9780470027264 - Robust Methods in Biostatistics di Heritier, Stephane; Cantoni, Eva; Copt, Samuel; Victoria-feser, Maria-pia (21 risultati)

HRD. Condizione: New. New Book. Shipped from UK. Established seller since 2000.

Condizione: New.

Condizione: new.

Condizione: As New. Unread book in perfect condition.

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Condizione: New.

Hardback. Condizione: New. Robust statistics is an extension of classical statistics that specifically takes into account the concept that the underlying models used to describe data are only approximate. Its basic philosophy is to produce statistical procedures which are stable when the data do not exactly match the postulated models as it is the case for example with outliers. Robust Methods in Biostatistics proposes robust alternatives to common methods used in statistics in general and in biostatistics in particular and illustrates their use on many biomedical datasets. The methods introduced include robust estimation, testing, model selection, model check and diagnostics. They are developed for the following general classes of models: Linear regressionGeneralized linear modelsLinear mixed modelsMarginal longitudinal data modelsCox survival analysis model The methods are introduced both at a theoretical and applied level within the framework of each general class of models, with a particular emphasis put on practical data analysis. This book is of particular use for research students,applied statisticians and practitioners in the health field interested in more stable statistical techniques. An accompanying website provides R code for computing all of the methods described, as well as for analyzing all the datasets used in the book.

Condizione: As New. Unread book in perfect condition.

Condizione: New. pp. 294 Illus.

Condizione: New. * First book on robust techniques to be specifically aimed at biostatistics. * Supported by an accompanying website containing data sets, programs written in R and a user guide. Series: Wiley Series in Probability and Statistics. Num Pages: 294 pages, Illustrations. BIC Classification: PBT; PS. Category: (P) Professional & Vocational. Dimension: 233 x 157 x 21. Weight in Grams: 542. . 2009. 1st Edition. hardcover. . . . .

Condizione: New. pp. 294 Index 1st Edition.

Hardcover. Condizione: Brand New. 1st edition. 268 pages. 9.25x6.25x0.75 inches. In Stock.

Condizione: New. * First book on robust techniques to be specifically aimed at biostatistics. * Supported by an accompanying website containing data sets, programs written in R and a user guide. Series: Wiley Series in Probability and Statistics. Num Pages: 294 pages, Illustrations. BIC Classification: PBT; PS. Category: (P) Professional & Vocational. Dimension: 233 x 157 x 21. Weight in Grams: 542. . 2009. 1st Edition. hardcover. . . . . Books ship from the US and Ireland.

Gebunden. Condizione: New. Robust statistics is an extension of classical statistics that specifically takes into account the concept that the underlying models used to describe data are only approximate. Its basic philosophy is to produce statistical procedures which are stable when.

Hardback. Condizione: New. Robust statistics is an extension of classical statistics that specifically takes into account the concept that the underlying models used to describe data are only approximate. Its basic philosophy is to produce statistical procedures which are stable when the data do not exactly match the postulated models as it is the case for example with outliers. Robust Methods in Biostatistics proposes robust alternatives to common methods used in statistics in general and in biostatistics in particular and illustrates their use on many biomedical datasets. The methods introduced include robust estimation, testing, model selection, model check and diagnostics. They are developed for the following general classes of models: Linear regressionGeneralized linear modelsLinear mixed modelsMarginal longitudinal data modelsCox survival analysis model The methods are introduced both at a theoretical and applied level within the framework of each general class of models, with a particular emphasis put on practical data analysis. This book is of particular use for research students,applied statisticians and practitioners in the health field interested in more stable statistical techniques. An accompanying website provides R code for computing all of the methods described, as well as for analyzing all the datasets used in the book.

Buch. Condizione: Neu. Neuware - Robust statistics is an extension of classical statistics that specifically takes into account the concept that the underlying models used to describe data are only approximate. Its basic philosophy is to produce statistical procedures which are stable when the data do not exactly match the postulated models as it is the case for example with outliers.Robust Methods in Biostatistics proposes robust alternatives to common methods used in statistics in general and in biostatistics in particular and illustrates their use on many biomedical datasets. The methods introduced include robust estimation, testing, model selection, model check and diagnostics. They are developed for the following general classes of models:\* Linear regression\* Generalized linear models\* Linear mixed models\* Marginal longitudinal data models\* Cox survival analysis modelThe methods are introduced both at a theoretical and applied level within the framework of each general class of models, with a particular emphasis put on practical data analysis. This book is of particular use for research students,applied statisticians and practitioners in the health field interested in more stable statistical techniques. An accompanying website provides R code for computing all of the methods described, as well as for analyzing all the datasets used in the book.

