Готові списки джерел за темами / Targeted Maximum Likelihood Estimation

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Добірка наукової літератури з теми "Targeted Maximum Likelihood Estimation"

Автор: Grafiati
Опубліковано: 25 січня 2025

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Статті в журналах з теми "Targeted Maximum Likelihood Estimation"

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Pang, Menglan, Tibor Schuster, Kristian B. Filion, Maria Eberg, and Robert W. Platt. "Targeted Maximum Likelihood Estimation for Pharmacoepidemiologic Research." Epidemiology 27, no. 4 (July 2016): 570–77. http://dx.doi.org/10.1097/ede.0000000000000487.

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Lendle, Samuel D., Bruce Fireman, and Mark J. van der Laan. "Targeted maximum likelihood estimation in safety analysis." Journal of Clinical Epidemiology 66, no. 8 (August 2013): S91—S98. http://dx.doi.org/10.1016/j.jclinepi.2013.02.017.

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Zheng, Wenjing, and Mark J. van der Laan. "Targeted Maximum Likelihood Estimation of Natural Direct Effects." International Journal of Biostatistics 8, no. 1 (January 6, 2012): 1–40. http://dx.doi.org/10.2202/1557-4679.1361.

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Dijkhuis, Talko B., and Frank J. Blaauw. "Transfering Targeted Maximum Likelihood Estimation for Causal Inference into Sports Science." Entropy 24, no. 8 (July 31, 2022): 1060. http://dx.doi.org/10.3390/e24081060.

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Although causal inference has shown great value in estimating effect sizes in, for instance, physics, medical studies, and economics, it is rarely used in sports science. Targeted Maximum Likelihood Estimation (TMLE) is a modern method for performing causal inference. TMLE is forgiving in the misspecification of the causal model and improves the estimation of effect sizes using machine-learning methods. We demonstrate the advantage of TMLE in sports science by comparing the calculated effect size with a Generalized Linear Model (GLM). In this study, we introduce TMLE and provide a roadmap for making causal inference and apply the roadmap along with the methods mentioned above in a simulation study and case study investigating the influence of substitutions on the physical performance of the entire soccer team (i.e., the effect size of substitutions on the total physical performance). We construct a causal model, a misspecified causal model, a simulation dataset, and an observed tracking dataset of individual players from 302 elite soccer matches. The simulation dataset results show that TMLE outperforms GLM in estimating the effect size of the substitutions on the total physical performance. Furthermore, TMLE is most robust against model misspecification in both the simulation and the tracking dataset. However, independent of the method used in the tracking dataset, it was found that substitutes increase the physical performance of the entire soccer team.
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Schuler, Megan S., and Sherri Rose. "Targeted Maximum Likelihood Estimation for Causal Inference in Observational Studies." American Journal of Epidemiology 185, no. 1 (December 9, 2016): 65–73. http://dx.doi.org/10.1093/aje/kww165.

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Luque-Fernandez, Miguel Angel, Michael Schomaker, Bernard Rachet, and Mireille E. Schnitzer. "Targeted maximum likelihood estimation for a binary treatment: A tutorial." Statistics in Medicine 37, no. 16 (April 23, 2018): 2530–46. http://dx.doi.org/10.1002/sim.7628.

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Cho, Sunyoung, Heejo Koo, Beom Kyung Kim, and Euna Han. "Causal Analyses of Statin to Prevent Liver Disease Progression: A Nationwide Study Using Superlearning Targeted Maximum Likelihood Estimation." Yakhak Hoeji 68, no. 1 (February 28, 2024): 44–55. http://dx.doi.org/10.17480/psk.2024.68.1.44.

