Relevant bibliographies by topics / Inverse estimator

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  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
  5. Conference papers
  6. Reports

Academic literature on the topic 'Inverse estimator'

Author: Grafiati
Published: 4 June 2021
Last updated: 1 February 2022

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Journal articles on the topic "Inverse estimator"

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Karniel, Amir, Ron Meir, and Gideon F. Inbar. "Best estimated inverse versus inverse of the best estimator." Neural Networks 14, no. 9 (2001): 1153–59. http://dx.doi.org/10.1016/s0893-6080(01)00098-3.

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KUNDHI, GUBHINDER, and MARCEL VOIA. "Bootstrap bias correction for average treatment effects with inverse propensity weights." Journal of Statistical Research 52, no. 2 (2019): 187–200. http://dx.doi.org/10.47302/jsr.2018520205.

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The estimated average treatment effect in observational studies is biased if the assumptions of ignorability and overlap are not satisfied. To deal with this potential problem when propensity score weights are used in the estimation of the treatment effects, in this paper we propose a bootstrap bias correction estimator for the average treatment effect (ATE) obtained with the inverse propensity score (BBC-IPS) estimator. We show in simulations that the BBC-IPC performs well when we have misspecifications of the propensity score (PS) due to: omitted variables (ignorability property may not be satisfied), overlap (imbalances in distribution between treatment and control groups) and confounding effects between observables and unobservables (endogeneity). Further refinements in bias reductions of the ATE estimates in smaller samples are attained by iterating the BBC-IPS estimator.
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Balzer, Laura, Jennifer Ahern, Sandro Galea, and Mark van der Laan. "Estimating Effects with Rare Outcomes and High Dimensional Covariates: Knowledge is Power." Epidemiologic Methods 5, no. 1 (2016): 1–18. http://dx.doi.org/10.1515/em-2014-0020.

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AbstractMany of the secondary outcomes in observational studies and randomized trials are rare. Methods for estimating causal effects and associations with rare outcomes, however, are limited, and this represents a missed opportunity for investigation. In this article, we construct a new targeted minimum loss-based estimator (TMLE) for the effect or association of an exposure on a rare outcome. We focus on the causal risk difference and statistical models incorporating bounds on the conditional mean of the outcome, given the exposure and measured confounders. By construction, the proposed estimator constrains the predicted outcomes to respect this model knowledge. Theoretically, this bounding provides stability and power to estimate the exposure effect. In finite sample simulations, the proposed estimator performed as well, if not better, than alternative estimators, including a propensity score matching estimator, inverse probability of treatment weighted (IPTW) estimator, augmented-IPTW and the standard TMLE algorithm. The new estimator yielded consistent estimates if either the conditional mean outcome or the propensity score was consistently estimated. As a substitution estimator, TMLE guaranteed the point estimates were within the parameter range. We applied the estimator to investigate the association between permissive neighborhood drunkenness norms and alcohol use disorder. Our results highlight the potential for double robust, semiparametric efficient estimation with rare events and high dimensional covariates.
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Tan, Z. "Regularized calibrated estimation of propensity scores with model misspecification and high-dimensional data." Biometrika 107, no. 1 (2019): 137–58. http://dx.doi.org/10.1093/biomet/asz059.

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Summary Propensity scores are widely used with inverse probability weighting to estimate treatment effects in observational studies. We study calibrated estimation as an alternative to maximum likelihood estimation for fitting logistic propensity score models. We show that, with possible model misspecification, minimizing the expected calibration loss underlying the calibrated estimators involves reducing both the expected likelihood loss and a measure of relative errors between the limiting and true propensity scores, which governs the mean squared errors of inverse probability weighted estimators. Furthermore, we derive a regularized calibrated estimator by minimizing the calibration loss with a lasso penalty. We develop a Fisher scoring descent algorithm for computing the proposed estimator and provide a high-dimensional analysis of the resulting inverse probability weighted estimators, leveraging the control of relative errors of propensity scores for calibrated estimation. We present a simulation study and an empirical application to demonstrate the advantages of the proposed methods over maximum likelihood and its regularization. The methods are implemented in the R package RCAL.
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Lendle, Samuel David, Bruce Fireman, and Mark J. van der Laan. "Balancing Score Adjusted Targeted Minimum Loss-based Estimation." Journal of Causal Inference 3, no. 2 (2015): 139–55. http://dx.doi.org/10.1515/jci-2012-0012.

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AbstractAdjusting for a balancing score is sufficient for bias reduction when estimating causal effects including the average treatment effect and effect among the treated. Estimators that adjust for the propensity score in a nonparametric way, such as matching on an estimate of the propensity score, can be consistent when the estimated propensity score is not consistent for the true propensity score but converges to some other balancing score. We call this property the balancing score property, and discuss a class of estimators that have this property. We introduce a targeted minimum loss-based estimator (TMLE) for a treatment-specific mean with the balancing score property that is additionally locally efficient and doubly robust. We investigate the new estimator’s performance relative to other estimators, including another TMLE, a propensity score matching estimator, an inverse probability of treatment weighted estimator, and a regression-based estimator in simulation studies.
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Glynn, Adam N., and Kevin M. Quinn. "An Introduction to the Augmented Inverse Propensity Weighted Estimator." Political Analysis 18, no. 1 (2010): 36–56. http://dx.doi.org/10.1093/pan/mpp036.

