Academic literature on the topic 'Non-Asymptotic and robust estimation'
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Journal articles on the topic "Non-Asymptotic and robust estimation"
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Hirose, Kei, and Hiroki Masuda. "Robust Relative Error Estimation." Entropy 20, no. 9 (2018): 632. http://dx.doi.org/10.3390/e20090632.
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Relative error estimation has been recently used in regression analysis. A crucial issue of the existing relative error estimation procedures is that they are sensitive to outliers. To address this issue, we employ the γ -likelihood function, which is constructed through γ -cross entropy with keeping the original statistical model in use. The estimating equation has a redescending property, a desirable property in robust statistics, for a broad class of noise distributions. To find a minimizer of the negative γ -likelihood function, a majorize-minimization (MM) algorithm is constructed. The proposed algorithm is guaranteed to decrease the negative γ -likelihood function at each iteration. We also derive asymptotic normality of the corresponding estimator together with a simple consistent estimator of the asymptotic covariance matrix, so that we can readily construct approximate confidence sets. Monte Carlo simulation is conducted to investigate the effectiveness of the proposed procedure. Real data analysis illustrates the usefulness of our proposed procedure.
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Rudenko, O. G., О. О. Bessonov, N. М. Serdyuk, К. О. Olijnik, and О. S. Romanyuk. "Robust object identification in the presence of non-Gaussian interference." Bionics of Intelligence 2, no. 93 (2019): 7–12. http://dx.doi.org/10.30837/bi.2019.2(93).02.
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The problem of identifying the parameters of a linear object in the presence of non-Gaussian interference is considered based on minimizing a combined functional that combines the properties of OLS and IIS. The conditions for the convergence of the gradient identification algorithm in mean and mean square are determined. Analytical estimates are obtained for non-asymptotic and asymptotic values of the parameter estimation error and the identification accuracy. It is shown that these values of the estimation error and identification accuracy depend on the choice of the mixing parameter.
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Ekundayo, Gbenga, and Ndubuisi Jeffery Jamani. "Estimation of Audit Delay Determinants: Do Outliers and Asymptotic Properties Matter?" European Journal of Business and Management Research 7, no. 5 (2022): 54–62. http://dx.doi.org/10.24018/ejbmr.2022.7.5.1604.
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The overriding objective of the study is to empirically examine if outliers and asymptotic properties of estimators matter in the estimation of audit delay determinants. The study employed the ex-post causal research design and focuses on a sample of ten (10) listed oil and gas firms in Nigeria. Secondary data from the content analysis of annual reports spanning the period 2010-2019 was used for the study. The study investigates if outliers and asymptotic properties matter in estimation outcomes comparing the following estimators; the standard OLS, Bootstrapped OLS and Robust estimators. The outcome of the study revealed that the robust estimator yields results that are significantly different from those of both the OLS and Bootstrapped OLS estimations. This suggests that the failure to address outliers in standard OLS estimations can significantly bias the estimation outcome and may be responsible for the myriad of inconclusive outcomes observed in the extant academic literature. Hence, the study confirms that in the estimation of determinants of audit delay, the considerations of outliers indeed constitute a significant statistical consideration for researchers and even more germane than asymptotic concerns.
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Calderon, Sergio, and Daniel Ordoñez Callamad. "Additive Outliers in Open-Loop Threshold Autoregressive Models: A Simulation Study." Revista Colombiana de Estadística 45, no. 1 (2022): 1–39. http://dx.doi.org/10.15446/rce.v45n1.92965.
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The effect of additive outliers is studied on an adapted non-linearity test and a robust estimation method for autoregressive coefficients in open-loop TAR (threshold autoregressive) models. Through a Monte Carlo experiment, the power and size of the non-linearity test are studied. Regarding the estimation method, the bias and ratio of mean squared errors are compared between the robust estimator and least squares. Simulation exercises are carried out for different percentages of contamination and the proportion of observations on each model regime. Furthermore, the approximation of the univariate normal distribution to the empirical distribution of estimated coefficients is analyzed along with the coverage level of asymptotic confidence intervals for the parameters. Results show that the adapted non-linearity test does not have size distortions, and it has a superior power than its least-squares counterpart when additive outliers are present. On the other hand, the robust estimation method for the autoregressive coefficients has a better mean squared error than least-squares when this type of observations are present. Lastly, the use of the non-linearity test and the estimation method are illustrated through a real example.
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Liu, Jie, Da-Yan Liu, Driss Boutat, Xuefeng Zhang, and Ze-Hao Wu. "Innovative non-asymptotic and robust estimation method using auxiliary modulating dynamical systems." Automatica 152 (June 2023): 110953. http://dx.doi.org/10.1016/j.automatica.2023.110953.
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Fiteni, Inmaculada. "ROBUST ESTIMATION OF STRUCTURAL BREAK POINTS." Econometric Theory 18, no. 2 (2002): 349–86. http://dx.doi.org/10.1017/s0266466602182065.
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This paper proposes robust M-estimators of dynamic linear models with a structural break of unknown location. Rates of convergence and limiting distributions for the estimated shift point and the estimated regression parameters are derived. The analysis is carried out in the framework of possibly dependent observations and also with trending regressors. The asymptotic distribution of the break location estimator is obtained both for fixed magnitude of shift and for shift with magnitude converging to zero as the sample size increases. The latter is essential for the derivation of feasible confidence intervals for the break location. Monte Carlo simulations illustrate the performance of asymptotic inferences in practice.
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Ritov, Ya'acov. "Asymptotic results in robust quasi-bayesian estimation." Journal of Multivariate Analysis 23, no. 2 (1987): 290–302. http://dx.doi.org/10.1016/0047-259x(87)90158-8.
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Poudyal, Chudamani. "ROBUST ESTIMATION OF LOSS MODELS FOR LOGNORMAL INSURANCE PAYMENT SEVERITY DATA." ASTIN Bulletin 51, no. 2 (2021): 475–507. http://dx.doi.org/10.1017/asb.2021.4.
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AbstractThe primary objective of this scholarly work is to develop two estimation procedures – maximum likelihood estimator (MLE) and method of trimmed moments (MTM) – for the mean and variance of lognormal insurance payment severity data sets affected by different loss control mechanism, for example, truncation (due to deductibles), censoring (due to policy limits), and scaling (due to coinsurance proportions), in insurance and financial industries. Maximum likelihood estimating equations for both payment-per-payment and payment-per-loss data sets are derived which can be solved readily by any existing iterative numerical methods. The asymptotic distributions of those estimators are established via Fisher information matrices. Further, with a goal of balancing efficiency and robustness and to remove point masses at certain data points, we develop a dynamic MTM estimation procedures for lognormal claim severity models for the above-mentioned transformed data scenarios. The asymptotic distributional properties and the comparison with the corresponding MLEs of those MTM estimators are established along with extensive simulation studies. Purely for illustrative purpose, numerical examples for 1500 US indemnity losses are provided which illustrate the practical performance of the established results in this paper.
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Vermeulen, Karel, and Stijn Vansteelandt. "Data-Adaptive Bias-Reduced Doubly Robust Estimation." International Journal of Biostatistics 12, no. 1 (2016): 253–82. http://dx.doi.org/10.1515/ijb-2015-0029.