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Many studies have shown that statins reduce the risk of progression to liver cirrhosis (LC) and hepatocellular carcinoma (HCC) among at-risk populations. However, causality has not been proved. This study examined whether statins could prevent LC and HCC in patients with progressive and worsening chronic liver disease, using a robust methodology for causality. Between 2002 and 2013, 52,145 patients with chronic liver diseases were identified from the National Health Insurance Service database in South Korea. The inverse probability weighting (IPW) and superlearning targeted maximum likelihood estimation (TMLE) were used to assess the causality of statin use on the risk of LC and HCC, adjusting for sex, age, comorbidities, and co-medications. Multivariable superlearning TMLE revealed that statin use was associated with reduction in the incidence risk of LC (Marginal odds ratio (MOR) 0.59, 95% confidence interval [CI] 0.50-0.65) and HCC (MOR 0.59, 95% CI 0.50-0.67). Such a protective effect was more evident with atorvastatin and lipophilic statin. This population-based observational study indicated the benefit of statin use, particularly atorvastatin and lipophilic statin, for causally reducing the risk of LC and HCC.
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Cai, Weixin, and Mark J. Laan. "One‐step targeted maximum likelihood estimation for time‐to‐event outcomes." Biometrics 76, no. 3 (November 28, 2019): 722–33. http://dx.doi.org/10.1111/biom.13172.

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Stitelman, Ori M., C. William Wester, Victor De Gruttola, and Mark J. van der Laan. "Targeted Maximum Likelihood Estimation of Effect Modification Parameters in Survival Analysis." International Journal of Biostatistics 7, no. 1 (January 30, 2011): 1–34. http://dx.doi.org/10.2202/1557-4679.1307.

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Grossman, J., M. Ghadessi, A. Contijoch, H. Ostojic, A. Cervantes, J. M. O'Connor, and M. Ducreux. "MSR75 Correlate: Assessing Dose Effect Using Targeted Maximum Likelihood Estimation (TMLE)." Value in Health 26, no. 12 (December 2023): S407. http://dx.doi.org/10.1016/j.jval.2023.09.2134.

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Дисертації з теми "Targeted Maximum Likelihood Estimation"

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Schnitzer, Mireille. "Targeted maximum likelihood estimation for longitudinal data." Thesis, McGill University, 2013. http://digitool.Library.McGill.CA:80/R/?func=dbin-jump-full&object_id=114242.

