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Science/Engineering Statistical Mechanics, Third Edition
Science/Engineering Essentials of Toxic Chemical Risk: Science and Society
Science/Engineering Telefoncoaching: So machen Sie aus Ihren Mitarbeitern Telefonprofis
Science/Engineering Wireless Communications (Wiley - IEEE)
Science/Engineering Posttraumatische Belastungsstörungen (German Edition)
Science/Engineering Lernplattformen in Schulen: Ansätze für E-Learning und Blended Learning in Präsenzklassen (1 Auflage)
Science/Engineering Stochastik für Einsteiger: Eine Einführung in die faszinierende Welt des Zufalls. Mit über 220 Übungsaufgaben und Lösungen {Repost}
Science/Engineering Testtheorie und Fragebogenkonstruktion (Springer-Lehrbuch)
Science/Engineering Centrifugal Pumps, 2nd Edition
Science/Engineering Computational Intelligence for Modelling and Prediction (Studies in Computational Intelligence) 1 edition {Repost}
Science/Engineering Networks, Crowds, and Markets: Reasoning About a Highly Connected World {repost}
Science/Engineering Introduction to Biophotonics (repost)
Science/Engineering The Art and Science of Psychotherapy (repost)
Science/Engineering Advances in Chemical Physics - Volume 15: Stochastic Processes in Chemical Physics
Science/Engineering "Emulsion Science: Basic Principles" (repost)
Science/Engineering Elementary Principles of Chemical Processes 3rd edition
Science/Engineering Boundary Element Analysis (repost)
Science/Engineering Collection of books on physics 2
Science/Engineering A Practical Handbook of Preparative HPLC by Donald A. Wellings (Repost)
Science/Engineering Reviews of Environmental Contamination and Toxicology 184 by George W. Ware
Science/Engineering Essentials of Toxic Chemical Risk: Science and Society
Science/Engineering Telefoncoaching: So machen Sie aus Ihren Mitarbeitern Telefonprofis
Science/Engineering Wireless Communications (Wiley - IEEE)
Science/Engineering Posttraumatische Belastungsstörungen (German Edition)
Science/Engineering Lernplattformen in Schulen: Ansätze für E-Learning und Blended Learning in Präsenzklassen (1 Auflage)
Science/Engineering Stochastik für Einsteiger: Eine Einführung in die faszinierende Welt des Zufalls. Mit über 220 Übungsaufgaben und Lösungen {Repost}
Science/Engineering Testtheorie und Fragebogenkonstruktion (Springer-Lehrbuch)
Science/Engineering Centrifugal Pumps, 2nd Edition
Science/Engineering Computational Intelligence for Modelling and Prediction (Studies in Computational Intelligence) 1 edition {Repost}
Science/Engineering Networks, Crowds, and Markets: Reasoning About a Highly Connected World {repost}
Science/Engineering Introduction to Biophotonics (repost)
Science/Engineering The Art and Science of Psychotherapy (repost)
Science/Engineering Advances in Chemical Physics - Volume 15: Stochastic Processes in Chemical Physics
Science/Engineering "Emulsion Science: Basic Principles" (repost)
Science/Engineering Elementary Principles of Chemical Processes 3rd edition
Science/Engineering Boundary Element Analysis (repost)
Science/Engineering Collection of books on physics 2
Science/Engineering A Practical Handbook of Preparative HPLC by Donald A. Wellings (Repost)
Science/Engineering Reviews of Environmental Contamination and Toxicology 184 by George W. Ware
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Science/Engineering Maximum Likelihood Estimation: Logic and Practice
Posted on 2010-03-16
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More Scott R. Eliason,"Maximum Likelihood Estimation: Logic and Practice" Eliason reveals to the reader the underlying logic and practice of maximum likelihood (ML) estimation by providing a general modeling framework that utilizes the tools of ML methods. This framework offers readers a flexible modeling strategy since it accommodates cases from the simplest linear models (such as the normal error regression model) to the most complex nonlinear models that link a system of endogenous and exogenous variables with non-normal distributions. Using examples to illustrate the techniques of finding ML estimators and estimates, Eliason discusses what properties are desirable in an estimator, basic techniques for finding maximum likelihood solutions, the general form of the covariance matrix for ML estimates, the sampling distribution of ML estimators; the use of ML in the normal as well as other distributions, and some useful illustrations of likelihoods.
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