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In this paper, we discuss an estimator for average treatment effects (ATEs) known as the augmented inverse propensity weighted (AIPW) estimator. This estimator has attractive theoretical properties and only requires practitioners to do two things they are already comfortable with: (1) specify a binary regression model for the propensity score, and (2) specify a regression model for the outcome variable. Perhaps the most interesting property of this estimator is its so-called “double robustness.” Put simply, the estimator remains consistent for the ATE if either the propensity score model or the outcome regression is misspecified but the other is properly specified. After explaining the AIPW estimator, we conduct a Monte Carlo experiment that compares the finite sample performance of the AIPW estimator to three common competitors: a regression estimator, an inverse propensity weighted (IPW) estimator, and a propensity score matching estimator. The Monte Carlo results show that the AIPW estimator has comparable or lower mean square error than the competing estimators when the propensity score and outcome models are both properly specified and, when one of the models is misspecified, the AIPW estimator is superior.
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Ni, Liqiang, and R. Dennis Cook. "A robust inverse regression estimator." Statistics & Probability Letters 77, no. 3 (2007): 343–49. http://dx.doi.org/10.1016/j.spl.2006.07.018.

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Mao, Huzhang, Liang Li, and Tom Greene. "Propensity score weighting analysis and treatment effect discovery." Statistical Methods in Medical Research 28, no. 8 (2018): 2439–54. http://dx.doi.org/10.1177/0962280218781171.

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Inverse probability weighting can be used to estimate the average treatment effect in propensity score analysis. When there is lack of overlap in the propensity score distributions between the treatment groups under comparison, some weights may be excessively large, causing numerical instability and bias in point and variance estimation. We study a class of modified inverse probability weighting estimators that can be used to avoid this problem. These weights cause the estimand to deviate from the average treatment effect. We provide some justification for this deviation from the perspective of treatment effect discovery. We show that when lack of overlap occurs, the modified weights can achieve substantial gains in statistical power compared with inverse probability weighting and other propensity score methods. We develop analytical variance estimates that properly adjust for the sampling variability of the estimated propensity scores, and augment the modified inverse probability weighting estimator with outcome models for improved efficiency, a property that resembles double robustness. Results from extensive simulations and a real data application support our conclusions. The proposed methodology is implemented in R package PSW.
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Moradi, Mohammad, Mohammad Salehi, Jennifer Ann Brown, and Naser Karimi. "Regression estimator under inverse sampling to estimate arsenic contamination." Environmetrics 22, no. 7 (2011): 894–900. http://dx.doi.org/10.1002/env.1116.

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Xiao, Min, Ting Chen, Kunpeng Huang, and Ruixing Ming. "Optimal Estimation for Power of Variance with Application to Gene-Set Testing." Journal of Systems Science and Information 8, no. 6 (2020): 549–64. http://dx.doi.org/10.21078/jssi-2020-549-16.

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Abstract Detecting differential expression of genes in genom research (e.g., 2019-nCoV) is not uncommon, due to the cost only small sample is employed to estimate a large number of variances (or their inverse) of variables simultaneously. However, the commonly used approaches perform unreliable. Borrowing information across different variables or priori information of variables, shrinkage estimation approaches are proposed and some optimal shrinkage estimators are obtained in the sense of asymptotic. In this paper, we focus on the setting of small sample and a likelihood-unbiased estimator for power of variances is given under the assumption that the variances are chi-squared distribution. Simulation reports show that the likelihood-unbiased estimators for variances and their inverse perform very well. In addition, application comparison and real data analysis indicate that the proposed estimator also works well.
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Dissertations / Theses on the topic "Inverse estimator"

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Liu, Yang. "Analysis of Dependently Truncated Sample Using Inverse Probability Weighted Estimator." Digital Archive @ GSU, 2011. http://digitalarchive.gsu.edu/math_theses/110.

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Many statistical methods for truncated data rely on the assumption that the failure and truncation time are independent, which can be unrealistic in applications. The study cohorts obtained from bone marrow transplant (BMT) registry data are commonly recognized as truncated samples, the time-to-failure is truncated by the transplant time. There are clinical evidences that a longer transplant waiting time is a worse prognosis of survivorship. Therefore, it is reasonable to assume the dependence between transplant and failure time. To better analyze BMT registry data, we utilize a Cox analysis in which the transplant time is both a truncation variable and a predictor of the time-to-failure. An inverse-probability-weighted (IPW) estimator is proposed to estimate the distribution of transplant time. Usefulness of the IPW approach is demonstrated through a simulation study and a real application.
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Swain, David James. "Enhancing and Reconstructing Digitized Handwriting." Thesis, Virginia Tech, 1997. http://hdl.handle.net/10919/36904.