Hardcover. Condizione: new. Hardcover. Robust statistics is an extension of classical statistics that specifically takes into account the concept that the underlying models used to describe data are only approximate. Its basic philosophy is to produce statistical procedures which are stable when the data do not exactly match the postulated models as it is the case for example with outliers. Robust Methods in Biostatistics proposes robust alternatives to common methods used in statistics in general and in biostatistics in particular and illustrates their use on many biomedical datasets. The methods introduced include robust estimation, testing, model selection, model check and diagnostics. They are developed for the following general classes of models: Linear regressionGeneralized linear modelsLinear mixed modelsMarginal longitudinal data modelsCox survival analysis model The methods are introduced both at a theoretical and applied level within the framework of each general class of models, with a particular emphasis put on practical data analysis. This book is of particular use for research students,applied statisticians and practitioners in the health field interested in more stable statistical techniques. An accompanying website provides R code for computing all of the methods described, as well as for analyzing all the datasets used in the book. * First book on robust techniques to be specifically aimed at biostatistics. * Supported by an accompanying website containing data sets, programs written in R and a user guide. This item is printed on demand. Shipping may be from multiple locations in the US or from the UK, depending on stock availability.

Hardback. Condizione: New. This item is printed on demand. New copy - Usually dispatched within 5-9 working days.

Hardcover. Condizione: Brand New. 1st edition. 268 pages. 9.25x6.25x0.75 inches. In Stock. This item is printed on demand.

Hardcover. Condizione: new. Hardcover. Robust statistics is an extension of classical statistics that specifically takes into account the concept that the underlying models used to describe data are only approximate. Its basic philosophy is to produce statistical procedures which are stable when the data do not exactly match the postulated models as it is the case for example with outliers. Robust Methods in Biostatistics proposes robust alternatives to common methods used in statistics in general and in biostatistics in particular and illustrates their use on many biomedical datasets. The methods introduced include robust estimation, testing, model selection, model check and diagnostics. They are developed for the following general classes of models: Linear regressionGeneralized linear modelsLinear mixed modelsMarginal longitudinal data modelsCox survival analysis model The methods are introduced both at a theoretical and applied level within the framework of each general class of models, with a particular emphasis put on practical data analysis. This book is of particular use for research students,applied statisticians and practitioners in the health field interested in more stable statistical techniques. An accompanying website provides R code for computing all of the methods described, as well as for analyzing all the datasets used in the book. * First book on robust techniques to be specifically aimed at biostatistics. * Supported by an accompanying website containing data sets, programs written in R and a user guide. This item is printed on demand. Shipping may be from our UK warehouse or from our Australian or US warehouses, depending on stock availability.

Hardcover. Condizione: new. Hardcover. Robust statistics is an extension of classical statistics that specifically takes into account the concept that the underlying models used to describe data are only approximate. Its basic philosophy is to produce statistical procedures which are stable when the data do not exactly match the postulated models as it is the case for example with outliers. Robust Methods in Biostatistics proposes robust alternatives to common methods used in statistics in general and in biostatistics in particular and illustrates their use on many biomedical datasets. The methods introduced include robust estimation, testing, model selection, model check and diagnostics. They are developed for the following general classes of models: Linear regressionGeneralized linear modelsLinear mixed modelsMarginal longitudinal data modelsCox survival analysis model The methods are introduced both at a theoretical and applied level within the framework of each general class of models, with a particular emphasis put on practical data analysis. This book is of particular use for research students,applied statisticians and practitioners in the health field interested in more stable statistical techniques. An accompanying website provides R code for computing all of the methods described, as well as for analyzing all the datasets used in the book. * First book on robust techniques to be specifically aimed at biostatistics. * Supported by an accompanying website containing data sets, programs written in R and a user guide. This item is printed on demand. Shipping may be from our Sydney, NSW warehouse or from our UK or US warehouse, depending on stock availability.