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Abstract Doubly robust estimators have now been proposed for a variety of target parameters in the causal inference and missing data literature. These consistently estimate the parameter of interest under a semiparametric model when one of two nuisance working models is correctly specified, regardless of which. The recently proposed bias-reduced doubly robust estimation procedure aims to partially retain this robustness in more realistic settings where both working models are misspecified. These so-called bias-reduced doubly robust estimators make use of special (finite-dimensional) nuisance parameter estimators that are designed to locally minimize the squared asymptotic bias of the doubly robust estimator in certain directions of these finite-dimensional nuisance parameters under misspecification of both parametric working models. In this article, we extend this idea to incorporate the use of data-adaptive estimators (infinite-dimensional nuisance parameters), by exploiting the bias reduction estimation principle in the direction of only one nuisance parameter. We additionally provide an asymptotic linearity theorem which gives the influence function of the proposed doubly robust estimator under correct specification of a parametric nuisance working model for the missingness mechanism/propensity score but a possibly misspecified (finite- or infinite-dimensional) outcome working model. Simulation studies confirm the desirable finite-sample performance of the proposed estimators relative to a variety of other doubly robust estimators.
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Chen, Haiqiang. "ROBUST ESTIMATION AND INFERENCE FOR THRESHOLD MODELS WITH INTEGRATED REGRESSORS." Econometric Theory 31, no. 4 (2014): 778–810. http://dx.doi.org/10.1017/s0266466614000553.
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This paper studies the robust estimation and inference of threshold models with integrated regressors. We derive the asymptotic distribution of the profiled least squares (LS) estimator under the diminishing threshold effect assumption that the size of the threshold effect converges to zero. Depending on how rapidly this sequence converges, the model may be identified or only weakly identified and asymptotic theorems are developed for both cases. As the convergence rate is unknown in practice, a model-selection procedure is applied to determine the model identification strength and to construct robust confidence intervals, which have the correct asymptotic size irrespective of the magnitude of the threshold effect. The model is then generalized to incorporate endogeneity and serial correlation in error terms, under which, we design a Cochrane–Orcutt feasible generalized least squares (FGLS) estimator which enjoys efficiency gains and robustness against different error specifications, including both I(0) and I(1) errors. Based on this FGLS estimator, we further develop a sup-Wald statistic to test for the existence of the threshold effect. Monte Carlo simulations show that our estimators and test statistics perform well.
Dissertations / Theses on the topic "Non-Asymptotic and robust estimation"
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Sohrabi, Maryam. "On Robust Asymptotic Theory of Unstable AR(p) Processes with Infinite Variance." Thesis, Université d'Ottawa / University of Ottawa, 2016. http://hdl.handle.net/10393/34280.
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In this thesis, we explore some asymptotic results in heavy-tailed theory. There are many empirical and compelling evidence in statistics that require modeling with heavy tailed observations.
This thesis is divided into three parts. First, we consider a robust estimation of the mean vector for a sequence of independent and identically distributed observations in the domain of attraction of a stable law with possibly different indices of stability between 1 and 2. The suggested estimator is asymptotically normal with unknown parameters. We apply an asymptotically valid bootstrap to construct a confidence region for the mean vector. Furthermore, a simulation study is performed to show that the estimation method is efficient for conducting inference about the mean vector for multivariate heavy-tailed observations.
In the second part, we present the asymptotic distribution of M-estimators for
parameters in an unstable AR(p) process. The innovations are assumed to be in
the domain of attraction of a stable law with index 0 < α ≤ 2. In particular,
when the model involves repeated unit roots or conjugate complex unit roots, M-
estimators have a higher asymptotic rate of convergence compared to the least square estimators. Moreover, we show that the asymptotic results can be written as Ito stochastic integrals.
Finally, the preceding methodologies lead to develop the asymptotic theory of M-estimators for parameters in unstable AR(p) processes with nonzero location parameter.
Similar to the preceding cases, we assume that the process is driven by innovations in the domain of attraction of a stable law with index 0 < α ≤ 2. In this thesis, for all models, we also cover the finite variance case (α = 2).
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Danilov, Mikhail. "Robust estimation of multivariate scatter in non-affine equivariant scenarios." Thesis, University of British Columbia, 2010. http://hdl.handle.net/2429/19462.
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We consider the problem of robust estimation of the scatter matrix of an elliptical distribution when observed data are corrupted in a cell-wise manner. The first half of the thesis develops a framework for dealing with data subjected to independent cell-wise contamination. Each data cell (as opposed to data case in traditional robustness) can be contaminated independently of the rest of the case. Instead of downweighting the whole case we attempt to identify the affected cells, remove the offending values and treat them as missing at random for subsequent likelihood-based processing. We explore several variations of the detection procedure that takes into account the multivariate structure of the data and end up with a heuristic algorithm that identifies and removes a large proportion of dangerous independent contamination. Although there are not many existing methods to measure against, the proposed covariance estimate compares favorably to naive alternatives such as pairwise estimates or univariate Winsorising.
The cell-wise data corruption mechanism that we deal with in the second half of this thesis is missing data. Missing data on their own have been well studied and likelihood methods are well developed. The new setting that we are interested in is when missing data come together with the traditional case-wise contamination. Both issues have been studied extensively over that last few decades but little attention has been paid to how to address them both at the same time. We propose a modification of the S-estimate that allows robust estimation of multivariate location and scatter matrix in the presence of missing completely at random (MCAR) data. The method is based on the idea of the maximum likelihood of the observed data and extends it into the world of S-estimates. The estimate comes complete with the computation algorithm, which is an adjusted version of the widely used Fast-S procedure. Simulation results and applications to real datasets confirm the superiority of our method over available alternatives.
Preliminary investigation reported in the concluding chapter suggests that combining the two main ideas presented in this thesis can yield an estimate that is robust against case-wise and cell-wise contamination simultaneously.
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Tamburello, Philip Michael. "Iterative Memoryless Non-linear Estimators of Correlation for Complex-Valued Gaussian Processes that Exhibit Robustness to Impulsive Noise." Diss., Virginia Tech, 2016. http://hdl.handle.net/10919/64785.
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The autocorrelation function is a commonly used tool in statistical time series analysis. Under the assumption of Gaussianity, the sample autocorrelation function is the standard method used to estimate this function given a finite number of observations. Non-Gaussian, impulsive observation noise following probability density functions with thick tails, which often occurs in practice, can bias this estimator, rendering classical time series analysis methods ineffective.
This work examines the robustness of two estimators of correlation based on memoryless nonlinear functions of observations, the Phase-Phase Correlator (PPC) and the Median- of-Ratios Estimator (MRE), which are applicable to complex-valued Gaussian random pro- cesses. These estimators are very fast and easy to implement in current processors. We show that these estimators are robust from a bias perspective when complex-valued Gaussian pro- cesses are contaminated with impulsive noise at the expense of statistical efficiency at the assumed Gaussian distribution. Additionally, iterative versions of these estimators named the IMRE and IPPC are developed, realizing an improved bias performance over their non- iterative counterparts and the well-known robust Schweppe-type Generalized M-estimator utilizing a Huber cost function (SHGM).