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This thesis involves restoration, reconstruction, and enhancement of a digitized library of hand-written documents. Imaging systems that perform this digitization often degrade the quality of the original documents. Many techniques exist for reconstructing, restoring, and enhancing digital images; however, many require <i> a priori </i> knowledge of the imaging system. In this study, only partial <i> a priori </i> knowledge is available, and therefore unknown parameters must be estimated before restoration, reconstruction, or enhancement is possible. The imaging system used to digitize the documents library has degraded the images in several ways. First, it has introduced a ringing that is apparent around each stroke. Second, the system has eliminated strokes of narrow widths. To restore these images, the imaging system is modeled by estimating the point spread function from sample impulse responses, and the image noise is estimated in an attempt to apply standard linear restoration techniques. The applicability of these techniques is investigated in the first part of this thesis. Then nonlinear filters, structural techniques, and enhancement techniques are applied to obtain substantial improvements in image quality.<br>Master of Science
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Pingel, Ronnie. "Some Aspects of Propensity Score-based Estimators for Causal Inference." Doctoral thesis, Uppsala universitet, Statistiska institutionen, 2014. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-229341.

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This thesis consists of four papers that are related to commonly used propensity score-based estimators for average causal effects. The first paper starts with the observation that researchers often have access to data containing lots of covariates that are correlated. We therefore study the effect of correlation on the asymptotic variance of an inverse probability weighting and a matching estimator. Under the assumptions of normally distributed covariates, constant causal effect, and potential outcomes and a logit that are linear in the parameters we show that the correlation influences the asymptotic efficiency of the estimators differently, both with regard to direction and magnitude. Further, the strength of the confounding towards the outcome and the treatment plays an important role. The second paper extends the first paper in that the estimators are studied under the more realistic setting of using the estimated propensity score. We also relax several assumptions made in the first paper, and include the doubly robust estimator. Again, the results show that the correlation may increase or decrease the variances of the estimators, but we also observe that several aspects influence how correlation affects the variance of the estimators, such as the choice of estimator, the strength of the confounding towards the outcome and the treatment, and whether constant or non-constant causal effect is present. The third paper concerns estimation of the asymptotic variance of a propensity score matching estimator. Simulations show that large gains can be made for the mean squared error by properly selecting smoothing parameters of the variance estimator and that a residual-based local linear estimator may be a more efficient estimator for the asymptotic variance. The specification of the variance estimator is shown to be crucial when evaluating the effect of right heart catheterisation, i.e. we show either a negative effect on survival or no significant effect depending on the choice of smoothing parameters.   In the fourth paper, we provide an analytic expression for the covariance matrix of logistic regression with normally distributed regressors. This paper is related to the other papers in that logistic regression is commonly used to estimate the propensity score.
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Coudret, Raphaël. "Stochastic modelling using large data sets : applications in ecology and genetics." Phd thesis, Université Sciences et Technologies - Bordeaux I, 2013. http://tel.archives-ouvertes.fr/tel-00865867.

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There are two main parts in this thesis. The first one concerns valvometry, which is here the study of the distance between both parts of the shell of an oyster, over time. The health status of oysters can be characterized using valvometry in order to obtain insights about the quality of their environment. We consider that a renewal process with four states underlies the behaviour of the studied oysters. Such a hidden process can be retrieved from a valvometric signal by assuming that some probability density function linked with this signal, is bimodal. We then compare several estimators which take this assumption into account, including kernel density estimators.In another chapter, we compare several regression approaches, aiming at analysing transcriptomic data. To understand which explanatory variables have an effect on gene expressions, we apply a multiple testing procedure on these data, through the linear model FAMT. The SIR method may find nonlinear relations in such a context. It is however more commonly used when the response variable is univariate. A multivariate version of SIR was then developed. Procedures to measure gene expressions can be expensive. The sample size n of the corresponding datasets is then often small. That is why we also studied SIR when n is less than the number of explanatory variables p.
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Portier, François. "Réduction de la dimension en régression." Phd thesis, Université Rennes 1, 2013. http://tel.archives-ouvertes.fr/tel-00871049.

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Dans cette thèse, nous étudions le problème de réduction de la dimension dans le cadre du modèle de régression suivant Y=g(B X,e), où X est un vecteur de dimension p, Y appartient à R, la fonction g est inconnue et le bruit e est indépendant de X. Nous nous intéressons à l'estimation de la matrice B, de taille dxp où d est plus petit que p, (dont la connaissance permet d'obtenir de bonnes vitesses de convergence pour l'estimation de g). Ce problème est traité en utilisant deux approches distinctes. La première, appelée régression inverse nécessite la condition de linéarité sur X. La seconde, appelée semi-paramétrique ne requiert pas une telle condition mais seulement que X possède une densité lisse. Dans le cadre de la régression inverse, nous étudions deux familles de méthodes respectivement basées sur E[X f(Y)] et E[XX^T f(Y)]. Pour chacune de ces familles, nous obtenons les conditions sur f permettant une estimation exhaustive de B, aussi nous calculons la fonction f optimale par minimisation de la variance asymptotique. Dans le cadre de l'approche semi-paramétrique, nous proposons une méthode permettant l'estimation du gradient de la fonction de régression. Sous des hypothèses semi-paramétriques classiques, nous montrons la normalité asymptotique de notre estimateur et l'exhaustivité de l'estimation de B. Quel que soit l'approche considérée, une question fondamentale est soulevée : comment choisir la dimension de B ? Pour cela, nous proposons une méthode d'estimation du rang d'une matrice par test d'hypothèse bootstrap.
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Alghamdi, Amani Saeed. "Study of Generalized Lomax Distribution and Change Point Problem." Bowling Green State University / OhioLINK, 2018. http://rave.ohiolink.edu/etdc/view?acc_num=bgsu1526387579759835.

