Rozprawy doktorskie na temat „Estimation des microstructures”
Utwórz poprawne odniesienie w stylach APA, MLA, Chicago, Harvard i wielu innych
Sprawdź 20 najlepszych rozpraw doktorskich naukowych na temat „Estimation des microstructures”.
Przycisk „Dodaj do bibliografii” jest dostępny obok każdej pracy w bibliografii. Użyj go – a my automatycznie utworzymy odniesienie bibliograficzne do wybranej pracy w stylu cytowania, którego potrzebujesz: APA, MLA, Harvard, Chicago, Vancouver itp.
Możesz również pobrać pełny tekst publikacji naukowej w formacie „.pdf” i przeczytać adnotację do pracy online, jeśli odpowiednie parametry są dostępne w metadanych.
Przeglądaj rozprawy doktorskie z różnych dziedzin i twórz odpowiednie bibliografie.
Blatt, Samantha Heidi. "From the Mouths of Babes: Using Incremental Enamel Microstructures to Evaluate the Applicability of the Moorrees Method of Dental Formation to the Estimation of Age of Prehistoric Native American Children". The Ohio State University, 2013. http://rave.ohiolink.edu/etdc/view?acc_num=osu1365696693.
Pełny tekst źródłaYang, Zheyi. "Numerical methods to estimate brain micro-structure from diffusion MRI data". Electronic Thesis or Diss., Institut polytechnique de Paris, 2023. http://www.theses.fr/2023IPPAE016.
Pełny tekst źródłaDiffusion magnetic resonance imaging (diffusion MRI) is a widely used non-invasive imaging modality to probe the micro-structural properties of biological tissues below the spatial resolution, by indirectly measuring the diffusion displacement of water molecules. Due to the geometrical complexity of the brain and intricate diffusion MRI mechanism, it is challenging to directly link the received signals to meaningful biophysical parameters, such as axon radii or volume fraction.In recent years, several biophysical models have been introduced to address the issue of weak interpretability. These models represent the diffusion MRI signals as a mixture of analytical signals under certain assumptions, e.g. impermeable membranes, of various disconnected simple geometries, such as spheres and sticks. Subsequently, they aim to extract the parameters of these geometries, which correlate with biophysical parameters, by inverting the analytical expression.However, the validity of these assumptions remains undetermined in actual experiments.The objective of this thesis is to improve the microstructure estimation reliability and efficiency from two perspectives. First, to facilitate the quantitative study of the valid range of biophysical models and the effect of geometrical deformation and cell membrane permeability via simulation, we proposed two reduced models derived from the Bloch-Torrey equation, respectively. For the case of the presence of permeable membranes, a new simulation approach using impermeable Laplace eigenbasis is proposed. As for the geometrical deformation, we use an asymptotic expansion with respect to the deformation angles to approximate the signal. These two reduced models enable efficient computation of signals for various values of deformation/permeability. Numerical simulations reveal that these two models can fast compute the signals within a reasonable error level compared to existing methods. Several studies have been conducted about the effects of permeability and deformation on the signals or the apparent diffusion coefficient (ADC), using the proposed models.Second, instead of inverting a simplified geometries model, we present a novel approach to associate soma size in gray matter by intermediary biomarkers. Numerical simulations identify a correlation between the volume-weighted soma radius/volume fraction and the inflection point of direction-averaged signals at high b-values (b>2500s/mm^2), offering insights for microstructure estimation. We fit a fully connected neural network using these biomarkers and compared to biophysical models, this approach offers comparable results on both synthetic and in vivo data and fast estimation since no inversion is involved
FitzGerald, Charles Michael. "Tooth crown formation and the variation of enamel microstructural growth markers in modern humans". Thesis, University of Cambridge, 1995. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.360038.
Pełny tekst źródłaYevstihnyeyev, Roman. "Estimation of Asset Volatility and Correlation Over Market Microstructure Noise in High-Frequency Data". Thesis, Harvard University, 2015. http://nrs.harvard.edu/urn-3:HUL.InstRepos:14398547.
Pełny tekst źródłaFang, Chengran. "Neuron modeling, Bloch-Torrey equation, and their application to brain microstructure estimation using diffusion MRI". Electronic Thesis or Diss., université Paris-Saclay, 2023. http://www.theses.fr/2023UPASG010.