An impulsive noise suppression technique is developed using basis pursuit and a priori atom weighting derived from the newly developed iterative estimators. This new technique is proposed as an alternative to the robust filter cleaner, a Kalman filter-like approach that relies on linear prediction residuals to identity and replace corrupted observations. It does not have the same initialization issues as the robust filter cleaner.
Robust spectral estimation methods are developed using these new estimators and impulsive noise suppression techniques. Results are obtained for synthetic complex-valued Guassian processes and real-world digital television signals collected using a software defined radio.<br>Ph. D.
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Yan, Jiajia. "Statistical analysis on diffusion tensor estimation." Thesis, University of Wolverhampton, 2017. http://hdl.handle.net/2436/621860.
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Diffusion tensor imaging (DTI) is a relatively new technology of magnetic resonance imaging, which enables us to observe the insight structure of the human body in vivo and non-invasively. It displays water molecule movement by a 3×3 diffusion tensor at each voxel. Tensor field processing, visualisation and tractography are all based on the diffusion tensors. The accuracy of estimating diffusion tensor is essential in DTI. This research focuses on exploring the potential improvements at the tensor estimation of DTI. We analyse the noise arising in the measurement of diffusion signals. We present robust methods, least median squares (LMS) and least trimmed squares (LTS) regressions, with forward search algorithm that reduce or eliminate outliers to the desired level. An investigation of the criterion to detect outliers is provided in theory and practice. We compare the results with the generalised non-robust models in simulation studies and applicants and also validated various regressions in terms of FA, MD and orientations. We show that the robust methods can handle the data with up to 50% corruption. The robust regressions have better estimations than generalised models in the presence of outliers. We also consider the multiple tensors problems. We review the recent techniques of multiple tensor problems. Then we provide a new model considering neighbours' information, the Bayesian single and double tensor models using neighbouring tensors as priors, which can identify the double tensors effectively. We design a framework to estimate the diffusion tensor field with detecting whether it is a single tensor model or multiple tensor model. An output of this framework is the Bayesian neighbour (BN) algorithm that improves the accuracy at the intersection of multiple fibres. We examine the dependence of the estimators on the FA and MD and angle between two principal diffusion orientations and the goodness of fit. The Bayesian models are applied to the real data with validation. We show that the double tensors model is more accurate on distinct fibre orientations, more anisotropic or similar mean diffusivity tensors. The final contribution of this research is in covariance tensor estimation. We define the median covariance matrix in terms of Euclidean and various non-Euclidean metrics taking its symmetric semi-positive definiteness into account. We compare with estimation methods, Euclidean, power Euclidean, square root Euclidean, log-Euclidean, Riemannian Euclidean and Procrustes median tensors. We provide an analysis of the different metric between different median covariance tensors. We also provide the weighting functions and define the weighted non-Euclidean covariance tensors. We finish with manifold-valued data applications that improve the illustration of DTI images in tensor field processing with defined non-weighted and weighted median tensors. The validation of non-Euclidean methods is studied in the tensor field processing. We show that the root square median estimator is preferable in general, which can effectively exclude outliers and clearly shows the important structures of the brain. The power Euclidean median estimator is recommended when producing FA map.
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Frontera, Pons Joana Maria. "Robust target detection for Hyperspectral Imaging." Thesis, Supélec, 2014. http://www.theses.fr/2014SUPL0024/document.
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L'imagerie hyperspectrale (HSI) repose sur le fait que, pour un matériau donné, la quantité de rayonnement émis varie avec la longueur d'onde. Les capteurs HSI mesurent donc le rayonnement des matériaux au sein de chaque pixel pour un très grand nombre de bandes spectrales contiguës et fournissent des images contenant des informations à la fois spatiale et spectrale. Les méthodes classiques de détection adaptative supposent généralement que le fond est gaussien à vecteur moyenne nul ou connu. Cependant, quand le vecteur moyen est inconnu, comme c'est le cas pour l'image hyperspectrale, il doit être inclus dans le processus de détection. Nous proposons dans ce travail d'étendre les méthodes classiques de détection pour lesquelles la matrice de covariance et le vecteur de moyenne sont tous deux inconnus.Cependant, la distribution statistique multivariée des pixels de l'environnement peut s'éloigner de l'hypothèse gaussienne classiquement utilisée. La classe des distributions elliptiques a été déjà popularisée pour la caractérisation de fond pour l’HSI. Bien que ces modèles non gaussiens aient déjà été exploités dans la modélisation du fond et dans la conception de détecteurs, l'estimation des paramètres (matrice de covariance, vecteur moyenne) est encore généralement effectuée en utilisant des estimateurs conventionnels gaussiens. Dans ce contexte, nous analysons de méthodes d’estimation robuste plus appropriées à ces distributions non-gaussiennes : les M-estimateurs. Ces méthodes de détection couplées à ces nouveaux estimateurs permettent d'une part, d'améliorer les performances de détection dans un environment non-gaussien mais d'autre part de garder les mêmes performances que celles des détecteurs conventionnels dans un environnement gaussien. Elles fournissent ainsi un cadre unifié pour la détection de cibles et la détection d'anomalies pour la HSI<br>Hyperspectral imaging (HSI) extends from the fact that for any given material, the amount of emitted radiation varies with wavelength. HSI sensors measure the radiance of the materials within each pixel area at a very large number of contiguous spectral bands and provide image data containing both spatial and spectral information. Classical adaptive detection schemes assume that the background is zero-mean Gaussian or with known mean vector that can be exploited. However, when the mean vector is unknown, as it is the case for hyperspectral imaging, it has to be included in the detection process. We propose in this work an extension of classical detection methods when both covariance matrix and mean vector are unknown.However, the actual multivariate distribution of the background pixels may differ from the generally used Gaussian hypothesis. The class of elliptical distributions has already been popularized for background characterization in HSI. Although these non-Gaussian models have been exploited for background modeling and detection schemes, the parameters estimation (covariance matrix, mean vector) is usually performed using classical Gaussian-based estimators. We analyze here some robust estimation procedures (M-estimators of location and scale) more suitable when non-Gaussian distributions are assumed. Jointly used with M-estimators, these new detectors allow to enhance the target detection performance in non-Gaussian environment while keeping the same performance than the classical detectors in Gaussian environment. Therefore, they provide a unified framework for target detection and anomaly detection in HSI
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Wang, Zhibo. "Estimations non-asymptotiques et robustes basées sur des fonctions modulatrices pour les systèmes d'ordre fractionnaire." Electronic Thesis or Diss., Bourges, INSA Centre Val de Loire, 2023. http://www.theses.fr/2023ISAB0003.
