Stock image for illustration purposes only - book cover, edition or condition may vary.
Meta Analysis: A Guide to Calibrating and Combining Statistical Evidence
Elena Kulinskaya
€ 80.36
FREE Delivery in Ireland
Description for Meta Analysis: A Guide to Calibrating and Combining Statistical Evidence
Paperback. Meta Analysis: A Guide to Calibrating and Combining Statistical Evidence acts as a source of basic methods for scientists wanting to combine evidence from different experiments. The authors aim to promote a deeper understanding of the notion of statistical evidence. The book is comprised of two parts - The Handbook, and The Theory. Series: Wiley Series in Probability and Statistics. Num Pages: 282 pages, black & white tables, figures. BIC Classification: PBT. Category: (P) Professional & Vocational. Dimension: 228 x 153 x 17. Weight in Grams: 432.
Meta Analysis: A Guide to Calibrating and Combining Statistical Evidence acts as a source of basic methods for scientists wanting to combine evidence from different experiments. The authors aim to promote a deeper understanding of the notion of statistical evidence.
The book is comprised of two parts – The Handbook, and The Theory. The Handbook is a guide for combining and interpreting experimental evidence to solve standard statistical problems. This section allows someone with a rudimentary knowledge in general statistics to apply the methods. The Theory provides the motivation, theory and results of simulation experiments to justify the methodology.
This is a coherent introduction to the statistical concepts required to understand the authors’ thesis that evidence in a test statistic can often be calibrated when transformed to the right scale.
The book is comprised of two parts – The Handbook, and The Theory. The Handbook is a guide for combining and interpreting experimental evidence to solve standard statistical problems. This section allows someone with a rudimentary knowledge in general statistics to apply the methods. The Theory provides the motivation, theory and results of simulation experiments to justify the methodology.
This is a coherent introduction to the statistical concepts required to understand the authors’ thesis that evidence in a test statistic can often be calibrated when transformed to the right scale.
Product Details
Format
Paperback
Publication date
2008
Publisher
John Wiley & Sons Inc United Kingdom
Number of pages
282
Condition
New
Series
Wiley Series in Probability and Statistics
Number of Pages
288
Place of Publication
, United States
ISBN
9780470028643
SKU
V9780470028643
Shipping Time
Usually ships in 4 to 8 working days
Ref
99-1
About Elena Kulinskaya
Dr. E. Kulinskaya – Director, Statistical Advisory Service, Imperial College, London. Professor S. Morgenthaler – Chair of Applied Statistics, Ecole Polytechnique Fédérale de Lausanne, Switzerland. Professor Morgenthaler was Assistant Professor at Yale University prior to moving to EPFL and has chaired various ISI committees. Professor R. G. Staudte – Department of Statistical Science, La Trobe University, Melbourne. During his career at La Trobe he has served as Head of the Department of Statistical Science for five years and Head of the School of Mathematical and Statistical Sciences for two years. He was an Associate Editor for the Journal of Statistical Planning & Inference for 4 years, and is a member of the American Statistical Association, the Sigma Xi Scientific Research Society and the Statistical Society of Australia.
Reviews for Meta Analysis: A Guide to Calibrating and Combining Statistical Evidence
"A book that offers an alternative, widely applicable, rigorously justified theory of meta-analysis." (Evidence Based Medicine, April 2009) "The book is well written and includes many examples. The book provides an interesting angle on statistical inference by introducing the concept of ‘evidence’. I enjoyed this concept very much." (Statistics in Medicine, May 2009) "I found the book well written, reasonably complete, and easy to read … .I recommend this book for both the new and experienced meta-analysts." (Journal of Biopharmaceutical Statistics, March 2009)