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Solís, Maikol. "Conditional covariance estimation for dimension reduction and sensivity analysis." Toulouse 3, 2014. http://thesesups.ups-tlse.fr/2354/.

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Cette thèse se concentre autour du problème de l'estimation de matrices de covariance conditionnelles et ses applications, en particulier sur la réduction de dimension et l'analyse de sensibilités. Dans le Chapitre 2 nous plaçons dans un modèle d'observation de type régression en grande dimension pour lequel nous souhaitons utiliser une méthodologie de type régression inverse par tranches. L'utilisation d'un opérateur fonctionnel, nous permettra d'appliquer une décomposition de Taylor autour d'un estimateur préliminaire de la densité jointe. Nous prouverons deux choses : notre estimateur est asymptoticalement normale avec une variance que dépend de la partie linéaire, et cette variance est efficace selon le point de vue de Cramér-Rao. Dans le Chapitre 3, nous étudions l'estimation de matrices de covariance conditionnelle dans un premier temps coordonnée par coordonnée, lesquelles dépendent de la densité jointe inconnue que nous remplacerons par un estimateur à noyaux. Nous trouverons que l'erreur quadratique moyenne de l'estimateur converge à une vitesse paramétrique si la distribution jointe appartient à une classe de fonctions lisses. Sinon, nous aurons une vitesse plus lent en fonction de la régularité de la densité de la densité jointe. Pour l'estimateur de la matrice complète, nous allons appliquer une transformation de régularisation de type "banding". Finalement, dans le Chapitre 4, nous allons utiliser nos résultats pour estimer des indices de Sobol utilisés en analyses de sensibilité. Ces indices mesurent l'influence des entrées par rapport a la sortie dans modèles complexes. L'avantage de notre implémentation est d'estimer les indices de Sobol sans l'utilisation de coûteuses méthodes de type Monte-Carlo. Certaines illustrations sont présentées dans le chapitre pour montrer les capacités de notre estimateur<br>This thesis will be focused in the estimation of conditional covariance matrices and their applications, in particular, in dimension reduction and sensitivity analyses. In Chapter 2, we are in a context of high-dimensional nonlinear regression. The main objective is to use the sliced inverse regression methodology. Using a functional operator depending on the joint density, we apply a Taylor decomposition around a preliminary estimator. We will prove two things: our estimator is asymptotical normal with variance depending only the linear part, and this variance is efficient from the Cramér-Rao point of view. In the Chapter 3, we study the estimation of conditional covariance matrices, first coordinate-wise where those parameters depend on the unknown joint density which we will replace it by a kernel estimator. We prove that the mean squared error of the nonparametric estimator has a parametric rate of convergence if the joint distribution belongs to some class of smooth functions. Otherwise, we get a slower rate depending on the regularity of the model. For the estimator of the whole matrix estimator, we will apply a regularization of type "banding". Finally, in Chapter 4, we apply our results to estimate the Sobol or sensitivity indices. These indices measure the influence of the inputs with respect to the output in complex models. The advantage of our implementation is that we can estimate the Sobol indices without use computing expensive Monte-Carlo methods. Some illustrations are presented in the chapter showing the capabilities of our estimator
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Nguyen, Huu Du. "System Reliability : Inference for Common Cause Failure Model in Contexts of Missing Information." Thesis, Lorient, 2019. http://www.theses.fr/2019LORIS530.