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Cette thèse développe la méthode des fonctions modulatrices pour des estimations non-asymptotiques et robustes pour des pseudo-états des systèmes nonlinéaires d'ordre fractionnaire, des systèmes linéaires d'ordre fractionnaire avec des accélérations en sortie, et des systèmes à retards d'ordre fractionnaire. Les estimateurs conçus sont fournis en termes de formules intégrales algébriques, ce qui assure une convergence non-asymptotique. Comme une caractéristique essentielle des algorithmes d'estimation conçus, les mesures de sorties bruitées ne sont impliquées que dans les termes intégraux, ce qui confère aux estimateurs une robustesse contre les bruits. Premièrement, pour les systèmes nonlinéaires d'ordre fractionnaire et partiellement inconnu, l'estimation de la dérivée fractionnaire du pseudo-état est abordée via la méthode des fonctions modulatrices. Grâce à la loi de l'indice additif des dérivées fractionnaires, l'estimation est décomposée en une estimation des dérivées fractionnaires de la sortie et une estimation des valeurs initiales fractionnaires. Pendant ce temps, la partie inconnue est estimée via une stratégie innovante de fenêtre glissante. Deuxièmement, pour les systèmes linéaires d'ordre fractionnaire avec des accélérations comme sortie, l'estimation de l'intégrale fractionnaire de l'accélération est d'abord considérée pour les systèmes mécaniques de vibration d'ordre fractionnaire, où seules des mesures d'accélération bruitées sont disponibles. Basée sur des approches numériques existantes qui traitent des intégrales fractionnaires, notre attention se limite principalement à l'estimation des valeurs initiales inconnues en utilisant la méthode des fonctions modulatrices. Sur cette base, le résultat est ensuite généralisé aux systèmes linéaires plus généraux d'ordre fractionnaire. En particulier, le comportement des dérivées fractionnaires à zéro est étudié pour des fonctions absolument continues, ce qui est assez différent de celui de l'ordre entier. Troisièment, pour les systèmes à retards d'ordre fractionnaire, l'estimation du pseudo-état est étudiée en concevant un système dynamique auxiliaire d'ordre fractionnaire, qui fournit un cadre plus général pour générer les fonctions modulatrices requises. Avec l'introduction de l'opérateur de retard et du changement de coordonnées généralisé bicausal, l'estimation du pseudo-état du système considéré peut être réduite à celle de la forme normale correspondante. Contrairement aux travaux précédents le schéma présenté permet une estimation directe du pseudo-état plutôt que d'estimer les dérivées fractionnaires de la sortie et un ensemble de valeurs initiales fractionnaires. De plus, l'efficacité et la robustesse des estimateurs proposés sont vérifiées par des simulations numériques dans cette thèse. Enfin, un résumé de ce travail et un aperçu des travaux futurs sont tirés<br>This thesis develops the modulating functions method for non-asymptotic and robust estimations for fractional-order nonlinear systems, fractional-order linear systems with accelerations as output, and fractional-order time-delay systems. The designed estimators are provided in terms of algebraic integral formulas, which ensure non-asymptotic convergence. As an essential feature of the designed estimation algorithms, noisy output measurements are only involved in integral terms, which endows the estimators with robustness against corrupting noises. First, for fractional-order nonlinear systems which are partially unknown, fractional derivative estimation of the pseudo-state is addressed via the modulating functions method. Thanks to the additive index law of fractional derivatives, the estimation is decomposed into the fractional derivatives estimation of the output and the fractional initial values estimation. Meanwhile, the unknown part is fitted via an innovative sliding window strategy. Second, for fractional-order linear systems with accelerations as output, fractional integral estimation of the acceleration is firstly considered for fractional-order mechanical vibration systems, where only noisy acceleration measurements are available. Based on the existing numerical approaches addressing the proper fractional integrals of accelerations, our attention is primarily restricted to estimating the unknown initial values using the modulating functions method. On this basis, the result is further generalized to more general fractional-order linear systems. In particular, the behaviour of fractional derivatives at zero is studied for absolutely continuous functions, which is quite different from that of integer order. Third, for fractional-order time-delay systems, pseudo-state estimation is studied by designing a fractional-order auxiliary modulating dynamical system, which provides a more general framework for generating the required modulating functions. With the introduction of the delay operator and the bicausal generalized change of coordinates, the pseudo-state estimation of the considered system can be reduced to that of the corresponding observer normal form. In contrast to the previous work, the presented scheme enables direct estimation for the pseudo-state rather than estimating the fractional derivatives of the output and a bunch of fractional initial values. In addition, the efficiency and robustness of the proposed estimators are verified by numerical simulations in this thesis. Finally, a summary of this work and an insight into future work were drawn
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Beltaief, Slim. "Algorithmes optimaux de traitement de données pour des systèmes complexes d'information et télécommunication dans un environnement incertain." Thesis, Normandie, 2017. http://www.theses.fr/2017NORMR056/document.
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Ce travail est consacré au problème d'estimation non paramétrique dans des modèles de régression en temps continu. On considère le problème d'estimation d'une fonction inconnue S supposée périodique. Cette estimation est basée sur des observations générées par un processus stochastique; ces observations peuvent être en temps continu ou discret. Pour ce faire, nous construisons une série d'estimateurs par projection et nous approchons la fonction inconnue S par une série de Fourier finie. Dans cette thèse, nous considérons le problème d'estimation dans le cadre adaptatif, c'est-à-dire le cas où la régularité de la fonction S est inconnue. Pour ce problème, nous développons une nouvelle méthode d'adaptation basée sur la procédure de sélection de modèle proposée par Konev et Pergamenshchikov (2012). Tout d'abord, cette procédure nous donne une famille d'estimateurs ; après nous choisissons le meilleur estimateur possible en minimisant une fonction coût. Nous donnons également une inégalité d'Oracle pour le risque de nos estimateurs et nous donnons la vitesse de convergence minimax<br>This thesis is devoted to the problem of non parametric estimation for continuous-time regression models. We consider the problem of estimating an unknown periodoc function S. This estimation is based on observations generated by a stochastic process; these observations may be in continuous or discrete time. To this end, we construct a series of estimators by projection and thus we approximate the unknown function S by a finite Fourier series. In this thesis we consider the estimation problem in the adaptive setting, i.e. in situation when the regularity of the fonction S is unknown. In this way, we develop a new adaptive method based on the model selection procedure proposed by Konev and Pergamenshchikov (2012). Firstly, this procedure give us a family of estimators, then we choose the best possible one by minimizing a cost function. We give also an oracle inequality for the risk of our estimators and we give the minimax convergence rate
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Liu, Jie. "State Estimation for Linear Singular and Nonlinear Dynamical Systems Based on Observable Canonical Forms." Electronic Thesis or Diss., Bourges, INSA Centre Val de Loire, 2024. http://www.theses.fr/2024ISAB0002.