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Semiparametric efficient methods in causal inference have been developed to robustly and efficiently estimate causal parameters. As in general causal estimation, the methods rely on a set of mathematical assumptions that translate into requirements of causal knowledge and confounder identification. Targeted maximum likelihood estimation (TMLE) methodology has been developed as a potential improvement on efficient estimating equations, in that it shares the qualities of double robustness (unbiasedness under partial misspecification) and semiparametric efficiency, but can be constructed to provide boundedness of parameter estimates, robustness to data sparsity, and a unique estimate.This thesis, composed primarily of three manuscripts, presents new research on the analysis of longitudinal and survival data with time-dependent confounders using TMLE. The first manuscript describes the construction of a two time-point TMLE using a generalized exponential distribution family member as the loss function for the outcome model. It demonstrates the robustness of the continuous version of this TMLE algorithm in a simulation study, and uses a modified version of the method in a simplified analysis of the PROmotion of Breastfeeding Intervention Trial (PROBIT) where evidence for a protective causal effect of breastfeeding on gastrointestinal infection is obtained.The second manuscript presents a description of several substitution estimators for longitudinal data, a specialized implementation of a longitudinal TMLE method, and a case study using the full PROBIT dataset. The K time point sequential TMLE algorithm employed (theory previously developed), implemented nonparametrically using Super Learner, differs fundamentally from the strategy used in the first manuscript, and offers some benefits in computation and ease of implementation. The analysis compares different durations of breastfeeding and the related exposure-specific (and censoring-free) mean counts of gastrointestinal infections over the first year of an infant's life and concludes that a protective effect is present. Simulated data mirroring the PROBIT dataset was generated, and the performance of TMLE was again assessed.The third manuscript develops a methodology to estimate marginal structural models for survival data. Utilizing the sequential longitudinal TMLE algorithm to estimate the exposure-specific survival curves for all exposure patterns, it demonstrates a way to combine inference in order to model the outcome using a linear specification. This article presents the theoretical construction of two different types of marginal structural models (modeling the log-odds survival and the hazard) and presents a simulation study demonstrating the unbiasedness of the technique. It then describes an analysis of the Canadian Co-infection Cohort study undertaken with one of the TMLE methods to fit survival curves and a model for the hazard function of development of end-stage liver disease (ESLD) conditional on time and clearance of the Hepatitis C virus.
Des méthodes d'analyse causale semi-paramétriques et efficaces ont été développées pour estimer les paramètres causaux efficacement et de façon robuste. Comme c'est le cas en général pour l'estimation causale, ces méthodes se basent sur un ensemble d'hypothèses mathématiques qui impliquent que la structure causale et les facteurs de confusion doivent être connus. La méthode d'estimation par le maximum de vraisemblance ciblé (TMLE) se veut une amélioration des équations d'estimation efficaces: elle a les propriétés de double robustesse (sans biais même avec une erreur de spécification partielle) et d'efficacité semi-paramétrique, mais peut également garantir des estimés finis pour les paramètres et la production d'un seul estimé en plus d'être robuste si les données sont éparses. Cette thèse, composée essentiellement de trois manuscrits, présente de nouvelles recherches sur l'analyse avec le TMLE de données longitudinales et de données de survie avec des facteurs de confusion variant dans le temps. Le premier manuscrit décrit la construction d'un TMLE à deux points dans le temps avec une distribution de la famille exponentielle généralisée comme fonction de perte du modèle de la réponse. Il démontre à l'aide d'une étude de simulation la robustesse de la version continue de cet algorithme TMLE, et utilise une version Poisson de la méthode pour une analyse simplifiée de l'étude PROmotion of Breastfeeding Intervention Trial (PROBIT) qui donne des signes d'un effet causal protecteur de l'allaitement sur les infections gastrointestinales. Le deuxième manuscrit présente une description de plusieurs estimateurs de substitution pour données longitudinales, une implémentation spéciale de la méthode TMLE longitudinale et une étude de cas du jeu de données PROBIT entier. Un algorithme TMLE séquentiel à K points dans le temps est utilisé (théorie déjà développée), lequel est implémenté de façon non-paramétrique avec le Super Learner. Cet algorithme diffère fondamentalement de la stratégie utilisée dans le premier manuscrit et offre des avantages en terme de calcul et de facilité d'implémentation. L'analyse compare les moyennes de dénombrements du nombre d'infections gastrointestinales dans la première année de vie d'un nouveau-né par durée d'allaitement et avec aucune censure, et conclut à la présence d'un effet protecteur. Des données simulées semblables au jeu de données PROBIT sont également générées, et la performance du TMLE de nouveau étudiée. Le troisième manuscrit développe une méthodologie pour estimer des modèles structurels marginaux pour données de survie. En utilisant l'algorithme séquentiel du TMLE longitudinal pour estimer des courbes de survie spécifiques à l'exposition pour tous les patrons d'exposition, il montre une façon de combiner les inférences pour modéliser la réponse à l'aide d'une spécification linéaire. Cet article présente la construction théorique de deux différents types de modèles structurels marginaux (modélisant le log du rapport des chances de survie et le risque) et présente une étude de simulation démontrant l'absence de biais de la technique. Il décrit ensuite une analyse de l'Étude de la Cohorte Canadienne de Co-Infection à l'aide d'une des méthodes TMLE pour ajuster des courbes de survie et un modèle pour la fonction de risque du développement de la maladie chronique du foie (ESLD) conditionnellement au temps et à l'élimination du virus de l'hépatite C.
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Sarovar, Varada. "Targeted Maximum Likelihood Estimation for Evaluation of the Health Impacts of Air Pollution." Thesis, University of California, Berkeley, 2017. http://pqdtopen.proquest.com/#viewpdf?dispub=10279902.

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The adverse effects of air pollution on human life is of serious concern for today’s society. Two population groups that are especially vulnerable to air pollution are pregnant women and their growing fetuses, and the focus of this thesis is to study the effects of air pollution on these populations. In order to address the methodological limitations in prior research, we quantify the impact of air pollution on various adverse pregnancy outcomes, utilizing machine learning and novel causal inference methods. Specifically, we utilize two semi-parametric, double robust, asymptotically efficient substitution estimators to estimate the causal attributable risk of various pregnancy outcomes of interest. Model fitting via machine learning algorithms helps to avoid reliance on misspecified parametric models and thereby improve both the robustness and precision of our estimates, ensuring meaningful statistical inference. Under assumptions, the causal attributable risk that we estimate translates to the absolute change in adverse pregnancy outcome risk that would be observed under a hypothetical intervention to change pollution levels, relative to currently observed levels. The estimated causal attributable risk provides a quantitative estimate of a quantity with more immediate public health and policy relevance.