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Le bon fonctionnement de l’ensemble d’un système industriel est parfois fortement dépendant de la fiabilité de certains éléments qui le composent. Une défaillance de l’un de ces éléments peut conduire à une défaillance totale du système avec des conséquences qui peuvent être catastrophiques en particulier dans le secteur de l’industrie nucléaire ou dans le secteur de l’industrie aéronautique. Pour réduire ce risque de panne catastrophique, une stratégie consiste à dupliquer les éléments sensibles dans le dispositif. Ainsi, si l’un de ces éléments tombe en panne, un autre pourra prendre le relais et le bon fonctionnement du système pourra être maintenu. Cependant, on observe couramment des situations qui conduisent à des défaillances simultanées d’éléments du système : on parle de défaillance de cause commune. Analyser, modéliser, prédire ce type d’événement revêt donc une importance capitale et sont l’objet des travaux présentés dans cette thèse. Il existe de nombreux modèles pour les défaillances de cause commune. Des méthodes d’inférence pour étudier les paramètres de ces modèles ont été proposées. Dans cette thèse, nous considérons la situation où l’inférence est menée sur la base de données manquantes. Nous étudions en particulier le modèle BFR (Binomial Failure Rate) et la méthode des facteurs alpha. En particulier, une approche bayésienne est développée en s’appuyant sur des techniques algorithmiques (Metropolis, IBF). Dans le domaine du nucléaire, les données de défaillances sont peu abondantes et des techniques particulières d’extrapolations de données doivent être mis en oeuvre pour augmenter l’information. Nous proposons dans le cadre de ces stratégies, des techniques de prédiction des défaillances de cause commune. L’actualité récente a mis en évidence l’importance de la fiabilité des systèmes redondants et nous espérons que nos travaux contribueront à une meilleure compréhension et prédiction des risques de catastrophes majeures<br>The effective operation of an entire industrial system is sometimes strongly dependent on the reliability of its components. A failure of one of these components can lead to the failure of the system with consequences that can be catastrophic, especially in the nuclear industry or in the aeronautics industry. To reduce this risk of catastrophic failures, a redundancy policy, consisting in duplicating the sensitive components in the system, is often applied. When one of these components fails, another will take over and the normal operation of the system can be maintained. However, some situations that lead to simultaneous failures of components in the system could be observed. They are called common cause failure (CCF). Analyzing, modeling, and predicting this type of failure event are therefore an important issue and are the subject of the work presented in this thesis. We investigate several methods to deal with the statistical analysis of CCF events. Different algorithms to estimate the parameters of the models and to make predictive inference based on various type of missing data are proposed. We treat confounded data using a BFR (Binomial Failure Rare) model. An EM algorithm is developed to obtain the maximum likelihood estimates (MLE) for the parameters of the model. We introduce the modified-Beta distribution to develop a Bayesian approach. The alpha-factors model is considered to analyze uncertainties in CCF. We suggest a new formalism to describe uncertainty and consider Dirichlet distributions (nested, grouped) to make a Bayesian analysis. Recording of CCF cause data leads to incomplete contingency table. For a Bayesian analysis of this type of tables, we propose an algorithm relying on inverse Bayes formula (IBF) and Metropolis-Hasting algorithm. We compare our results with those obtained with the alpha- decomposition method, a recent method proposed in the literature. Prediction of catastrophic event is addressed and mapping strategies are described to suggest upper bounds of prediction intervals with pivotal method and Bayesian techniques. Recent events have highlighted the importance of reliability redundant systems and we hope that our work will contribute to a better understanding and prediction of the risks of major CCF events
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Krämer, Romy, Matthias Richter, and Bernd Hofmann. "Parameter estimation in a generalized bivariate Ornstein-Uhlenbeck model." Universitätsbibliothek Chemnitz, 2005. http://nbn-resolving.de/urn:nbn:de:swb:ch1-200501307.

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In this paper, we consider the inverse problem of calibrating a generalization of the bivariate Ornstein-Uhlenbeck model introduced by Lo and Wang. Even though the generalized Black-Scholes option pricing formula still holds, option prices change in comparison to the classical Black-Scholes model. The time-dependent volatility function and the other (real-valued) parameters in the model are calibrated simultaneously from option price data and from some empirical moments of the logarithmic returns. This gives an ill-posed inverse problem, which requires a regularization approach. Applying the theory of Engl, Hanke and Neubauer concerning Tikhonov regularization we show convergence of the regularized solution to the true data and study the form of source conditions which ensure convergence rates.
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Saracco, Jérôme. "Contributions à la régression inverse par tranchage : sliced inverse regression (S.I.R.)." Toulouse 3, 1996. http://www.theses.fr/1996TOU30185.

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La regression inverse par tranchage (sliced inverse regression (s. I. R. )) est une methode de regression semiparametrique reposant sur un argument geometrique. Au contraire des autres methodes de regression semiparametrique, elle ne requiert que des temps de calcul informatique tres courts. Dans cette these, apres un panorama de l'etat actuel des travaux sur s. I. R. , nous envisageons deux aspects de cette methode, ainsi qu'une application a l'estimation simplifiee d'un modele de selection. (1) nous developpons une theorie asymptotique basee sur un decoupage non aleatoire en tranches et portant sur la loi asymptotique de la partie parametrique du modele. (2) une extension semiparametrique du modele de selection tobit peut s'interpreter geometriquement dans le cadre de s. I. R. Exploitant cette observation, nous introduisons un estimateur simplifie pour un tel modele, et nous etudions sa convergence en probabilite et en loi. Des simulations, y compris lorsque certaines hypotheses theoriques ne sont pas respectees par les donnees, confirment le bon comportement de notre estimateur. (3) pour le cas d'echantillons de petite taille, l'estimation par tranchage se revele sensible au choix des tranches. Nous proposons deux methodes alternatives a un tranchage particulier fixe par l'utilisateur, l'une est basee sur un argument nonparametrique, et l'autre est basee sur un lissage de plusieurs decoupages en tranches. Nous etablissons diverses proprietes asymptotiques de ces methodes. Nous les comparons aux methodes s. I. R. Existantes par simulations sur des echantillons de 25 et 50 observations. Les methodes que nous proposons se revelent sensiblement meilleures que les methodes anterieures. Nous avons programme l'ensemble des methodes s. I. R. En splus. Nous fournissons une illustration et un descriptif de l'implementation informatique que nous avons realisee, les differentes procedures et fonctions sont disponibles par ftp
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Books on the topic "Inverse estimator"

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Aster, Richard C. Parameter estimation and inverse problems. 2nd ed. Academic Press, 2012.

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H, Thurber Clifford, and Borchers Brian, eds. Parameter estimation and inverse problems. Elsevier Academic Press, 2005.

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Alquier, Pierre, Eric Gautier, and Gilles Stoltz, eds. Inverse Problems and High-Dimensional Estimation. Springer Berlin Heidelberg, 2011. http://dx.doi.org/10.1007/978-3-642-19989-9.