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Cette thèse a pour objectif, d’une part, de concevoir des estimateurs pour les systèmessinguliers linéaires en utilisant la méthode des fonctions de modulation. D’autrepart, elle vise à développer des observateurs pour une classe de systèmes dynamiquesnon linéaires en utilisant la méthode des formes normales d’observateurs. Pour lessystèmes singuliers, les estimateurs conçus sont présentés sous forme de formulesintégrales algébriques, garantissant une convergence non asymptotique. Une caractéristique essentielle des algorithmes d’estimation conçus est que les mesures bruitées des sorties ne sont impliquées que dans des termes intégraux, conférant ainsi aux estimateurs une robustesse face aux bruits perturbateurs. Pour les systèmes non linéaires, l’idée principale de conception consiste à transformer les systèmes proposés en une forme simplifiée qui supporte les observateurs existants tels que l’observateur à grandgain et l’observateur en mode glissant. Cette forme simple est appelée forme canoniqueobservable dépendant de la sortie auxiliaire.Pour les systèmes singuliers linéaires, nous transformons le système considéré enune forme similaire à la forme canonique observable de Brunovsky en injectant lesdérivées des entrées et des sorties. Tout d’abord, pour les systèmes singuliers linéairesmono-entrée mono-sortie, la condition d’observabilité est proposée. Des formules algébriques avec une fenêtre d’intégration glissante sont obtenues pour les variables dans différentes situations sans connaître la condition initiale du système. Ensuite, pour les systèmes singuliers linéaires à multiples entrées et sorties, une méthode innovante d’estimation non asymptotique et robuste basée sur la forme canonique observable à l’aide d’un ensemble de systèmes dynamiques de modulation auxiliaires est introduite. Ces derniers systèmes auxiliaires sont donnés par la forme canonique observable contrôlable avec des conditions initiales nulles. En introduisant un ensemble de systèmes dynamiques de modulation auxiliaires qui fournit un cadre plus général pour générer les fonctions de modulation requises, des formules intégrales algébriques sont obtenues à la fois pour les variables d’état et les dérivées de sortie. De plus, l’efficacité et la robustesse des estimateurs proposés sont vérifiées par des simulations numériques dans cette thèse.Pour les systèmes dynamiques non linéaires, nous proposons une famille de systèmesdynamiques non linéaires à multiples sorties "prêts à porter" qui peuvent êtretransformés en formes normales d’observateurs dépendant de la sortie auxiliaire, permettant ainsi le support de l’observateur en mode glissant bien connu. Pour cela, aumoyen de la méthode d’extension de dynamique et d’un ensemble des changementsde coordonnées (calculs algébriques intégraux de base), les termes non linéairessont annulés par une dynamique auxiliaire ou remplacés par des fonctions non linéairesdes multiples sorties. Il convient de mentionner que cette procédure est menée à biende manière compréhensible sans recourir aux outils de la géométrie différentielle, cequi est convivial pour ceux qui ne sont pas familiers avec les calculs des crochets deLie. De plus, l’efficacité et la robustesse des observateurs proposés sont vérifiées pardes simulations numériques dans cette thèse. Deuxièmement, une classe plus large desystèmes dynamiques non linéaires à multiples entrées et sorties "prêts à porter" estfournie pour étendre et développer davantage les systèmes proposés dans le premiercas. De manière similaire, au moyen de la dynamique auxiliaire correspondante etd’un ensemble des changements de coordonnées, les systèmes fournis sont convertisen formes normales non linéaires ciblées dépendant à la fois des multiples sorties etdes variables auxiliaires. Naturellement, cette procédure est également réalisée sansrecourir aux outils géométriques. Enfin, des conclusions sont présentées avec quelques perspectives<br>This thesis aims, on the one hand, to design estimators for linear singular systems usingthemethod of modulation functions. On the other hand, it aims to develop observersfor a class of nonlinear dynamical systems using the method of canonical formsof observers. For singular systems, the designed estimators are presented in the formof algebraic integral equations, ensuring non-asymptotic convergence. An essentialcharacteristic of the designed estimation algorithms is that noisy measurements of theoutputs are only involved in integral terms, thereby imparting robustness to the estimatorsagainst perturbing noises. For nonlinear systems, the main design idea is totransform the proposed systems into a simplified form that accommodates existingobservers such as the high-gain observer and the sliding-mode observer. This simpleformis called auxiliary output depending observable canonical form.For the linear singular systems, we transform the considered system into a formsimilar to the Brunovsky’s observable canonical form with the injection of the inputs’and outputs’ derivatives. First, for linear singular systems with single input and singleoutput, the observability condition is proposed. The system’s input-output differentialequation is derived based on the Brunovsky’s observable canonical form. Algebraicformulas with a sliding integration window are obtained for the variables in differentsituations without knowing the system’s initial condition. Second, for linear singular systemswith multiple input and multiple output, an innovative nonasymptotic and robust estimation method based on the observable canonical form by means of a set of auxiliary modulating dynamical systems is introduced. The latter auxiliary systems are given by the controllable observable canonical with zero initial conditions. The proposed method is applied to estimate the states and the output’s derivatives for linear singular system in noisy environment. By introducing a set of auxiliary modulating dynamical systems which provides a more general framework for generating the requiredmodulating functions, algebraic integral formulas are obtained both for the state variables and the output’s derivatives. After giving the solutions of the required auxiliary systems, error analysis in discrete noisy case is addressed, where the provided noise error bound can be used to select design parameters.For the nonlinear dynamical systems, we propose a family of "ready to wear" nonlineardynamical systemswith multiple outputs that can be transformed into the outputauxiliarydepending observer normal forms which can support the well-known slidingmode observer. For this, by means of the so-called dynamics extension method anda set of changes of coordinates (basic algebraic integral computations), the nonlinearterms are canceled by auxiliary dynamics or replaced by nonlinear functions of themultiple outputs. It is worth mentioning that this procedure is finished in a comprehensible way without resort to the tools of differential geometry, which is user-friendly for those who are not familiar with the computations of Lie brackets. In addition, the efficiency and robustness of the proposed observers are verified by numerical simulations in this thesis. Second, a larger class of "ready to wear" nonlinear dynamicalsystems with multiple inputs and multiple outputs are provided to further extend anddevelop the systems proposed in the first case. In a similar way, by means of the corresponding auxiliary dynamics and a set of changes of coordinates, the provided systems are converted into targeted nonlinear observable canonical forms depending on both the multiple outputs and auxiliary variables. Naturally, this procedure is still completed without resort to geometrical tools. Finally, conclusions are outlined with some perspectives
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Herrington, Richard S. "Simulating Statistical Power Curves with the Bootstrap and Robust Estimation." Thesis, University of North Texas, 2001. https://digital.library.unt.edu/ark:/67531/metadc2846/.
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Power and effect size analysis are important methods in the psychological sciences. It is well known that classical statistical tests are not robust with respect to power and type II error. However, relatively little attention has been paid in the psychological literature to the effect that non-normality and outliers have on the power of a given statistical test (Wilcox, 1998). Robust measures of location exist that provide much more powerful tests of statistical hypotheses, but their usefulness in power estimation for sample size selection, with real data, is largely unknown. Furthermore, practical approaches to power planning (Cohen, 1988) usually focus on normal theory settings and in general do not make available nonparametric approaches to power and effect size estimation. Beran (1986) proved that it is possible to nonparametrically estimate power for a given statistical test using bootstrap methods (Efron, 1993). However, this method is not widely known or utilized in data analysis settings. This research study examined the practical importance of combining robust measures of location with nonparametric power analysis. Simulation and analysis of real world data sets are used. The present study found that: 1) bootstrap confidence intervals using Mestimators gave shorter confidence intervals than the normal theory counterpart whenever the data had heavy tailed distributions; 2) bootstrap empirical power is higher for Mestimators than the normal theory counterpart when the data had heavy tailed distributions; 3) the smoothed bootstrap controls type I error rate (less than 6%) under the null hypothesis for small sample sizes; and 4) Robust effect sizes can be used in conjuction with Cohen's (1988) power tables to get more realistic sample sizes given that the data distribution has heavy tails.
