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Publicado por Taylor & Francis Group, 2005
ISBN 10: 1597180122 ISBN 13: 9781597180122
Idioma: Inglés
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Añadir al carritoPaperback. Condición: Good. No Jacket. Pages can have notes/highlighting. Spine may show signs of wear. ~ ThriftBooks: Read More, Spend Less 1.43.
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Añadir al carritoPaperback. Condición: Good. No Jacket. Pages can have notes/highlighting. Spine may show signs of wear. ~ ThriftBooks: Read More, Spend Less 1.43.
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Añadir al carritoFourth Edition. Softcover, still in shrink wrap. For researchers in all disciplines who need to compute maximum likelihood estimators that are not available as pre-packaged routines.
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Añadir al carritoBroschiert. 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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Añadir al carritoPaperback / softback. Condición: New. New copy - Usually dispatched within 4 working days. 960.
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Añadir al carritoCondición: good. Supports Goodwill of Silicon Valley job training programs. The cover and pages are in Good condition! Any other included accessories are also in Good condition showing use. Use can include some highlighting and writing, page and cover creases as well as other types visible wear.
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Añadir al carritoCondición: New. 2023. 5th Edition. paperback. . . . . .
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Añadir al carritoCondición: Good. The book may have minor cosmetic wear (i.e. creased spine/cover, scratches, curled corners, folded pages, minor sunburn, minor water damage, minor bent). The book may have some highlights/notes/underlined pages - Accessories such as CD, codes, toys, may not be included - Safe and Secure Mailer - No Hassle Return.
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Añadir al carritoPaperback. Condición: Brand New. 5th edition. 472 pages. 9.29x7.28x1.18 inches. In Stock.
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Añadir al carritopaperback. Condición: Good. Connecting readers with great books since 1972! Used textbooks may not include companion materials such as access codes, etc. May have some wear or writing/highlighting. We ship orders daily and Customer Service is our top priority!
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Añadir al carritoTaschenbuch. 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 research or for a general-purpose ML estimator.
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Añadir al carritoCondición: 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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Añadir al carritoPaperback. Condición: Good. Connecting readers with great books since 1972! Used textbooks may not include companion materials such as access codes, etc. May have some wear or writing/highlighting. We ship orders daily and Customer Service is our top priority!
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Añadir al carritopaperback. Condición: Very Good. Connecting readers with great books since 1972! Used books may not include companion materials, and may have some shelf wear or limited writing. We ship orders daily and Customer Service is our top priority!
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Añadir al carritoPaperback. Condición: new. Excellent Condition.Excels in customer satisfaction, prompt replies, and quality checks.
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Añadir al carritoCondición: New. 2023. 5th Edition. paperback. . . . . . Books ship from the US and Ireland.
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Añadir al carritoPaperback. 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 overview 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.