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Volume 2, Issue 1 2006 Article 11 The International Journal of Biostatistics Targeted Maximum Likelihood Learning Mark J. van der Laan, Division of Biostatistics, School of Public Health, University of California, Berkeley Daniel Rubin, University of California, Berkeley Recommended Citation: van der Laan, Mark J. and Rubin, Daniel (2006) "Targeted Maximum Likelihood Learning," The International Journal of Biostatistics: Vol. 2: Iss. 1, Article 11. DOI: 10.2202/1557-4679.1043 Targeted Maximum Likelihood Learning Mark J. van der Laan and Daniel Rubin Abstract

Volume 8, Issue 1 2012 Article 30 The International Journal of Biostatistics Targeted Maximum Likelihood Estimation for Prediction Calibration Jordan Brooks, University of California - Berkeley Mark J. van der Laan, University of California - Berkeley Alan S. Go, Kaiser Permanente Division of Research Recommended Citation: Brooks, Jordan; van der Laan, Mark J.; and Go, Alan S. (2012) "Targeted Maximum Likelihood Estimation for Prediction Calibration," The International Journal of Biostatistics: Vol. 8: Iss. 1, Article 30. DOI: 10.1515/1557-4679.1385 ©2012 De

1 Introduction Diffusion processes are frequently used to model continuous time variables in many scientific fields including biology, chemistry, economics, physics, etc. Stochastic differential equations (SDEs) are probabilistic approaches to diffusions and now widely used to characterize diffusion processes. Because the closed-form transition density of a diffusion is usually hard to obtain and the sampling interval in practice is not zero, maximum likelihood estimator (MLE) based on true density is inapplicable to many parametric SDEs. Various methods have

Volume 6, Issue 1 2010 Article 17 The International Journal of Biostatistics Collaborative Double Robust Targeted Maximum Likelihood Estimation Mark J. van der Laan, University of California, Berkeley Susan Gruber, University of California, Berkeley Recommended Citation: van der Laan, Mark J. and Gruber, Susan (2010) "Collaborative Double Robust Targeted Maximum Likelihood Estimation," The International Journal of Biostatistics: Vol. 6: Iss. 1, Article 17. DOI: 10.2202/1557-4679.1181 Collaborative Double Robust Targeted Maximum Likelihood Estimation Mark J. van

Volume 3, Issue 1 2004 Article 31 Statistical Applications in Genetics and Molecular Biology Maximum Likelihood for Genome Phylogeny on Gene Content Hongmei Zhang, University of West Florida Xun Gu, Iowa State University Recommended Citation: Zhang, Hongmei and Gu, Xun (2004) "Maximum Likelihood for Genome Phylogeny on Gene Content," Statistical Applications in Genetics and Molecular Biology: Vol. 3: Iss. 1, Article 31. DOI: 10.2202/1544-6115.1060 ©2004 by the authors. All rights reserved. Maximum Likelihood for Genome Phylogeny on Gene Content Hongmei Zhang and

tm 10/2013 Beiträge PRNU and DSNU Maximum Likelihood Estimation Using Sensor Statistics PRNU- und DSNU-Maximum-Likelihood-Schätzung mit Hilfe der Sensorstatistik Marc Geese∗, Paul Ruhnau, Robert Bosch GmbH, Leonberg, Bernd Jähne, Heidelberg Collaboratory for Image Processing, Heidelberg ∗ Correspondence author: marc.geese@de.bosch.com Summary Image sensors come with a spatial inhomogene- ity, known as Fixed Pattern Noise, that degrades the image quality. In this paper a known maximum likelihood estimation method [1] is extended in a way that it allows to

The B.E. Journal of Theoretical Economics Contributions Volume 9, Issue 1 2009 Article 30 Identifying Community Structures from Network Data via Maximum Likelihood Methods Jernej Copic∗ Matthew O. Jackson† Alan Kirman‡ ∗UCLA, jcopic@econ.ucla.edu †Stanford University and Santa Fe Institute, jacksonm@stanford.edu ‡GREQAM, kirman@univmed.fr Recommended Citation Jernej Copic, Matthew O. Jackson, and Alan Kirman (2009) “Identifying Community Structures from Network Data via Maximum Likelihood Methods,” The B.E. Journal of Theoretical Eco- nomics: Vol. 9: Iss. 1

Volume 7, Issue 1 2011 Article 17 The International Journal of Biostatistics A Targeted Maximum Likelihood Estimator for Two-Stage Designs Sherri Rose, University of California, Berkeley Mark J. van der Laan, University of California, Berkeley Recommended Citation: Rose, Sherri and van der Laan, Mark J. (2011) "A Targeted Maximum Likelihood Estimator for Two-Stage Designs," The International Journal of Biostatistics: Vol. 7: Iss. 1, Article 17. DOI: 10.2202/1557-4679.1217 A Targeted Maximum Likelihood Estimator for Two-Stage Designs Sherri Rose and Mark J. van

the following rejection algorithm: parameter values are randomly drawn from the prior distribution, data sets are then simulated under these values. To reduce complexity, informative but low dimensional summaries are derived from the data sets. All parameter values that gave rise to summary statistics sufficiently similar to those computed for the observed data are then accepted as a sample from the posterior distribution. Hence, with a uniform prior (with support on a compact subset of the parameter space taken large enough to contain the maximum likelihood

Volume 8, Issue 1 2012 Article 16 The International Journal of Biostatistics Maximum Likelihood Estimation for Semiparametric Density Ratio Model Guoqing Diao, George Mason University Jing Ning, University of Texas M.D. Anderson Cancer Center jing qin, biostatistics research brance, NIAID, NIH Recommended Citation: Diao, Guoqing; Ning, Jing; and qin, jing (2012) "Maximum Likelihood Estimation for Semiparametric Density Ratio Model," The International Journal of Biostatistics: Vol. 8: Iss. 1, Article 16. DOI: 10.1515/1557-4679.1372 ©2012 De Gruyter. All rights