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Van, Deventer Petrus Jacobus Uys. "Outliers, influential observations and robust estimation in non-linear regression analysis and discriminant analysis." Doctoral thesis, University of Cape Town, 1993. http://hdl.handle.net/11427/4363.
Full textBooks on the topic "Non-Asymptotic and robust estimation"
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T, Rachev S., and Fabozzi Frank J, eds. Robust and non-robust models in statistics. Nova Science Publishers, 2009.
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Jurečková, Jana. Robust statistical procedures: Asymptotics and interrelations. Wiley, 1996.
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Clarke, Brenton R. Robustness Theory And Application: First edition. Edited by J. Stuart Hunter and Joseph B. Kadane. John Wiley & Sons, 2018.
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Wheelock, David C. Robust non-parametric quantile estimation of efficiency and productivity change in U.S. commercial banking, 1985-2004. Federal Reserve Bank of St. Louis, 2006.
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Cheng, Russell. Non-Standard Parametric Statistical Inference. Oxford University Press, 2017.
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Jana Jurečková and Pranab Kumar Sen. Robust Statistical Procedures: Asymptotics and Interrelations. Wiley-Interscience, 1996.
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Clarke, Brenton R. Robustness Theory and Application. Wiley & Sons, Limited, John, 2018.
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Clarke, Brenton R. Robustness Theory and Application. Wiley & Sons, Incorporated, John, 2018.
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Inverted Pendulum in Control Theory and Robotics: From Theory to New Innovations. Institution of Engineering & Technology, 2017.
Find full textBook chapters on the topic "Non-Asymptotic and robust estimation"
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Bednarski, Tadeusz. "Fréchet Differentiability and Robust Estimation." In Asymptotic Statistics. Physica-Verlag HD, 1994. http://dx.doi.org/10.1007/978-3-642-57984-4_4.
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Kitsos, Christos P., and Christine H. Müller. "Robust Estimation of Non-linear Aspects." In Contributions to Statistics. Physica-Verlag HD, 1995. http://dx.doi.org/10.1007/978-3-662-12516-8_24.
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De Brabanter, Jos, Kristiaan Pelckmans, Johan A. K. Suykens, and Joos Vandewalle. "Robust Cross-Validation Score Function for Non-linear Function Estimation." In Artificial Neural Networks — ICANN 2002. Springer Berlin Heidelberg, 2002. http://dx.doi.org/10.1007/3-540-46084-5_116.
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Sheng, Hu, YangQuan Chen, and TianShuang Qiu. "Non-linear Transform Based Robust Adaptive Latency Change Estimation of Evoked Potentials." In Fractional Processes and Fractional-Order Signal Processing. Springer London, 2012. http://dx.doi.org/10.1007/978-1-4471-2233-3_12.
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Hu, Nan, Weimin Huang, and Surendra Ranganath. "Robust Attentive Behavior Detection by Non-linear Head Pose Embedding and Estimation." In Computer Vision – ECCV 2006. Springer Berlin Heidelberg, 2006. http://dx.doi.org/10.1007/11744078_28.
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Kumar, Kunal, Prince Kumar, and Susmita Kar. "A Non-Iterative Robust Scheme for Static State Estimation Based on S-Estimator Using Complex PMU Measurements." In Renewable Resources and Energy Management. CRC Press, 2023. http://dx.doi.org/10.1201/9781003361312-31.
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Nöll, Tobias, Johannes Köhler, and Didier Stricker. "Robust and Accurate Non-parametric Estimation of Reflectance Using Basis Decomposition and Correction Functions." In Computer Vision – ECCV 2014. Springer International Publishing, 2014. http://dx.doi.org/10.1007/978-3-319-10605-2_25.
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Roensch, Birgit, and Wolfgang Stummer. "Robust Estimation by Means of Scaled Bregman Power Distances. Part I. Non-homogeneous Data." In Lecture Notes in Computer Science. Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-26980-7_33.
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Tavakkoli, Alireza, Mircea Nicolescu, and George Bebis. "Automatic Robust Background Modeling Using Multivariate Non-parametric Kernel Density Estimation for Visual Surveillance." In Advances in Visual Computing. Springer Berlin Heidelberg, 2005. http://dx.doi.org/10.1007/11595755_44.
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Golyanik, Vladislav. "Application of Point Set Registration and Monocular Non-Rigid 3D Reconstruction to Scene Flow Estimation." In Robust Methods for Dense Monocular Non-Rigid 3D Reconstruction and Alignment of Point Clouds. Springer Fachmedien Wiesbaden, 2020. http://dx.doi.org/10.1007/978-3-658-30567-3_10.
Full textConference papers on the topic "Non-Asymptotic and robust estimation"
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Xu, Yanwen, and Pingfeng Wang. "Sequential Sampling Based Reliability Analysis for High Dimensional Rare Events With Confidence Intervals." In ASME 2020 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference. American Society of Mechanical Engineers, 2020. http://dx.doi.org/10.1115/detc2020-22146.
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Abstract Analysis of rare failure events accurately is often challenging with an affordable computational cost in many engineering applications, and this is especially true for problems with high dimensional system inputs. The extremely low probabilities of occurrences for those rare events often lead to large probability estimation errors and low computational efficiency. Thus, it is vital to develop advanced probability analysis methods that are capable of providing robust estimations of rare event probabilities with narrow confidence bounds. Generally, confidence intervals of an estimator can be established based on the central limit theorem, but one of the critical obstacles is the low computational efficiency, since the widely used Monte Carlo method often requires a large number of simulation samples to derive a reasonably narrow confidence interval. This paper develops a new probability analysis approach that can be used to derive the estimates of rare event probabilities efficiently with narrow estimation bounds simultaneously for high dimensional problems. The asymptotic behaviors of the developed estimator has also been proved theoretically without imposing strong assumptions. Further, an asymptotic confidence interval is established for the developed estimator. The presented study offers important insights into the robust estimations of the probability of occurrences for rare events. The accuracy and computational efficiency of the developed technique is assessed with numerical and engineering case studies. Case study results have demonstrated that narrow bounds can be built efficiently using the developed approach, and the true values have always been located within the estimation bounds, indicating that good estimation accuracy along with a significantly improved efficiency.
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Nugroho, Sebastian, Ahmad Taha, and Junjian Qi. "Robust Dynamic State Estimation of Synchronous Machines with Asymptotic State Estimation Error Performance Guarantees." In 2020 IEEE Power & Energy Society General Meeting (PESGM). IEEE, 2020. http://dx.doi.org/10.1109/pesgm41954.2020.9281940.
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Xu, Z. B., J. Y. Yao, Z. L. Dong, and Y. Zheng. "Adaptive Robust Control for Hydraulic Actuators With Disturbance Estimation." In ASME 2015 International Mechanical Engineering Congress and Exposition. American Society of Mechanical Engineers, 2015. http://dx.doi.org/10.1115/imece2015-50133.