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Khanafer, Sajida. "Sensory Integration During Goal Directed Reaches: The Effects of Manipulating Target Availability." Thèse, Université d'Ottawa / University of Ottawa, 2012. http://hdl.handle.net/10393/23422.

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When using visual and proprioceptive information to plan a reach, it has been proposed that the brain combines these cues to estimate the object and/or limb’s location. Specifically, according to the maximum-likelihood estimation (MLE) model, more reliable sensory inputs are assigned a greater weight (Ernst & Banks, 2002). In this research we examined if the brain is able to adjust which sensory cue it weights the most. Specifically, we asked if the brain changes how it weights sensory information when the availability of a visual cue is manipulated. Twenty-four healthy subjects reached to visual (V), proprioceptive (P), or visual + proprioceptive (VP) targets under different visual delay conditions (e.g. on V and VP trials, the visual target was available for the entire reach, it was removed with the go-signal or it was removed 1, 2 or 5 seconds before the go-signal). Subjects completed 5 blocks of trials, with 90 trials per block. For 12 subjects, the visual delay was kept consistent within a block of trials, while for the other 12 subjects, different visual delays were intermixed within a block of trials. To establish which sensory cue subjects weighted the most, we compared endpoint positions achieved on V and P reaches to VP reaches. Results indicated that all subjects weighted sensory cues in accordance with the MLE model across all delay conditions and that these weights were similar regardless of the visual delay. Moreover, while errors increased with longer visual delays, there was no change in reaching variance. Thus, manipulating the visual environment was not enough to change subjects’ weighting strategy, further i
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Ruprecht, Jürg. "Maximum likelihood estimation of multipath channels /." [S.l.] : [s.n.], 1989. http://e-collection.ethbib.ethz.ch/show?type=diss&nr=8789.

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Horbelt, Werner. "Maximum likelihood estimation in dynamical systems." [S.l. : s.n.], 2001. http://deposit.ddb.de/cgi-bin/dokserv?idn=963810812.

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Sabbagh, Yvonne. "Maximum Likelihood Estimation of Hammerstein Models." Thesis, Linköping University, Department of Electrical Engineering, 2003. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-2061.

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In this Master's thesis, Maximum Likelihood-based parametric identification methods for discrete-time SISO Hammerstein models from perturbed observations on both input and output, are investigated.

Hammerstein models, consisting of a static nonlinear block followed by a dynamic linear one, are widely applied to modeling nonlinear dynamic systems, i.e., dynamic systems having nonlinearity at its input.

Two identification methods are proposed. The first one assumes a Hammerstein model where the input signal is noise-free and the output signal is perturbed with colored noise. The second assumes, however, white noises added to the input and output of the nonlinearity and to the output of the whole considered Hammerstein model. Both methods operate directly in the time domain and their properties are illustrated by a number of simulated examples. It should be observed that attention is focused on derivation, numerical calculation, and simulation corresponding to the first identification method mentioned above.

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7

Leeuw, Johannes Leonardus van der. "Maximum likelihood estimation of exact ARMA models /." Tilburg : Tilburg University Press, 1997. http://www.gbv.de/dms/goettingen/265169976.pdf.

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Ehlers, Rene. "Maximum likelihood estimation procedures for categorical data." Pretoria : [s.n.], 2002. http://upetd.up.ac.za/thesis/available/etd-07222005-124541.

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Zou, Yiqun. "Attainment of Global Convergence in Maximum Likelihood Estimation." Thesis, University of Manchester, 2009. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.511845.

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Mariano, Machado Robson José. "Penalised maximum likelihood estimation for multi-state models." Thesis, University College London (University of London), 2018. http://discovery.ucl.ac.uk/10060352/.