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Inverse problem theory: Methods for data fitting and model parameter estimation. Elsevier, 1987.

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Bellassoued, Mourad, and Masahiro Yamamoto. Carleman Estimates and Applications to Inverse Problems for Hyperbolic Systems. Springer Japan, 2017. http://dx.doi.org/10.1007/978-4-431-56600-7.

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Banks, H. Thomas. A unified framework for approximation in inverse problems for distributed parameter systems. National Aeronautics and Space Administration, Langley Research Center, Institute for Computer Applications in Science and Engineering, 1988.

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Srivastava, M. S. Admissibility of the inverse and the inadmissibility of the classical estimators in multi-univariate calibration. University of Toronto, Dept. of Statistics, 1994.

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Eric, Gautier, Stoltz Gilles, and SpringerLink (Online service), eds. Inverse Problems and High-Dimensional Estimation: Stats in the Château Summer School, August 31 - September 4, 2009. Springer-Verlag Berlin Heidelberg, 2011.

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Anton, Stoorvogel, and Sannuti Peddapullaiah 1941-, eds. Filtering theory: With applications to fault detection, isolation, and estimation. Birkhäuser, 2007.

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Poletaeva, Vladislava. Economics of sustainable industrial growth: concept, problems and possible mechanisms of formation. INFRA-M Academic Publishing LLC., 2020. http://dx.doi.org/10.12737/1086387.

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The monograph examines the issues of transformation of the Russian economy from raw materials export model to a model of sustainable industrial growth. In the first Chapter of the work the author formulates the definition of sustainable economy growth and the expediency of its formation, analyzes the problems that hinder the transformation of national economic system into a model for sustainable industrial growth, and identified possible mechanisms of such transformation. In the second Chapter, in order to determine the sources of the implementation of the financial mechanism of forming of economy of sustainable industrial growth, the author assesses financial potential of economic entities and analyzes the role of the banking sector and the state to invest resources in the Russian economy. In the third Chapter the author provides the rationale (for the decision of task of forming of economy of industrial growth) for the development of cooperation in the banking sector and the state in the financing of manufacturing industry on the basis of realization of interests of all key stakeholders of such projects, identifies the interests of the state, banking sector and manufacturing industries and estimated the fullness of their realization in the framework of the existing mechanisms of the banking and government lending to the economy.&#x0D; Designed for teachers, students of economic specialties, as well as anyone interested in the problems of development of economy in modern conditions.
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Book chapters on the topic "Inverse estimator"

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Singh, Kesar, Minge Xie, and William E. Strawderman. "Confidence distribution (CD) -- distribution estimator of a parameter." In Complex Datasets and Inverse Problems. Institute of Mathematical Statistics, 2007. http://dx.doi.org/10.1214/074921707000000102.

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Llacer, Jorge, Timothy D. Solberg, Claus Promberger, and Alexander Kuzmany. "The use of the Maximum Likelihood Estimator and the Dynamically Penalized Likelihood methods in inverse radiation therapy planning." In The Use of Computers in Radiation Therapy. Springer Berlin Heidelberg, 2000. http://dx.doi.org/10.1007/978-3-642-59758-9_9.

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van Wieringen, Wessel N., and Carel F. W. Peeters. "Application of a New Ridge Estimator of the Inverse Covariance Matrix to the Reconstruction of Gene-Gene Interaction Networks." In Computational Intelligence Methods for Bioinformatics and Biostatistics. Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-24462-4_15.

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Seshadri, V. "Estimation." In The Inverse Gaussian Distribution. Springer New York, 1999. http://dx.doi.org/10.1007/978-1-4612-1456-4_2.

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Seshadri, V. "Plutonium Estimation." In The Inverse Gaussian Distribution. Springer New York, 1999. http://dx.doi.org/10.1007/978-1-4612-1456-4_28.

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Sun, Ne-Zheng, and Alexander Sun. "The Classical Inverse Problem." In Model Calibration and Parameter Estimation. Springer New York, 2015. http://dx.doi.org/10.1007/978-1-4939-2323-6_2.

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Seshadri, V. "Small area estimation." In The Inverse Gaussian Distribution. Springer New York, 1999. http://dx.doi.org/10.1007/978-1-4612-1456-4_26.

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Cavalier, Laurent. "Inverse Problems in Statistics." In Inverse Problems and High-Dimensional Estimation. Springer Berlin Heidelberg, 2011. http://dx.doi.org/10.1007/978-3-642-19989-9_1.

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Piterbarg, Leonid I., and Alexander G. Ostrovskii. "The Inverse Problem: Autoregressive Estimators." In Advection and Diffusion in Random Media. Springer US, 1997. http://dx.doi.org/10.1007/978-1-4757-4458-3_8.

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Lin, Tao, and Richard Ewing. "Parameter Estimation for Distributed Systems Arising in Fluid Flow Problems via Time Series Methods." In Inverse Problems. Birkhäuser Basel, 1986. http://dx.doi.org/10.1007/978-3-0348-7014-6_8.

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Conference papers on the topic "Inverse estimator"

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Tao, Shaozhe, Yifan Sun, and Daniel Boley. "Inverse Covariance Estimation with Structured Groups." In Twenty-Sixth International Joint Conference on Artificial Intelligence. International Joint Conferences on Artificial Intelligence Organization, 2017. http://dx.doi.org/10.24963/ijcai.2017/395.