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In this paper, an adaptive robust control for hydraulic actuators with disturbance estimation is proposed for a hydraulic system with mismatched generalized uncertainties (e.g., parameter derivations, external disturbances, and/or unmodeled dynamics), in which a finite time disturbance observer and an adaptive robust controller are synthesized via backstepping method. The finite time disturbance observer is designed to estimate the mismatched generalized uncertainties. The adaptive robust controller is designed to handle parametric uncertainties and stabilize the closed loop system. The proposed controller accounts for not only the parametric uncertainties, but also the mismatched generalized uncertainties. Furthermore, the controller theoretically guarantees a prescribed tracking transient performance and final tracking accuracy while achieving asymptotic tracking performance after a finite time T0, which is very important for high accuracy tracking control of hydraulic servo systems. Simulation results are obtained to verify the high performance nature of the proposed control strategy.
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Agamennoni, Gabriel, Stewart Worrall, James Ward, and Eduardo Nebot. "Robust non-linear smoothing for vehicle state estimation." In 2013 IEEE Intelligent Vehicles Symposium (IV). IEEE, 2013. http://dx.doi.org/10.1109/ivs.2013.6629464.
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Fortunati, Stefano, Alexandre Renaux, and Frederic Pascal. "Robust Semiparametric DOA Estimation in non-Gaussian Environment." In 2020 IEEE Radar Conference (RadarConf20). IEEE, 2020. http://dx.doi.org/10.1109/radarconf2043947.2020.9266451.
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Simpkins, Jonathan D., and Robert L. Stevenson. "Robust grid registration for non-blind PSF estimation." In IS&T/SPIE Electronic Imaging, edited by Amir Said, Onur G. Guleryuz, and Robert L. Stevenson. SPIE, 2012. http://dx.doi.org/10.1117/12.909887.
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Tian, Kun, and Hai-hua Yu. "Robust non-fragile fault-tolerant H∞ control for time-delay uncertain linear systems." In 2015 International Conference on Estimation, Detection and Information Fusion (ICEDIF). IEEE, 2015. http://dx.doi.org/10.1109/icedif.2015.7280214.
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Zha, Jingqiang, Junmin Wang, Min Li, Xin Zhang, and Xiao Yu. "Structured Robust Linear Parameter-Varying Vehicle Sideslip Angle Estimation." In ASME 2019 Dynamic Systems and Control Conference. American Society of Mechanical Engineers, 2019. http://dx.doi.org/10.1115/dscc2019-9021.
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Abstract Non-smooth structured robust controller design has drawn a lot of attention recently due to its ability to deal with uncertainty and its convenience for implementation. In this paper, the method is extended to design the structured robust linear parameter-varying (LPV) estimator by pulling out scheduling variables from estimator using linear fractional transformation (LFT). The structured robust LPV estimator is then applied to vehicle sideslip angle estimation. Both the measured vehicle speed and estimated tire cornering stiffness are treated as scheduling variables to further reduce sideslip angle estimation error. The effects of estimator order and number of repetitiveness of scheduling variables are studied using a MATLAB/Simulink bicycle model. The developed approach is later verified in Hardware-in-the-Loop (HIL) simulation environment using dSPACE SCALEXIO and MicroAutoBox. A comprehensive high-fidelity dSPACE automotive simulation models (ASM) vehicle model is used for the real-time HIL simulation. Double-lane change and sine steer maneuvers have been implemented to verify the effectiveness of the structured robust LPV sideslip angle estimation method.
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Mohanty, Amit, and Bin Yao. "Indirect Adaptive Robust Control of Uncertain Systems With Unknown Asymmetric Input Deadband Using a Smooth Inverse." In ASME 2009 Dynamic Systems and Control Conference. ASMEDC, 2009. http://dx.doi.org/10.1115/dscc2009-2771.
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In this paper, we present an indirect adaptive robust controller (IARC) for output tracking of a class of uncertain nonlinear systems with unknown input asymmetric deadband in presence of uncertain nonlinearities and parametric uncertainties. Most of the parameter adaptation algorithms, such as, gradient-type and least squares-type require that the unknown parameters of a system appear in affine with known regressor functions globally. However, deadband nonlinearity can not be represented in those global linear parametric form. Therefore, the existing parameter estimation algorithms for deadband focus on some approximate linear parametric model. Hence, even in absence of any other uncertain nonlinearities and disturbances, these algorithms can never achieve asymptotic tracking. Departing from those approximate deadband estimation, we design an indirect parameter estimation algorithm with online condition monitoring. This parameter estimation algorithm in conjunction with a well-designed robust controller and a deadband inverse function can be used to obtain asymptotic tracking without restoring to discontinuous control law. With this strong result in our repertoire, we proceed to design a smooth deadband inverse (SDI) function to avoid certain problems during implementation, e.g, control input chattering and significant appearance of high-frequency dynamics. The effect of such an approximation on the L2-norm of output tracking error is analytically determined. We also show that while operating away from the deadband, the proposed controller even with an SDI can achieve asymptotic tracking. In presence of disturbances and other uncertain nonlinearities, the proposed IARC controller attains guaranteed transient performance and final tracking accuracy.
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Grondin, Francois, and Francois Michaud. "Robust speech/non-speech discrimination based on pitch estimation for mobile robots." In 2016 IEEE International Conference on Robotics and Automation (ICRA). IEEE, 2016. http://dx.doi.org/10.1109/icra.2016.7487306.
Full textReports on the topic "Non-Asymptotic and robust estimation"
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Wheelock, David C., and Paul W. Wilson. Robust Non-parametric Quantile Estimation of Efficiency and Productivity Change in U.S. Commercial Banking, 1985-2004. Federal Reserve Bank of St. Louis, 2006. http://dx.doi.org/10.20955/wp.2006.041.
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Horowitz, Joel L. Non-asymptotic inference in instrumental variables estimation. The IFS, 2017. http://dx.doi.org/10.1920/wp.cem.2017.4617.
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Horowitz, Joel L. Non-asymptotic inference in instrumental variables estimation. The IFS, 2018. http://dx.doi.org/10.1920/wp.cem.2018.5218.
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Powell, Andrew, and Matteo Bobba. Aid Effectiveness: Politics Matters. Inter-American Development Bank, 2007. http://dx.doi.org/10.18235/0010874.
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The literature on aid effectiveness has focused more on recipient policies than the determinants of aid allocation yet a consistent result is that political allies obtain more aid from donors than non-allies. This paper shows that aid allocated to political allies is ineffective for growth, whereas aid extended to countries that are not allies is highly effective. The result appears to be robust across different specifications and estimation techniques. In particular, new methods are employed to control for endogeneity. The paper suggests that aid allocation should be scrutinized carefully to make aid as effective as possible.
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Schling, Maja, Nicolás Pazos, Leonardo Corral, and Marisol Inurritegui. The Effects of Tenure Security on Women's Empowerment and Food Security: Evidence From a Land Regularization Program in Ecuador. Inter-American Development Bank, 2023. http://dx.doi.org/10.18235/0005355.