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Multi-state models can be used to analyse processes where change of status over time is of interest. In medical research, processes are commonly defined by a set of living states and a dead state. Transition times between living states are often interval censored. In this case, models are usually formulated in a Markov processes framework. The likelihood function is then constructed using transition probabilities. Models are specified using proportional hazards for the effect of covariates on transition intensities. Time-dependency is usually defined by parametric models, which can represent a strong model assumption. Semiparametric hazards specification with splines is a more flexible method for modelling time-dependency in multi-state models. Penalised maximum likelihood is used to estimate these models. Selecting the optimal amount of smoothing is challenging as the problem involves multiple penalties. This thesis aims to develop methods to estimate multi-state models with splines for interval-censored data. We propose a penalised likelihood method to estimate multi-state models that allow for parametric and semiparametric hazards specifications. The estimation is based on a scoring algorithm, and a grid search method to estimate the smoothing parameters. This method is shown using an application to ageing research. Furthermore, we extend the proposed method by developing a computationally more efficient method to estimate multi-state models with splines. For this extension, the estimation is based on a scoring algorithm, and an automatic smoothing parameters selection. The extended method is illustrated with two data analyses and a simulation study.
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Книги з теми "Targeted Maximum Likelihood Estimation"

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Eliason, Scott. Maximum Likelihood Estimation. 2455 Teller Road, Newbury Park California 91320 United States of America: SAGE Publications, Inc., 1993. http://dx.doi.org/10.4135/9781412984928.

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Eggermont, P. P. B., and V. N. LaRiccia. Maximum Penalized Likelihood Estimation. New York, NY: Springer New York, 2001. http://dx.doi.org/10.1007/978-1-0716-1244-6.

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LaRiccia, Vincent N., and Paul P. Eggermont. Maximum Penalized Likelihood Estimation. New York, NY: Springer New York, 2009. http://dx.doi.org/10.1007/b12285.

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N, LaRiccia V., ed. Maximum penalized likelihood estimation. New York: Springer, 2001.

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5

Millar, Russell B. Maximum Likelihood Estimation and Inference. Chichester, UK: John Wiley & Sons, Ltd, 2011. http://dx.doi.org/10.1002/9780470094846.

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Jeffrey, Pitblado, Sribney William, and Stata Corporation, eds. Maximum likelihood estimation with stata. 3rd ed. College Station, Tex: Stata Press, 2006.

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7

S, Pitblado Jeffrey, and Poi Brian, eds. Maximum likelihood estimation with Stata. 4th ed. College Station, Tex: Stata Press, 2010.

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8

Nagelkerke, Nico J. D. Maximum Likelihood Estimation of Functional Relationships. New York, NY: Springer New York, 1992. http://dx.doi.org/10.1007/978-1-4612-2858-5.

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Nagelkerke, Nico J. D. Maximum likelihood estimation of functional relationships. Berlin: Springer-Verlag, 1992.

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10

Ruprecht, Jürg. Maximum-likelihood estimation of multipath channel. Konstanz: Hartung-Gorre, 1989.

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Частини книг з теми "Targeted Maximum Likelihood Estimation"

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Gruber, Susan, and Mark van der Laan. "Collaborative Targeted Maximum Likelihood Estimation to Assess Causal Effects in Observational Studies." In Biopharmaceutical Applied Statistics Symposium, 1–23. Singapore: Springer Singapore, 2018. http://dx.doi.org/10.1007/978-981-10-7826-2_1.

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Baig, Nauman Anwar, Muhammad Anwar Baig, Thawban Anwar Baig, Adnan Anwar Baig, and Abdullah Anwar Baig. "Estimation of Phase, Range, Doppler of Targets Using Maximum Likelihood Estimator." In Proceedings of the Future Technologies Conference (FTC) 2018, 483–90. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-030-02683-7_34.

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3

Heidenreich, Philipp, and Abdelhak M. Zoubir. "Computational Aspects of Maximum Likelihood DOA Estimation of Two Targets with Applications to Automotive Radar." In Smart Mobile In-Vehicle Systems, 3–18. New York, NY: Springer New York, 2013. http://dx.doi.org/10.1007/978-1-4614-9120-0_1.

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Kelley Pace, R. "Maximum Likelihood Estimation." In Handbook of Regional Science, 1–17. Berlin, Heidelberg: Springer Berlin Heidelberg, 2018. http://dx.doi.org/10.1007/978-3-642-36203-3_88-1.

