Brian poi (12 resultados)
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Librería: books4less (Versandantiquariat Petra Gros GmbH & Co. KG), Welling, Alemaniabooks4less (Versandantiquariat Petra Gros GmbH & Co. KG)
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Broschiert. Condición: Gut. 4th New edition. XXII; 352 Seiten Der Erhaltungszustand des hier angebotenen Werks ist trotz seiner Bibliotheksnutzung sehr sauber. Es befindet sich neben dem Rückenschild lediglich ein Bibliotheksstempel im Buch; ordnungsgemäß entwidmet. In ENGLISCHER Sprache. Sprache: Englisch Gewicht in Gramm: 740.
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Librería: Majestic Books, Hounslow, Reino UnidoMajestic Books
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EUR 81,74
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Librería: Chiron Media, Wallingford, Reino UnidoChiron Media
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EUR 74,20
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Paperback. Condición: New.
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Librería: Ria Christie Collections, Uxbridge, Reino UnidoRia Christie Collections
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EUR 79,53
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Librería: Books Puddle, New York, NY, Estados Unidos de AmericaBooks Puddle
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EUR 92,92
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Condición: New.
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Librería: Revaluation Books, Exeter, Reino UnidoRevaluation Books
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EUR 85,70
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Paperback. Condición: Brand New. 5th edition. 472 pages. 9.29x7.28x1.18 inches. In Stock.
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Librería: Biblios, frankfurt am main, HESSE, AlemaniaBiblios
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EUR 93,00
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Condición: New.
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Librería: Rarewaves.com USA, London, LONDO, Reino UnidoRarewaves.com USA
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EUR 107,69
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Paperback. Condición: New. Maximum Likelihood Estimation with Stata, Fifth Edition is the essential reference and guide for researchers in all disciplines who wish to write maximum likelihood (ML) estimators in Stata. Beyond providing comprehensive coverage of Stata's commands for writing ML estimators, the book presents an over…view of the underpinnings of maximum likelihood and how to think about ML estimation.The fifth edition includes a new second chapter that demonstrates the easy-to-use mlexp command. This command allows you to directly specify a likelihood function and perform estimation without any programming.The core of the book focuses on Stata's ml command. It shows you how to take full advantage of ml's noteworthy features:Linear constraintsFour optimization algorithms (Newton-Raphson, DFP, BFGS, and BHHH)Observed information matrix (OIM) variance estimatorOuter product of gradients (OPG) variance estimatorHuber/White/sandwich robust variance estimatorCluster-robust variance estimatorComplete and automatic support for survey data analysisDirect support of evaluator functions written in MataWhen appropriate options are used, many of these features are provided automatically by ml and require no special programming or intervention by the researcher writing the estimator.In later chapters, you will learn how to take advantage of Mata, Stata's matrix programming language. For ease of programming and potential speed improvements, you can write your likelihood-evaluator program in Mata and continue to use ml to control the maximization process. A new chapter in the fifth edition shows how you can use the moptimize() suite of Mata functions if you want to implement your maximum likelihood estimator entirely within Mata.In the final chapter, the authors illustrate the major steps required to get from log-likelihood function to fully operational estimation command. This is done using several different models: logit and probit, linear regression, Weibull regression, the Cox proportional hazards model, random-effects regression, and seemingly unrelated regression. This edition adds a new example of a bivariate Poisson model, a model that is not available otherwise in Stata.The authors provide extensive advice for developing your own estimation commands. With a little care and the help of this book, users will be able to write their own estimation commands---commands that look and behave just like the official estimation commands in Stata.Whether you want to fit a special ML estimator for your own research or wish to write a general-purpose ML estimator for others to use, you need this book.
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Librería: moluna, Greven, Alemaniamoluna
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EUR 82,50
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Condición: New. Jeff Pitblado is Executive Director, Statistical Software at StataCorp. Pitblado has played a leading role in the development of ml: he added the ability of ml to work with survey data, and he wrote the current implementation of ml in Ma.
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Librería: AHA-BUCH GmbH, Einbeck, AlemaniaAHA-BUCH GmbH
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EUR 86,71
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Taschenbuch. Condición: Neu. Neuware - Maximum Likelihood Estimation with Stata, Fifth Edition is the essential reference and guide for researchers in all disciplines who wish to write maximum likelihood (ML) estimators in Stata. Learn about ML estimation and how to write Stata code for a special ML estimator for your own resear…ch or for a general-purpose ML estimator.
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Librería: preigu, Osnabrück, Alemaniapreigu
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EUR 92,05
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Taschenbuch. Condición: Neu. Maximum Likelihood Estimation with Stata, Fifth Edition | Brian Poi (u. a.) | Taschenbuch | Einband - flex.(Paperback) | Englisch | 2023 | Stata Press | EAN 9781597184113 | Verantwortliche Person für die EU: Libri GmbH, Europaallee 1, 36244 Bad Hersfeld, gpsr[at]libri[dot]de | Anbieter: preigu.
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Librería: Rarewaves.com UK, London, Reino UnidoRarewaves.com UK
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EUR 100,74
Envío por EUR 75,87Se envía de Reino Unido a Estados Unidos de AmericaCantidad disponible: 1 disponibles
Paperback. Condición: New. Maximum Likelihood Estimation with Stata, Fifth Edition is the essential reference and guide for researchers in all disciplines who wish to write maximum likelihood (ML) estimators in Stata. Beyond providing comprehensive coverage of Stata's commands for writing ML estimators, the book presents an over…view of the underpinnings of maximum likelihood and how to think about ML estimation.The fifth edition includes a new second chapter that demonstrates the easy-to-use mlexp command. This command allows you to directly specify a likelihood function and perform estimation without any programming.The core of the book focuses on Stata's ml command. It shows you how to take full advantage of ml's noteworthy features:Linear constraintsFour optimization algorithms (Newton-Raphson, DFP, BFGS, and BHHH)Observed information matrix (OIM) variance estimatorOuter product of gradients (OPG) variance estimatorHuber/White/sandwich robust variance estimatorCluster-robust variance estimatorComplete and automatic support for survey data analysisDirect support of evaluator functions written in MataWhen appropriate options are used, many of these features are provided automatically by ml and require no special programming or intervention by the researcher writing the estimator.In later chapters, you will learn how to take advantage of Mata, Stata's matrix programming language. For ease of programming and potential speed improvements, you can write your likelihood-evaluator program in Mata and continue to use ml to control the maximization process. A new chapter in the fifth edition shows how you can use the moptimize() suite of Mata functions if you want to implement your maximum likelihood estimator entirely within Mata.In the final chapter, the authors illustrate the major steps required to get from log-likelihood function to fully operational estimation command. This is done using several different models: logit and probit, linear regression, Weibull regression, the Cox proportional hazards model, random-effects regression, and seemingly unrelated regression. This edition adds a new example of a bivariate Poisson model, a model that is not available otherwise in Stata.The authors provide extensive advice for developing your own estimation commands. With a little care and the help of this book, users will be able to write their own estimation commands---commands that look and behave just like the official estimation commands in Stata.Whether you want to fit a special ML estimator for your own research or wish to write a general-purpose ML estimator for others to use, you need this book.