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This paper evaluates the impact of a rural land administration program in Ecuador on female empowerment and household food security. Using a double robust estimation that combines the difference-in-difference approach with inverse probability weighting, we explore whether receiving a georeferenced cadastral map of ones parcel provides women with increased bargaining power, empowering them to participate more actively in productive and consumption decision-making that leads to improved diversification of the production portfolio and the households food security. Although we find no significant effects on aggregate levels of empowerment, results show that female beneficiaries became more empowered with regards to access to resources, particularly in terms of applying for and receiving credit. Program participation also significantly affected womens time use, as beneficiary women spent more hours working in non-agricultural activities, investing in their own businesses, and generating off-farm wages. Households who received jointly titled cadastral maps also increased their food security and shifted their production portfolios towards crops and livestock products of both higher market and nutritional value. These results suggest that increasing informal tenure security through cadastral mapping may spur female empowerment, which enables women to increase their bargaining power within the household in order to improve their own and the family's overall welfare.
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Bouezmarni, Taoufik, Mohamed Doukali, and Abderrahim Taamouti. Copula-based estimation of health concentration curves with an application to COVID-19. CIRANO, 2022. http://dx.doi.org/10.54932/mtkj3339.
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COVID-19 has created an unprecedented global health crisis that caused millions of infections and deaths worldwide. Many, however, argue that pre-existing social inequalities have led to inequalities in infection and death rates across social classes, with the most-deprived classes are worst hit. In this paper, we derive semi/non-parametric estimators of Health Concentration Curve (HC) that can quantify inequalities in COVID-19 infections and deaths and help identify the social classes that are most at risk of infection and dying from the virus. We express HC in terms of copula function that we use to build our estimators of HC. For the semi-parametric estimator, a parametric copula is used to model the dependence between health and socio-economic variables. The copula function is estimated using maximum pseudo-likelihood estimator after replacing the cumulative distribution of health variable by its empirical analogue. For the non-parametric estimator, we replace the copula function by a Bernstein copula estimator. Furthermore, we use the above estimators of HC to derive copula-based estimators of health Gini coeffcient. We establish the consistency and the asymptotic normality of HC’s estimators. Using different data-generating processes and sample sizes, a Monte-Carlo simulation exercise shows that the semiparametric estimator outperforms the smoothed nonparametric estimator, and that the latter does better than the empirical estimator in terms of Integrated Mean Squared Error. Finally, we run an extensive empirical study to illustrate the importance of HC’s estimators for investigating inequality in COVID-19 infections and deaths in the U.S. The empirical results show that the inequalities in state’s socio-economic variables like poverty, race/ethnicity, and economic prosperity are behind the observed inequalities in the U.S.’s COVID-19 infections and deaths.
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Zanoni, Wladimir, and Ailin He. Citizenship and the Economic Assimilation of Canadian Immigrants. Inter-American Development Bank, 2021. http://dx.doi.org/10.18235/0003117.
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In this paper, we examine whether acquiring citizenship improves the economic assimilation of Canadian migrants. We took advantage of a natural experiment made possible through changes in the Canadian Citizenship Act of 2014, which extended the physical presence requirement for citizenship from three to four years. Using quasi-experimental methods, we found that delaying citizenship eligibility by one year adversely affected Canadian residents' wages. Access to better jobs explains a citizenship premium of 11 percent in higher wages among naturalized migrants. Our estimates are robust to model specifications, differing sampling windows to form the treatment and comparison groups, and whether the estimator is a non-parametric rather than a parametric one. We discuss how our findings are relevant to the optimal design of naturalization policies regarding efficiency and equity.
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Cattaneo, Matias D., Richard K. Crump, and Weining Wang. Beta-Sorted Portfolios. Federal Reserve Bank of New York, 2023. http://dx.doi.org/10.59576/sr.1068.
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Beta-sorted portfolios—portfolios comprised of assets with similar covariation to selected risk factors—are a popular tool in empirical finance to analyze models of (conditional) expected returns. Despite their widespread use, little is known of their statistical properties in contrast to comparable procedures such as two-pass regressions. We formally investigate the properties of beta-sorted portfolio returns by casting the procedure as a two-step nonparametric estimator with a nonparametric first step and a beta-adaptive portfolios construction. Our framework rationalizes the well-known estimation algorithm with precise economic and statistical assumptions on the general data generating process. We provide conditions that ensure consistency and asymptotic normality along with new uniform inference procedures allowing for uncertainty quantification and general hypothesis testing for financial applications. We show that the rate of convergence of the estimator is non-uniform and depends on the beta value of interest. We also show that the widely used Fama-MacBeth variance estimator is asymptotically valid but is conservative in general and can be very conservative in empirically relevant settings. We propose a new variance estimator, which is always consistent and provide an empirical implementation which produces valid inference. In our empirical application we introduce a novel risk factor—a measure of the business credit cycle—and show that it is strongly predictive of both the cross-section and time-series behavior of U.S. stock returns.
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Lee, W. S., Victor Alchanatis, and Asher Levi. Innovative yield mapping system using hyperspectral and thermal imaging for precision tree crop management. United States Department of Agriculture, 2014. http://dx.doi.org/10.32747/2014.7598158.bard.
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Original objectives and revisions – The original overall objective was to develop, test and validate a prototype yield mapping system for unit area to increase yield and profit for tree crops. Specific objectives were: (1) to develop a yield mapping system for a static situation, using hyperspectral and thermal imaging independently, (2) to integrate hyperspectral and thermal imaging for improved yield estimation by combining thermal images with hyperspectral images to improve fruit detection, and (3) to expand the system to a mobile platform for a stop-measure- and-go situation. There were no major revisions in the overall objective, however, several revisions were made on the specific objectives. The revised specific objectives were: (1) to develop a yield mapping system for a static situation, using color and thermal imaging independently, (2) to integrate color and thermal imaging for improved yield estimation by combining thermal images with color images to improve fruit detection, and (3) to expand the system to an autonomous mobile platform for a continuous-measure situation. Background, major conclusions, solutions and achievements -- Yield mapping is considered as an initial step for applying precision agriculture technologies. Although many yield mapping systems have been developed for agronomic crops, it remains a difficult task for mapping yield of tree crops. In this project, an autonomous immature fruit yield mapping system was developed. The system could detect and count the number of fruit at early growth stages of citrus fruit so that farmers could apply site-specific management based on the maps. There were two sub-systems, a navigation system and an imaging system. Robot Operating System (ROS) was the backbone for developing the navigation system using an unmanned ground vehicle (UGV). An inertial measurement unit (IMU), wheel encoders and a GPS were integrated using an extended Kalman filter to provide reliable and accurate localization information. A LiDAR was added to support simultaneous localization and mapping (SLAM) algorithms. The color camera on a Microsoft Kinect was used to detect citrus trees and a new machine vision algorithm was developed to enable autonomous navigations in the citrus grove. A multimodal imaging system, which consisted of two color cameras and a thermal camera, was carried by the vehicle for video acquisitions. A novel image registration method was developed for combining color and thermal images and matching fruit in both images which achieved pixel-level accuracy. A new Color- Thermal Combined Probability (CTCP) algorithm was created to effectively fuse information from the color and thermal images to classify potential image regions into fruit and non-fruit classes. Algorithms were also developed to integrate image registration, information fusion and fruit classification and detection into a single step for real-time processing. The imaging system achieved a precision rate of 95.5% and a recall rate of 90.4% on immature green citrus fruit detection which was a great improvement compared to previous studies. Implications – The development of the immature green fruit yield mapping system will help farmers make early decisions for planning operations and marketing so high yield and profit can be achieved.