose distribution is elliptically symmetric with covariance matrix proportional to Im x Σ. It is more convenient to deal with the canonical forme of this model where Y is decomposed in two matrices, namely, Zq_p which summarizes the information contained in M, and Un_p, where n = m - q which summarizes the information sufficient to estimate Σ. As the natural estimators of the form ^Σ a = a S (where S = UT U and a is a positive constant) perform poorly when the dimension of variables p and the ratio p=n are large, we propose estimators of the form ^Σa;G = a(S + S S+G(Z; S)) where S+ is the Moore-Penrose inverse of S (which coincides with S-1 when S is invertible). We provide conditions on the correction matrix SS+G(Z; S) such that ^Σa;G improves over ^Σa under the quadratic loss L(Σ; ^Σ) = tr(^ΣΣ‾1 - Ip)² and under the data based loss LS (Σ; ^Σ) = tr(S+Σ(^ΣΣ‾1 - Ip)²).. We adopt a unified approach of the two cases where S is invertible and S is non-invertible. To this end, a new Stein-Haff type identity and calculus on eigenstructure for S are developed. Our theory is illustrated with the large class of orthogonally invariant estimators and with simulations
Goffard, Pierre-Olivier. "Approximations polynomiales de densités de probabilité et applications en assurance." Thesis, Aix-Marseille, 2015. http://www.theses.fr/2015AIXM4026/document.
Full textThis PhD thesis studies numerical methods to approximate the probability density function of random variables governed by compound distributions. These random variables are useful in actuarial science to model the risk of a portfolio of contracts. In ruin theory, the probability of ultimate ruin within the compound Poisson ruin model is the survival function of a geometric compound distribution. The proposed method consists in a projection of the probability density function onto an orthogonal polynomial system. These polynomials are orthogonal with respect to a probability measure that belongs to Natural Exponential Families with Quadratic Variance Function. The polynomiam approximation is compared to other numerical methods that recover the probability density function from the knowledge of the moments or the Laplace transform of the distribution. The polynomial method is then extended in a multidimensional setting, along with the probability density estimator derived from the approximation formula. An aggregation procedure adapted to life insurance portfolios is also described. The method aims at building a portfolio of model points in order to compute the best estimate liabilities in a timely manner and in a way that is compliant with the European directive Solvency II
Gismalla, Yousif Ebtihal. "Performance analysis of spectrum sensing techniques for cognitive radio systems." Thesis, University of Manchester, 2013. https://www.research.manchester.ac.uk/portal/en/theses/performance-analysis-of-spectrum-sensing-techniques-for-cognitive-radio-systems(157fe1af-717c-4705-a649-d809766cf5cb).html.
Full textTanguay, Allison J. "New bilinear estimates for quadratic-derivative nonlinear wave equations in 2+1 dimensions." 2012. https://scholarworks.umass.edu/dissertations/AAI3546060.
Full textMorris, Andrew Jordan. "Local Hardy spaces and quadratic estimates for Dirac type operators on Riemannian manifolds." Phd thesis, 2010. http://hdl.handle.net/1885/8864.
Full textBandara, Lashi. "Geometry and the Kato square root problem." Phd thesis, 2013. http://hdl.handle.net/1885/10690.
Full textChen, Chi Wen, and 陳啟文. "The Optimal Quadratic Estimator for the Variance of Sample Mean." Thesis, 1996. http://ndltd.ncl.edu.tw/handle/76172186825891239907.
Full textLo, Ming-Huang, and 羅明煌. "Bayes estimate of the single factor quadratic response surface under the noninfmative prior." Thesis, 1995. http://ndltd.ncl.edu.tw/handle/47001337830263587154.
Full textAlghamdi, Masheal M. "Semi-Supervised Half-Quadratic Nonnegative Matrix Factorization for Face Recognition." Thesis, 2014. http://hdl.handle.net/10754/317308.
Full textDelpish, Ayesha Nneka Niu Xu-Feng. "A comparison of estimators in hierarchical linear modeling restricted maximum likelihood versus bootstrap via minimum norm quadratic unbiased estimators /." 2006. http://etd.lib.fsu.edu/theses/available/06262006-100559.
Full textAdvisor: Xu-Feng Niu, Florida State University, College of Arts and Sciences, Dept. of Statistics. Title and description from dissertation home page (viewed Sept. 18, 2006). Document formatted into pages; contains ix, 116 pages. Includes bibliographical references.
Fathy, Younis M. [Verfasser]. "Bayes quadratic unbiased estimator of spatial covariance parameters / vorgelegt von Younis M. Fathy." 2006. http://d-nb.info/982408013/34.
Full textHe, Guo-Zhen, and 何國禎. "Optimal Linear Quadratic Estimator and Tracker Designs for Linear Systems with Unknown Disturbances." Thesis, 2016. http://ndltd.ncl.edu.tw/handle/43941320731659090214.