Pełny tekst źródłaNon-invasively estimating brain microstructure that consists of a very large number of neurites, somas, and glial cells is essential for future neuroimaging. Diffusion MRI (dMRI) is a promising technique to probe brain microstructural properties below the spatial resolution of MRI scanners. Due to the structural complexity of brain tissue and the intricate diffusion MRI mechanism, in vivo microstructure estimation is challenging.Existing methods typically use simplified geometries, particularly spheres, and sticks, to model neuronal structures and to obtain analytical expressions of intracellular signals. The validity of the assumptions made by these methods remains undetermined. This thesis aims to facilitate simulationdriven brain microstructure estimation by replacing simplified geometries with realistic neuron geometry models and the analytical intracellular signal expressions with diffusion MRI simulations. Combined with accurate neuron geometry models, numerical dMRI simulations can give accurate intracellular signals and seamlessly incorporate effects arising from, for instance, neurite undulation or water exchange between soma and neurites.Despite these advantages, dMRI simulations have not been widely adopted due to the difficulties in constructing realistic numerical phantoms, the high computational cost of dMRI simulations, and the difficulty in approximating the implicit mappings between dMRI signals and microstructure properties. This thesis addresses the above problems by making four contributions. First, we develop a high-performance opensource neuron mesh generator and make publicly available over a thousand realistic cellular meshes.The neuron mesh generator, swc2mesh, can automatically and robustly convert valuable neuron tracing data into realistic neuron meshes. We have carefully designed the generator to maintain a good balance between mesh quality and size. A neuron mesh database, NeuronSet, which contains 1213 simulation-ready cell meshes and their neuroanatomical measurements, was built using the mesh generator. These meshes served as the basis for further research. Second, we increased the computational efficiency of the numerical matrix formalism method by accelerating the eigendecomposition algorithm and exploiting GPU computing. The speed was increased tenfold. With similar accuracy, the optimized numerical matrix formalism is 20 times faster than the FEM method and 65 times faster than a GPU-based Monte Carlo method. By performing simulations on realistic neuron meshes, we investigated the effect of water exchange between somas and neurites, and the relationship between soma size and signals. We then implemented a new simulation method that provides a Fourier-like representation of the dMRI signals. This method was derived theoretically and implemented numerically. We validated the convergence of the method and showed that the error behavior is consistent with our error analysis. Finally, we propose a simulation-driven supervised learning framework to estimate brain microstructure using diffusion MRI. By exploiting the powerful modeling and computational capabilities that are mentioned above, we have built a synthetic database containing the dMRI signals and microstructure parameters of 1.4 million artificial brain voxels. We have shown that this database can help approximate the underlying mappings of the dMRI signals to volume and surface fractions using artificial neural networks
Fernandez, Tapia Joaquin. "Modeling, optimization and estimation for the on-line control of trading algorithms in limit-order markets". Thesis, Paris 6, 2015. http://www.theses.fr/2015PA066354/document.
Pełny tekst źródłaThis PhD thesis focuses on the quantitative analysis of mathematical problems arising in the field of optimal algorithmic trading. Concretely, we propose a scientific approach in order to optimize processes related to the capture and provision of liquidity in electronic markets. Because of the strongly industry-focused character of this work, not only we are interested in giving rigorous mathematical results but also to understand this research project in the context of the different stages that come into play during the practical implementation of the tools developed throughout the following chapters (e.g. model interpretation, parameter estimation, programming etc.).From a scientific standpoint the core of our work focuses on two techniques taken from the world of optimization and probability; these are, stochastic control and stochastic approximation. In particular, we provide original academic results for the problem of high frequency market making and the problem of portfolio liquidation by using limit orders; both by using a backward optimization approach. We also propose a forward optimization framework to solve the market making problem; the latter approach being quite innovative for optimal trading, as it opens the door for machine learning techniques.From a practical angle, this PhD thesis seeks to create a bridge between academic research and practitioners. Our mathematical findings are constantly put in perspective in terms of their practical implementation. Hence, we focus a large part of our work on studying the different factors that are of paramount importance to understand when transforming our quantitative techniques into industrial value: understanding the underlying market microstructure, empirical stylized facts, data processing, discussion about the models, limitations of our scientific framework etc
Sun, Yucheng. "Essays in volatility estimation based on high frequency data". Doctoral thesis, Universitat Pompeu Fabra, 2017. http://hdl.handle.net/10803/402831.
