Webb annabel (19 résultats)

paperback. Etat : Acceptable. Please see the condition note after this for details, if this is missing please consider Acceptable to mean poor quality that could include major staining, water damage, writing, missing dustjacket, etc etc. Our books are dispatched from a Yorkshire former cotton mill. We list via barcode/ISBN so please note that the images are stock images and may not be the exact copy you receive, furthermore the details about edition and year might not be accurate as many publishers reuse the same ISBN for multiple editions and as we simply scan a barcode or enter an ISBN we do not check the validity of the edition data when listing. If you're looking for an exact edition please don't order (at least not without checking with us first, although we don't always have time to check). We aim to dispatch prompty, the service used will depend on order value and book size. We can ship to most countries, see our shipping policies. Payment is via Abe only. …

Etat : very_good. Gently read. May have name of previous ownership, or ex-library edition. Binding tight; spine straight and smooth, with no creasing; covers clean and crisp. Minimal signs of handling or shelving. 100% GUARANTEE! Shipped with delivery confirmation, if you're not satisfied with purchase please return item! Ships USPS Media Mail. Barclay, Sue; Berridge, Richard; Brand, Maggie; Cooke, David; Galbraith, Sheila; Geissler, Peter; Gower, Jeremy; Milne, Annabel; Pyke, Jeremy; Tourret, Gwen; Tourret, Pat; Webb, David (illustrateur).…

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Etat : New.

Etat : As New. Unread book in perfect condition.

Etat : New.

Etat : New.

Etat : As New. Unread book in perfect condition.

Etat : New. 1st edition NO-PA16APR2015-KAP.

Etat : New.

Etat : New.

Etat : New. In English.

Hardback. Etat : New. Many conventional survival analysis methods, such as the Kaplan-Meier method for survival function estimation and the partial likelihood method for Cox model regression coefficients estimation, were developed under the assumption that survival times are subject to right censoring only. However, in practice, survival time observations may include interval-censored data, especially when the exact time of the event of interest cannot be observed. When interval-censored observations are present in a survival dataset, one generally needs to consider likelihood-based methods for inference. If the survival model under consideration is fully parametric, then likelihood-based methods impose neither theoretical nor computational challenges. However, if the model is semi-parametric, there will be difficulties in both theoretical and computational aspects.Likelihood Methods in Survival Analysis: With R Examples explores these challenges and provides practical solutions. It not only covers conventional Cox models where survival times are subject to interval censoring, but also extends to more complicated models, such as stratified Cox models, extended Cox models where time-varying covariates are present, mixture cure Cox models, and Cox models with dependent right censoring. The book also discusses non-Cox models, particularly the additive hazards model and parametric log-linear models for bivariate survival times where there is dependence among competing outcomes.FeaturesProvides a broad and accessible overview of likelihood methods in survival analysisCovers a wide range of data types and models, from the semi-parametric Cox model with interval censoring through to parametric survival models for competing risksIncludes many examples using real data to illustrate the methodsIncludes integrated R code for implementation of the methodsSupplemented by a GitHub repository with datasets and R codeThe book will make an ideal reference for researchers and graduate students of biostatistics, statistics, and data science, whose interest in survival analysis extend beyond applications. It offers useful and solid training to those who wish to enhance their knowledge in the methodology and computational aspects of biostatistics.…

Hardcover. Etat : Brand New. 404 pages. 9.18x6.12x9.21 inches. In Stock.

Hardback. Etat : New. Many conventional survival analysis methods, such as the Kaplan-Meier method for survival function estimation and the partial likelihood method for Cox model regression coefficients estimation, were developed under the assumption that survival times are subject to right censoring only. However, in practice, survival time observations may include interval-censored data, especially when the exact time of the event of interest cannot be observed. When interval-censored observations are present in a survival dataset, one generally needs to consider likelihood-based methods for inference. If the survival model under consideration is fully parametric, then likelihood-based methods impose neither theoretical nor computational challenges. However, if the model is semi-parametric, there will be difficulties in both theoretical and computational aspects.Likelihood Methods in Survival Analysis: With R Examples explores these challenges and provides practical solutions. It not only covers conventional Cox models where survival times are subject to interval censoring, but also extends to more complicated models, such as stratified Cox models, extended Cox models where time-varying covariates are present, mixture cure Cox models, and Cox models with dependent right censoring. The book also discusses non-Cox models, particularly the additive hazards model and parametric log-linear models for bivariate survival times where there is dependence among competing outcomes.FeaturesProvides a broad and accessible overview of likelihood methods in survival analysisCovers a wide range of data types and models, from the semi-parametric Cox model with interval censoring through to parametric survival models for competing risksIncludes many examples using real data to illustrate the methodsIncludes integrated R code for implementation of the methodsSupplemented by a GitHub repository with datasets and R codeThe book will make an ideal reference for researchers and graduate students of biostatistics, statistics, and data science, whose interest in survival analysis extend beyond applications. It offers useful and solid training to those who wish to enhance their knowledge in the methodology and computational aspects of biostatistics.…

Etat : New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. Jun Ma, School of Mathematical and Physical Sciences, Macquarie University, North Ryde, AustraliaAnnabel Webb, School of Mathematical and Physical Sciences, Macquarie University, North Ryde, AustraliaMalcolm Hudson, S. …