Missing Random Effect by Chen (21 results)

Paperback. Condition: Very Good. Missing the Random Effect This book is in very good condition and will be shipped within 24 hours of ordering. The cover may have some limited signs of wear but the pages are clean, intact and the spine remains undamaged. This book has clearly been well maintained and looked after thus far. Money back guarantee if you are not satisfied. See all our books here, order more than 1 book and get discounted shipping. .

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Taschenbuch. Condition: Neu. Missing the Random Effect | When the Parameter Space Is Expanding | Ru Chen | Taschenbuch | 152 S. | Englisch | 2012 | AV Akademikerverlag | EAN 9783639455144 | Verantwortliche Person für die EU: preigu GmbH & Co. KG, Lengericher Landstr. 19, 49078 Osnabrück, mail[at]preigu[dot]de | Anbieter: preigu.

Taschenbuch. Condition: Neu. Neuware - In this project we look at the case when two of fundamental assumptions in the method of Maximum Likelihood are violated. In particular, we study a special class of misspecified models, where the true modelis a mixed effect model but the working model is a fixed effect model with parameters of dimension increasing with sample size. We provide a sufficient condition under which the MLE derived from the working model converges to a well-defined and asymptotically normally-distributed limit. In linear models, the sample variance is biased; but there exists a robust variance estimator of the MLE that converges to the true variance in probability. We also study the Criterion-based automatic model selection methods and find that they may select a linear model that contains spurious variables, but this can be avoided by using the robust variance estimator for the MLE in Bonferroni-adjusted model section or by choosing lamdan that grows fast enough in Shao`s GIC. In generalized linear models, general results are given and computational and simulation studies are carried out to corroborate asymptotic theoretical results as well as to calculate quantities that are not available in theoretical calculation. We find that when the link function in generalized linear mixed models is correctly specified, the estimated parameters have entries that are close to zero except for those corresponding to the fixed effects in the true model. The estimated variance of the MLE is always smaller than the true variance of the MLE, but the robust `sandwich` variance estimator can estimate the true variance very well, and extra significant variables will appear only when the link function is not correctly specified.

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Taschenbuch. Condition: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -Revision with unchanged content. In this project we look at the case when two of fundamental assumptions in the method of Maximum Likelihood are violated. In particular, we study a special class of misspecified models, where the true model is a mixed effect model but the working model is a fixed effect model with parameters of dimension increasing with sample size. We provide a sufficient condition under which the MLE derived from the working model converges to a welldefined and asymptotically normally-distributed limit. In linear models, the sample variance is biased; but there exists a robust variance estimator of the MLE that converges to the true variance in probability. We also study the Criterion-based automatic model selection methods and find that they may select a linear model that contains spurious variables, but this can be avoided by using the robust variance estimator for the MLE in Bonferroni-adjusted model section or by choosing n that grows fast enough in Shao s GIC. In generalized linear models, general results are given and computational and simulation studies are carried out to corroborate asymptotic theoretical results as well as to calculate quantities that are not available in theoretical calculation. 152 pp. Englisch.

Condition: New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. Autor/Autorin: Chen RuPh.D in mathematical statistics fromUnviersity of Maryland at College Park.Lead biostatistician at MannkindCorporation, New Jersey.Revision with unchanged content. In this project we look at the case when two of fundamenta.

Taschenbuch. Condition: Neu. This item is printed on demand - Print on Demand Titel. Neuware -Revision with unchanged content. In this project we look at the case when two of fundamental assumptions in the method of Maximum Likelihood are violated. In particular, we study a special class of misspecified models, where the true model is a mixed effect model but the working model is a fixed effect model with parameters of dimension increasing with sample size. We provide a sufficient condition under which the MLE derived from the working model converges to a welldefined and asymptotically normally-distributed limit. In linear models, the sample variance is biased; but there exists a robust variance estimator of the MLE that converges to the true variance in probability. We also study the Criterion-based automatic model selection methods and find that they may select a linear model that contains spurious variables, but this can be avoided by using the robust variance estimator for the MLE in Bonferroni-adjusted model section or by choosing ¿n that grows fast enough in Shao's GIC. In generalized linear models, general results are given and computational and simulation studies are carried out to corroborate asymptotic theoretical results as well as to calculate quantities that are not available in theoretical calculation.VDM Verlag, Dudweiler Landstraße 99, 66123 Saarbrücken 152 pp. Englisch.

Taschenbuch. Condition: Neu. nach der Bestellung gedruckt Neuware - Printed after ordering - Revision with unchanged content. In this project we look at the case when two of fundamental assumptions in the method of Maximum Likelihood are violated. In particular, we study a special class of misspecified models, where the true model is a mixed effect model but the working model is a fixed effect model with parameters of dimension increasing with sample size. We provide a sufficient condition under which the MLE derived from the working model converges to a welldefined and asymptotically normally-distributed limit. In linear models, the sample variance is biased; but there exists a robust variance estimator of the MLE that converges to the true variance in probability. We also study the Criterion-based automatic model selection methods and find that they may select a linear model that contains spurious variables, but this can be avoided by using the robust variance estimator for the MLE in Bonferroni-adjusted model section or by choosing n that grows fast enough in Shao s GIC. In generalized linear models, general results are given and computational and simulation studies are carried out to corroborate asymptotic theoretical results as well as to calculate quantities that are not available in theoretical calculation.