Pełny tekst źródłaBasándonos en datos de precios de alta frecuencia, esta tesis se centra en la estimación de la covarianza realizada y la volatilidad integrada de precios de activos, y la aplicación de la estimación de la volatilidad para la detección de saltos en los precios. El primer capítulo utiliza el procedimiento LASSO para regularizar algunos estimadores de matrices de covarianza realizada de alta dimensión. Establecemos propiedades teóricas de los estimadores regularizados que muestran su precisión de estimación y la probabilidad de que revelen correctamente la estructura de red de los activos. En el segundo capítulo se propone un nuevo estimador de la volatilidad integrada que es la variación cuadrática de la parte continua en el proceso de precios. Este estimador se obtiene truncando el estimador de varianza realizado en dos escalas. Demostramos su consistencia en presencia de ruido de microestructura del mercado y saltos de actividad finitos o infinitos en el proceso de precios. El tercer capítulo emplea este estimador para diseñar un test para explorar la existencia de saltos en los precios con ruido.
Tunyavetchakit, Sophon [Verfasser], i Rainer [Akademischer Betreuer] Dahlhaus. "Volatility Decomposition and Nonparametric Estimation of Spot Volatility of Models with Poisson Sampling under Market Microstructure Noise / Sophon Tunyavetchakit ; Betreuer: Rainer Dahlhaus". Heidelberg : Universitätsbibliothek Heidelberg, 2016. http://d-nb.info/1180615786/34.
Pełny tekst źródła[Verfasser], Sophon Tunyavetchakit, i Rainer [Akademischer Betreuer] Dahlhaus. "Volatility Decomposition and Nonparametric Estimation of Spot Volatility of Models with Poisson Sampling under Market Microstructure Noise / Sophon Tunyavetchakit ; Betreuer: Rainer Dahlhaus". Heidelberg : Universitätsbibliothek Heidelberg, 2016. http://nbn-resolving.de/urn:nbn:de:bsz:16-heidok-214504.
Pełny tekst źródłaBornert, Michel. "Morphologie microstructurale et comportement mécanique ; caractérisations expérimentales, approches par bornes et estimations autocohérentes généralisées". Phd thesis, Ecole Nationale des Ponts et Chaussées, 1996. http://tel.archives-ouvertes.fr/tel-00113078.
Pełny tekst źródłaBibinger, 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.
Pełny tekst źródłaA 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.
Kokoszka, Florian. "Estimations du mélange vertical le long de sections hydrologiques en Atlantique Nord". Thesis, Brest, 2012. http://www.theses.fr/2012BRES0097/document.
Pełny tekst źródłaVertical mixing in the ocean contributes to sustain the Meridionnal Overturning. Circulation (MOC) by allowing the renewal of deep waters. A section across the MOC is performed by the hydrological radial OVIDE repeated every two years between Portugal and Greenland since 2002. The energy required for mixing is provided by internal waves generated by wind and tides and micro-structure measurements(VMP) in 2008 show intensified values of dissipation Evmp in the main thermocline and near topographies. Our study is based on these observations and aims tostudy the vertical fine-scale structure of the ocean. Estimates of the dissipation E due to internal waves are made with CTD and LADCP measurements. The comparison with VMP measurements allow us to optimize the parameterization of E by framing the observations by factor 3 and their mean values at ±30%. The systematic application to the OVIDE dataset provides a mapping of the mixing across the basin. Geographical distribution of the vertical diffusion K is similar along the five sections, with values near10−4m2/s in the main thermocline and at the bottom of topographies, and near 10−5m2/s in the ocean interior. Regional differences are present and K can belocally close to 10−3m2/s. Application to FOUREX1997 datas et reveals an increase of K along the Mid-Atlantic Ridge, where the average values are 2 to 3stronger than along OVIDE sections. The spatial distribution of Thorpe scales LT appears to be correlated with internal waves mixing patterns. Nevertheless dissipation estimates based on LT overestimates Evmp by a 10 to 100 factor, which maybe due to misrepresentation of the stage of turbulence development in the ocean. Some mechanisms that can generate internal waves are proposed. Probable sites where tidal generation could occur are located using a simple model of wave beam trajectory. A possible correlation between geostrophic flows and internal waves is considered in the main thermocline. Finally the study of Turnerangles shows that double-diffusion instabilities may bepresent over a large part of the section
Gannac, Yves. "Alliages Fe-6,5%Si élaborés par solidification rapide sous atmosphère controlée : microstructure, propriétés magnétiques et comparaison avec des alliages Fe-Si industriels". Toulouse, INSA, 1992. http://www.theses.fr/1992ISAT0015.