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Lee, Myoung-jae. "Maximum Likelihood Estimation." In Methods of Moments and Semiparametric Econometrics for Limited Dependent Variable Models, 41–67. New York, NY: Springer New York, 1996. http://dx.doi.org/10.1007/978-1-4757-2550-6_4.

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Nguyen, Hung T., and Gerald S. Rogers. "Maximum Likelihood Estimation." In Springer Texts in Statistics, 129–36. New York, NY: Springer New York, 1989. http://dx.doi.org/10.1007/978-1-4613-8914-9_20.

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Pan, Jian-Xin, and Kai-Tai Fang. "Maximum Likelihood Estimation." In Growth Curve Models and Statistical Diagnostics, 77–158. New York, NY: Springer New York, 2002. http://dx.doi.org/10.1007/978-0-387-21812-0_3.

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Brown, Jonathon D. "Maximum-Likelihood Estimation." In Linear Models in Matrix Form, 69–104. Cham: Springer International Publishing, 2014. http://dx.doi.org/10.1007/978-3-319-11734-8_3.

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Krolzig, Hans-Martin. "Maximum Likelihood Estimation." In Lecture Notes in Economics and Mathematical Systems, 89–122. Berlin, Heidelberg: Springer Berlin Heidelberg, 1997. http://dx.doi.org/10.1007/978-3-642-51684-9_7.

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Haynes, Winston. "Maximum Likelihood Estimation." In Encyclopedia of Systems Biology, 1190–91. New York, NY: Springer New York, 2013. http://dx.doi.org/10.1007/978-1-4419-9863-7_1235.

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Тези доповідей конференцій з теми "Targeted Maximum Likelihood Estimation"

1

Zheng, Hao, Yong Cheng, and Yang Liu. "Maximum Expected Likelihood Estimation for Zero-resource Neural Machine Translation." In Twenty-Sixth International Joint Conference on Artificial Intelligence. California: International Joint Conferences on Artificial Intelligence Organization, 2017. http://dx.doi.org/10.24963/ijcai.2017/594.

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While neural machine translation (NMT) has made remarkable progress in translating a handful of high-resource language pairs recently, parallel corpora are not always available for many zero-resource language pairs. To deal with this problem, we propose an approach to zero-resource NMT via maximum expected likelihood estimation. The basic idea is to maximize the expectation with respect to a pivot-to-source translation model for the intended source-to-target model on a pivot-target parallel corpus. To approximate the expectation, we propose two methods to connect the pivot-to-source and source-to-target models. Experiments on two zero-resource language pairs show that the proposed approach yields substantial gains over baseline methods. We also observe that when trained jointly with the source-to-target model, the pivot-to-source translation model also obtains improvements over independent training.
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Moghari, Mehdi Hedjazi, and Purang Abolmaesumi. "Maximum likelihood estimation of the distribution of target registration error." In Medical Imaging, edited by Michael I. Miga and Kevin R. Cleary. SPIE, 2008. http://dx.doi.org/10.1117/12.768868.

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Zhang, Mengdi, Hongyi Lu, Shiyin Li, and Zhiwei Li. "Maneuvering target imaging and motion parameter estimation based on improved maximum likelihood estimation." In IET International Radar Conference (IRC 2023). Institution of Engineering and Technology, 2023. http://dx.doi.org/10.1049/icp.2024.1674.

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Schatzberg, Alon, Anthony J. Devaney, and Ross Deming. "Maximum likelihood estimation of target location in acoustic and electromagnetic imaging." In SEG Technical Program Expanded Abstracts 1995. Society of Exploration Geophysicists, 1995. http://dx.doi.org/10.1190/1.1887557.

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Ramsay, Gordon, and Li Deng. "Maximum-likelihood estimation for articulatory speech recognition using a stochastic target model." In 4th European Conference on Speech Communication and Technology (Eurospeech 1995). ISCA: ISCA, 1995. http://dx.doi.org/10.21437/eurospeech.1995-225.

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Baum, Marcus, and Peter Willett. "A hybrid data association model for efficient multi-target maximum likelihood estimation." In ICASSP 2014 - 2014 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). IEEE, 2014. http://dx.doi.org/10.1109/icassp.2014.6854395.