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Estimating the inverse covariance matrix of p variables from n observations is challenging when n is much less than p, since the sample covariance matrix is singular and cannot be inverted. A popular solution is to optimize for the L1 penalized estimator; however, this does not incorporate structure domain knowledge and can be expensive to optimize. We consider finding inverse covariance matrices with group structure, defined as potentially overlapping principal submatrices, determined from domain knowledge (e.g. categories or graph cliques). We propose a new estimator for this problem setting that can be derived efficiently via the conditional gradient method, leveraging chordal decomposition theory for scalability. Simulation results show significant improvement in sample complexity when the correct group structure is known. We also apply these estimators to 14,910 stock closing prices, with noticeable improvement when group sparsity is exploited.
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Henriksson, M., S. Borguet, O. Le´onard, and T. Gro¨nstedt. "On Inverse Problems in Turbine Engine Parameter Estimation." In ASME Turbo Expo 2007: Power for Land, Sea, and Air. ASMEDC, 2007. http://dx.doi.org/10.1115/gt2007-27756.

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This paper extends previous work on model order reduction based on singular value decomposition. It is shown how the decrease in estimator variance must be balanced against the bias on the estimate inevitably introduced by solving the inverse problem in a reduced order space. A proof for the decrease in estimator variance by means of multi-point analysis is provided. The proof relies on comparing the Cramer-Rao lower bound of the single point and the multi-point estimators. Model order selection is discussed in the presence of a varying degree of a priori parameter information, through the use of a regularization parameter. Simulation results on the SR-30 turbojet engine indicate that the theoretically attainable multi-point improvements are difficult to realize in practical jet engine applications.
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Nordenvaad, Magnus L., and Lennart Svensson. "A map based estimator for inverse complex covariance matricies." In ICASSP 2012 - 2012 IEEE International Conference on Acoustics, Speech and Signal Processing. IEEE, 2012. http://dx.doi.org/10.1109/icassp.2012.6288638.

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Van der Made, P. M., P. Van Riel, and A. J. Berkhout. "Optimization of estimator-resolution in III-posed inverse problems." In 1985 SEG Technical Program Expanded Abstracts. SEG, 1985. http://dx.doi.org/10.1190/1.1892654.

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Ke, Tong, Kejian J. Wu, and Stergios I. Roumeliotis. "RISE-SLAM: A Resource-aware Inverse Schmidt Estimator for SLAM." In 2019 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS). IEEE, 2019. http://dx.doi.org/10.1109/iros40897.2019.8967892.

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Mahapatra, Dheeren Ku, and Lakshi Prosad Roy. "An estimator for compound-Gaussian multilook SAR clutter amplitude with inverse gamma texture." In 2015 Annual IEEE India Conference (INDICON). IEEE, 2015. http://dx.doi.org/10.1109/indicon.2015.7443757.

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Martinez-Camara, Marta, Michael Muma, Abdelhak M. Zoubir, and Martin Vetterli. "A new robust and efficient estimator for ill-conditioned linear inverse problems with outliers." In ICASSP 2015 - 2015 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). IEEE, 2015. http://dx.doi.org/10.1109/icassp.2015.7178606.

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Verbosio, Fabio, Juraj Kardos, Mauro Bianco, and Olaf Schenk. "Highly Scalable Stencil-Based Matrix-Free Stochastic Estimator for the Diagonal of the Inverse." In 2018 30th International Symposium on Computer Architecture and High Performance Computing (SBAC-PAD). IEEE, 2018. http://dx.doi.org/10.1109/cahpc.2018.8645868.

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Lloyd, George M. "Approximation of a Random Process by Inversion of a Kernel Density Estimator." In ASME 2012 International Mechanical Engineering Congress and Exposition. American Society of Mechanical Engineers, 2012. http://dx.doi.org/10.1115/imece2012-87974.

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A random process is frequently defined entirely by outcomes measured from it. Generally an important goal is to be able to approximate the random process sufficiently accurately in some sense. Common reasons for doing so are to provide surrogate variates for performing tests of hypothesis, and also to provide a model of the random process computationally suitable for inputs to or as stochastic coefficients for a numerical model. In cases of concern here the context is not limited to univariate random process, nor to Gaussian processes. This paper examines an alternative approach for modeling an arbitrary random process known only through its source variates. In this new method the kernel density estimator is inverted to provide a functional in the space of standard normal random variables. This functional can be expanded into a series representation of the random process using Wiener expansions. Several benefits accrue to this method. First, the computation expense of evaluating a KDE (and computing its inverse) need only be done once. Secondly, the rate of convergence of the series representation yields information on the departure of the random process from a strictly Gaussian one.
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Patil, Nishad, Sandeep Menon, Diganta Das, and Michael Pecht. "Evaluation of Robust Covariance Estimation Techniques for Anomaly Detection of Insulated Gate Bipolar Transistors (IGBT)." In ASME 2010 Conference on Smart Materials, Adaptive Structures and Intelligent Systems. ASMEDC, 2010. http://dx.doi.org/10.1115/smasis2010-3861.