Pełny tekst źródłaAl-Saleh, Mohammad A. "Nonlinear Parameter Estimation for Multiple Site-Type Polyolefin Catalysts Using an Integrated Microstructure Deconvolution Methodology". Thesis, 2011. http://hdl.handle.net/10012/5818.
Pełny tekst źródłaTsai, Yun-Cheng, i 蔡芸琤. "Estimating Realized Variance and True Prices from High-Frequency Data with Microstructure Noise". Thesis, 2016. http://ndltd.ncl.edu.tw/handle/07329595626980843759.
Pełny tekst źródła國立臺灣大學
資訊工程學研究所
104
The market prices and the continuous quadratic variation play critical roles in high-frequency trading. However, the microstructure noise could make the observed prices differ from the true prices and hence bias the estimates of continuous quadratic variation. Following Zhou, we assume the observed prices are the result of adding microstructure noise to the true but hidden prices. Microstructure noise is assumed to be independent and identically distributed (i.i.d.); it is also independent of true prices. Zhang et al. propose a batch estimator for the continuous quadratic variation of high-frequency data in the presence of microstructure noise. It gives the estimates after all the data arrive. This thesis proposes a recursive version of their estimator that outputs variation estimates as the data arrive. The recursive version estimator gives excellent estimates well before all the data arrive. Both real high-frequency futures data and simulation data confirm the performance of recursive estimator. When prices are sampled from a geometric Brownian motion process, the Kalman filter can produce optimal estimates of true prices from the observed prices. However, the covariance matrix of microstructure noise and that of true prices must be known for this claim to hold. In practice, neither covariance matrix is known so they must be estimated. This thesis presents a robust Kalman filter (RKF) to estimate the true prices when microstructure noise is present. The RKF does not need the aforesaid covariance matrices as inputs. Simulation results show that the RKF gives essentially identical estimates to the Kalman filter, which has access to the two above mentioned covariance matrices.
Schmidt-Hieber, Anselm Johannes. "Nonparametric Methods in Spot Volatility Estimation". Doctoral thesis, 2010. http://hdl.handle.net/11858/00-1735-0000-000D-F1CF-6.
Pełny tekst źródłaSolomon, Christopher S. "Estimation and Control of Friction in Bulk Plastic Deformation Process". Thesis, 2018. http://etd.iisc.ac.in/handle/2005/4194.
Pełny tekst źródłaKotchoni, Rachidi. "Efficient estimation using the characteristic function : theory and applications with high frequency data". Thèse, 2010. http://hdl.handle.net/1866/4392.
