Poi brian (11 risultati)
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Da: books4less (Versandantiquariat Petra Gros GmbH & Co. KG), Welling, Germaniabooks4less (Versandantiquariat Petra Gros GmbH & Co. KG)
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EUR 47,95
EUR 15,95 spedizioneSpedito da Germania a U.S.A.Quantità: 1 disponibili
Broschiert. Condizione: 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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Da: Majestic Books, Hounslow, Regno UnitoMajestic Books
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EUR 80,88
EUR 7,59 spedizioneSpedito da Regno Unito a U.S.A.Quantità: 3 disponibili
Condizione: New.
- Brossura
Da: Chiron Media, Wallingford, Regno UnitoChiron Media
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EUR 74,19
EUR 18,08 spedizioneSpedito da Regno Unito a U.S.A.Quantità: 1 disponibili
Paperback. Condizione: New.
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Da: Ria Christie Collections, Uxbridge, Regno UnitoRia Christie Collections
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EUR 79,53
EUR 13,98 spedizioneSpedito da Regno Unito a U.S.A.Quantità: 1 disponibili
Condizione: New. In.
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Da: Books Puddle, New York, NY, U.S.A.Books Puddle
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EUR 93,05
EUR 3,45 spedizioneSpedito in U.S.A.Quantità: 3 disponibili
Condizione: New.
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Da: Revaluation Books, Exeter, Regno UnitoRevaluation Books
Contatta il venditoreVenditore con 5 stelleCondizione: Nuovo
EUR 85,69
EUR 14,59 spedizioneSpedito da Regno Unito a U.S.A.Quantità: 1 disponibili
Paperback. Condizione: Brand New. 5th edition. 472 pages. 9.29x7.28x1.18 inches. In Stock.
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Da: Biblios, frankfurt am main, HESSE, GermaniaBiblios
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EUR 93,96
EUR 9,95 spedizioneSpedito da Germania a U.S.A.Quantità: 3 disponibili
Condizione: New.
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Da: Rarewaves.com USA, London, LONDO, Regno UnitoRarewaves.com USA
Contatta il venditoreVenditore con 5 stelleCondizione: Nuovo
EUR 107,22
Spedizione gratuitaSpedito da Regno Unito a U.S.A.Quantità: 1 disponibili
Paperback. Condizione: 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 ove…rview 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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Da: moluna, Greven, Germaniamoluna
Contatta il venditoreVenditore con 5 stelleCondizione: Nuovo
EUR 82,50
EUR 48,99 spedizioneSpedito da Germania a U.S.A.Quantità: 1 disponibili
Condizione: 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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Da: AHA-BUCH GmbH, Einbeck, GermaniaAHA-BUCH GmbH
Contatta il venditoreVenditore con 5 stelleCondizione: Nuovo
EUR 86,71
EUR 64,87 spedizioneSpedito da Germania a U.S.A.Quantità: 2 disponibili
Taschenbuch. Condizione: 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 resea…rch or for a general-purpose ML estimator.
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Da: Rarewaves.com UK, London, Regno UnitoRarewaves.com UK
Contatta il venditoreVenditore con 5 stelleCondizione: Nuovo
EUR 100,73
EUR 75,86 spedizioneSpedito da Regno Unito a U.S.A.Quantità: 1 disponibili
Paperback. Condizione: 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 ove…rview 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.