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An approach to detect anomalies in IGBTs is to monitor the collector-emitter current and voltage in application. These current and voltage parameters can then be reduced to a univariate distance measure called the Mahalanobis Distance (MD). The MD values with the use of an appropriate threshold enable anomaly detection of these devices. Mahalanobis distances (MD) are weighted Euclidean distances; the distance of each point from the center of the distribution is weighted by the inverse of the sample variance-covariance matrix. The presence of outliers in the monitored data can lead to the overestimation of the covariance matrix that in turn affects the anomaly detection results. This issue can be addressed by the use of robust covariance estimation techniques. In this study, the minimum volume ellipsoid (MVE) estimator, the minimum covariance determinant estimator (MCD) and the nearest neighbor variance estimator (NNVE) were used for anomaly detection of IGBTs. IGBTs were aged under a resistive load until failure. The monitored collector-emitter current and voltage values were used as input parameters for the MD calculation. The three robust covariance estimation techniques were used to compute the MD values and the anomaly detection times were compared to the results obtained by the classical covariance estimation technique.
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Reports on the topic "Inverse estimator"

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Hou, Elizabeth Mary, and Earl Christopher Lawrence. Variational Methods for Posterior Estimation of Non-linear Inverse Problems. Office of Scientific and Technical Information (OSTI), 2018. http://dx.doi.org/10.2172/1475317.

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Candy, J. V. ,. LLNL. Inverse synthetic aperture radar processing using parametric time-frequency estimators Phase I. Office of Scientific and Technical Information (OSTI), 1997. http://dx.doi.org/10.2172/304514.

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Rocha, Guilherme V., Peng Zhao, and Bin Yu. A Path Following Algorithm for Sparse Pseudo-Likelihood Inverse Covariance Estimation (SPLICE). Defense Technical Information Center, 2008. http://dx.doi.org/10.21236/ada487557.

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Borden, Brett, and R. J. Dinger. Radar Inverse Scattering Using Statistical Estimation of the Echo Phase- Front Derivatives. Defense Technical Information Center, 1986. http://dx.doi.org/10.21236/ada176598.

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Yu, Guoshen, Guillermo Sapiro, and Stephane Mallat. Solving Inverse Problems with Piecewise Linear Estimators: From Gaussian Mixture Models to Structured Sparsity. Defense Technical Information Center, 2010. http://dx.doi.org/10.21236/ada540722.

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Miller, Eric L., and Alan S. Willsky. Wavelet Transforms and Multiscale Estimation Techniques for the Solution of Multisensor Inverse Problems. Defense Technical Information Center, 1994. http://dx.doi.org/10.21236/ada458528.

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Lambraskos, Samuel G., LuLu Huang, and A. Shabaev. Initial Parameter Estimation for Inverse Thermal Analysis of Ti-6Al-4V Deep Penetration Welds. Defense Technical Information Center, 2014. http://dx.doi.org/10.21236/ada602585.

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Nakos, James Thomas, Victor G. Figueroa, and Jill E. Murphy. Uncertainty analysis of heat flux measurements estimated using a one-dimensional, inverse heat-conduction program. Office of Scientific and Technical Information (OSTI), 2005. http://dx.doi.org/10.2172/921718.

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Kaffenberger, Michelle, and Lant Pritchett. Women’s Education May Be Even Better Than We Thought: Estimating the Gains from Education When Schooling Ain’t Learning. Research on Improving Systems of Education (RISE), 2020. http://dx.doi.org/10.35489/bsg-rise-wp_2020/049.

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Women’s schooling has long been regarded as one of the best investments in development. Using two different cross-nationally comparable data sets which both contain measures of schooling, assessments of literacy, and life outcomes for more than 50 countries, we show the association of women’s education (defined as schooling and the acquisition of literacy) with four life outcomes (fertility, child mortality, empowerment, and financial practices) is much larger than the standard estimates of the gains from schooling alone. First, estimates of the association of outcomes with schooling alone cannot distinguish between the association of outcomes with schooling that actually produces increased learning and schooling that does not. Second, typical estimates do not address attenuation bias from measurement error. Using the new data on literacy to partially address these deficiencies, we find that the associations of women’s basic education (completing primary schooling and attaining literacy) with child mortality, fertility, women’s empowerment and the associations of men’s and women’s basic education with positive financial practices are three to five times larger than standard estimates. For instance, our country aggregated OLS estimate of the association of women’s empowerment with primary schooling versus no schooling is 0.15 of a standard deviation of the index, but the estimated association for women with primary schooling and literacy, using IV to correct for attenuation bias, is 0.68, 4.6 times bigger. Our findings raise two conceptual points. First, if the causal pathway through which schooling affects life outcomes is, even partially, through learning then estimates of the impact of schooling will underestimate the impact of education. Second, decisions about how to invest to improve life outcomes necessarily depend on estimates of the relative impacts and relative costs of schooling (e.g., grade completion) versus learning (e.g., literacy) on life outcomes. Our results do share the limitation of all previous observational results that the associations cannot be given causal interpretation and much more work will be needed to be able to make reliable claims about causal pathways.
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Klibanov, Michael V., and Sergey E. Pamyatnykh. Global Uniqueness for a Coefficient Inverse Problem for the Non-Stationary Transport Equation via Carleman Estimate. Defense Technical Information Center, 2006. http://dx.doi.org/10.21236/ada448486.

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