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Dianat, Mojtaba, Mohammad Reza Taban, and Ali Akbar Tadaion. "A new approach for target localization using Maximum Likelihood Estimation in MIMO radar." In 2011 24th IEEE Canadian Conference on Electrical and Computer Engineering (CCECE). IEEE, 2011. http://dx.doi.org/10.1109/ccece.2011.6030554.

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Lu, Chengye, Jinzhou Li, Miao Wang, and Jinfeng Hu. "Parameter Estimation for Maneuvering Target in OTHR Relying on Improved Maximum-Likelihood Algorithm." In 2019 IEEE International Conferences on Ubiquitous Computing & Communications (IUCC) and Data Science and Computational Intelligence (DSCI) and Smart Computing, Networking and Services (SmartCNS). IEEE, 2019. http://dx.doi.org/10.1109/iucc/dsci/smartcns.2019.00143.

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Vikalo, H., B. Hassibi, and A. Hassibi. "On joint maximum-likelihood estimation of PCR efficiency and initial amount of target." In 2006 IEEE International Workshop on Genomic Signal Processing and Statistics. IEEE, 2006. http://dx.doi.org/10.1109/gensips.2006.353149.

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Hou, Wang, Gucan Long, Zhihui Lei, and Jing Dong. "The small target detection based on maximum likelihood estimation and spot detection operator." In Selected Proceedings of the Photoelectronic Technology Committee Conferences held July-December 2013, edited by Jorge Ojeda-Castaneda, Shensheng Han, Ping Jia, Jiancheng Fang, Dianyuan Fan, Liejia Qian, Yuqiu Gu, and Xueqing Yan. SPIE, 2014. http://dx.doi.org/10.1117/12.2054032.

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Звіти організацій з теми "Targeted Maximum Likelihood Estimation"

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Ljung, Lennart, Sanjoy K. Mitter, and Jose M. Moura. Optimal Recursive Maximum Likelihood Estimation,. Fort Belvoir, VA: Defense Technical Information Center, March 1987. http://dx.doi.org/10.21236/ada187980.

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Adams, Terry. Maximum Likelihood Estimation: Some Basics. Office of Scientific and Technical Information (OSTI), January 2025. https://doi.org/10.2172/2496644.

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Ait-Sahalia, Yacine, and Robert Kimmel. Maximum Likelihood Estimation of Stochastic Volatility Models. Cambridge, MA: National Bureau of Economic Research, June 2004. http://dx.doi.org/10.3386/w10579.

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Bates, David. Maximum Likelihood Estimation of Latent Affine Processes. Cambridge, MA: National Bureau of Economic Research, May 2003. http://dx.doi.org/10.3386/w9673.

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5

Avdis, Efstathios, and Jessica Wachter. Maximum likelihood estimation of the equity premium. Cambridge, MA: National Bureau of Economic Research, November 2013. http://dx.doi.org/10.3386/w19684.

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Ainsleigh, P. L., J. D. George, and V. K. Jain. Maximum Likelihood Parameter Estimation for Acoustic Transducer Calibration. Fort Belvoir, VA: Defense Technical Information Center, August 1988. http://dx.doi.org/10.21236/ada204923.

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Moore, Terrence, and Brian Sadler. Maximum-Likelihood Estimation and Scoring Under Parametric Constraints. Fort Belvoir, VA: Defense Technical Information Center, May 2006. http://dx.doi.org/10.21236/ada448612.

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Diebold, Francis, and Til Schuermann. Exact Maximum Likelihood Estimation of Observation-Driven Econometric Models. Cambridge, MA: National Bureau of Economic Research, April 1996. http://dx.doi.org/10.3386/t0194.

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9

Hall, Jr, Lehnigk Charles E., Viswanath Siegfried H., and Guttalu R. Maximum-Likelihood Parameter Estimation of a Generalized Gumbel Distribution. Fort Belvoir, VA: Defense Technical Information Center, March 1989. http://dx.doi.org/10.21236/ada207994.

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Lake, Douglas. Efficient Maximum Likelihood Estimation for Multiple and Coupled Harmonics. Fort Belvoir, VA: Defense Technical Information Center, December 1999. http://dx.doi.org/10.21236/ada372834.

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