Full text國立中央大學
電機工程學系
104
Improved robust observer-based servo designs are proposed in this thesis for the linear systems subject to unknown disturbances. First, the poles of the error dynamic system of the state observer integrated with the unknown input estimator for the continuous-time minimum phase system subject to unknown input disturbance (UID) are optimally assigned to lie to the left of some vertical line in the s-plane with prescribed degree of relative stability. Similar merit has been also applied to the servo design. Consequently, restrictions on the estimation of UID with low frequencies and servo control for slow time-varying command inputs presented in literature have been released to the cases for the UID with high frequencies and drastic time-varying command inputs, so that a more wide range unknown input estimations and servo designs can be achieved. In contrast with the above-mentioned merits, the proposed approach for the continuous-time systems has been also extended to the discrete-time version for the minimum phase and/or non-minimum phase systems. Especially, the new current-output observer/UID esitmator-based servo design for the discrete-time system with an unknown disturbance is proposed. Furthermore, based on the equivalent input disturbance (EID) principle, the proposed approaches are applicable to the class of mismatched input disturbances.
Hung-JenChen and 陳泓任. "A Robust PI Optimal Linear Quadratic State-Estimate Tracker for Continuous-Time Minimum Phase Systems." Thesis, 2017. http://ndltd.ncl.edu.tw/handle/n75uc2.
Full textKuo-YangLiao and 廖國洋. "New PI Optimal Linear Quadratic State-Estimate Trackers for Non-Square Non-Minimum Phase Systems." Thesis, 2019. http://ndltd.ncl.edu.tw/handle/sre6r6.
Full textZih-WeiLin and 林子為. "A new robust PI-based optimal linear quadratic state-estimate tracker for discrete-time non-square non-minimum phase systems." Thesis, 2017. http://ndltd.ncl.edu.tw/handle/v34xzb.
Full textAugustyniak, Maciej. "Une famille de distributions symétriques et leptocurtiques représentée par la différence de deux variables aléatoires gamma." Thèse, 2008. http://hdl.handle.net/1866/8192.
Full textCraciun, Geanina. "Fonctions de perte en actuariat." Thèse, 2009. http://hdl.handle.net/1866/7878.
Full textNayihouba, Kolobadia Ada. "Essays in dynamic panel data models and labor supply." Thèse, 2019. http://hdl.handle.net/1866/23499.
Full textThis thesis is organized in three chapters. The first two chapters propose a regularization approach to the estimation of two estimators of the dynamic panel data model : the Generalized Method of Moment (GMM) estimator and the Limited Information Maximum Likelihood (LIML) estimator. The last chapter of the thesis is an application of regularization to the estimation of labor supply elasticities using pseudo panel data models. In a dynamic panel data model, the number of moment conditions increases rapidly with the time dimension, resulting in a large dimensional covariance matrix of the instruments. Inverting this large dimensional matrix to compute the estimator leads to poor finite sample properties. To address this issue, we propose a regularization approach to the estimation of such models where a generalized inverse of the covariance matrix of the intruments is used instead of its usual inverse. Three regularization schemes are used : Principal components, Tikhonov which is based on Ridge regression (also called Bayesian shrinkage) and finally Landweber Fridman which is an iterative method. All these methods involve a regularization parameter which is similar to the smoothing parameter in nonparametric regressions. The finite sample properties of the regularized estimator depends on this parameter which needs to be selected between many potential values. In the first chapter (co-authored with Marine Carrasco), we propose the regularized GMM estimator of the dynamic panel data models. Under double asymptotics, we show that our regularized estimators are consistent and asymptotically normal provided that the regularization parameter goes to zero slower than the sample size goes to infinity. We derive a data driven selection of the regularization parameter based on an approximation of the higher-order Mean Square Error and show its optimality. The simulations confirm that regularization improves the properties of the usual GMM estimator. As empirical application, we investigate the effect of financial development on economic growth. In the second chapter (co-authored with Marine Carrasco), we propose the regularized LIML estimator of the dynamic panel data model. The LIML estimator is known to have better small sample properties than the GMM estimator but its implementation becomes problematic when the time dimension of the panel becomes large. We derive the asymptotic properties of the regularized LIML under double asymptotics. A data-driven procedure to select the parameter of regularization is proposed. The good performances of the regularized LIML estimator over the usual (not regularized) LIML estimator, the usual GMM estimator and the regularized GMM estimator are confirmed by the simulations. In the last chapter, I consider the estimation of the labor supply elasticities of Canadian men through a regularization approach. Unobserved heterogeneity and measurement errors on wage and income variables are known to cause endogeneity issues in the estimation of labor supply models. A popular solution to the endogeneity issue is to group data in categories based on observable characteristics and compute the weighted least squares at the group level. This grouping estimator has been proved to be equivalent to instrumental variables (IV) estimator on the individual level data using group dummies as intruments. Hence, in presence of large number of groups, the grouping estimator exhibites a small bias similar to the one of the IV estimator in presence of many instruments. I take advantage of the correspondance between grouping estimators and the IV estimator to propose a regularization approach to the estimation of the model. Using this approach leads to wage elasticities that are substantially different from those obtained through grouping estimators.