Pełny tekst źródłaIn estimating the integrated volatility of financial assets using noisy high frequency data, the time series properties assumed for the microstructure noise determines the proper choice of the volatility estimator. In the first chapter of the current thesis, we propose a new model for the microstructure noise with three important features. First of all, our model assumes that the noise is L-dependent. Secondly, the memory lag L is allowed to increase with the sampling frequency. And thirdly, the noise may include an endogenous part, that is, a piece that is correlated with the latent returns. The main difference between this microstructure model and existing ones is that it implies a first order autocorrelation that converges to 1 as the sampling frequency goes to infinity. We use this semi-parametric model to derive a new shrinkage estimator for the integrated volatility. The proposed estimator makes an optimal signal-to-noise trade-off by combining a consistent estimators with an inconsistent one. Simulation results show that the shrinkage estimator behaves better than the best of the two combined ones. We also propose some estimators for the parameters of the noise model. An empirical study based on stocks listed in the Dow Jones Industrials shows the relevance of accounting for possible time dependence in the noise process. Chapters 2, 3 and 4 pertain to the generalized method of moments based on the characteristic function. In fact, the likelihood functions of many financial econometrics models are not known in close form. For example, this is the case for the stable distribution and a discretely observed continuous time model. In these cases, one may estimate the parameter of interest by specifying a moment condition based on the difference between the theoretical (conditional) characteristic function and its empirical counterpart. The challenge is then to exploit the whole continuum of moment conditions hence defined to achieve the maximum likelihood efficiency. This problem has been solved in Carrasco and Florens (2000) who propose the CGMM procedure. The objective function of the CGMM is a quadrqtic form on the Hilbert space defined by the moment function. That objective function depends on a Tikhonov-type regularized inverse of the covariance operator associated with the moment function. Carrasco and Florens (2000) have shown that the estimator obtained by minimizing the proposed objective function is asymptotically as efficient as the maximum likelihood estimator provided that the regularization parameter (α) converges to zero as the sample size goes to infinity. However, the nature of this objective function raises two important questions. First of all, how do we select α in practice? And secondly, how do we implement the CGMM when the multiplicity (d) of the integrals embedded in the objective-function d is large. These questions are tackled in the last three chapters of the thesis. In Chapter 2, we propose to choose α by minimizing the approximate mean square error (MSE) of the estimator. Following an approach similar to Newey and Smith (2004), we derive a higher-order expansion of the estimator from which we characterize the finite sample dependence of the MSE on α. We provide two data-driven methods for selecting the regularization parameter in practice. The first one relies on the higher-order expansion of the MSE whereas the second one uses only simulations. We show that our simulation technique delivers a consistent estimator of α. Our Monte Carlo simulations confirm the importance of the optimal selection of α. The goal of Chapter 3 is to illustrate how to efficiently implement the CGMM for d≤2. To start with, we review the consistency and asymptotic normality properties of the CGMM estimator. Next we suggest some numerical recipes for its implementation. Finally, we carry out a simulation study with the stable distribution that confirms the accuracy of the CGMM as an inference method. An empirical application based on the autoregressive variance Gamma model led to a well-known conclusion: investors require a positive premium for bearing the expected risk while a negative premium is attached to the unexpected risk. In implementing the characteristic function based CGMM, a major difficulty lies in the evaluation of the multiple integrals embedded in the objective function. Numerical quadratures are among the most accurate methods that can be used in the present context. Unfortunately, the number of quadrature points grows exponentially with d. When the data generating process is Markov or dependent, the accurate implementation of the CGMM becomes roughly unfeasible when d≥3. In Chapter 4, we propose a strategy that consists in creating univariate samples by taking a linear combination of the elements of the original vector process. The weights of the linear combinations are drawn from a normalized set of ℝ^{d}. Each univariate index generated in this way is called a frequency domain bootstrap sample that can be used to compute an estimator of the parameter of interest. Finally, all the possible estimators obtained in this fashion can be aggregated to obtain the final estimator. The optimal aggregation rule is discussed in the paper. The overall method is illustrated by a simulation study and an empirical application based on autoregressive Gamma models. This thesis makes an extensive use of the bootstrap, a technique according to which the statistical properties of an unknown distribution can be estimated from an estimate of that distribution. It is thus possible to improve our simulations and empirical results by using the state-of-the-art refinements of the bootstrap methodology.
The attached file is created with Scientific Workplace Latex
(8741097), Ritwik Bandyopadhyay. "ENSURING FATIGUE PERFORMANCE VIA LOCATION-SPECIFIC LIFING IN AEROSPACE COMPONENTS MADE OF TITANIUM ALLOYS AND NICKEL-BASE SUPERALLOYS". Thesis, 2020.
Znajdź pełny tekst źródłaAbsolonová, Karolína. "Histologický odhad dožitého věku jedince ze spálené a nespálené kompaktní kosti lidského žebra". Doctoral thesis, 2012. http://www.nusl.cz/ntk/nusl-309514.
Pełny tekst źródła