A war to be won [[electronic resource] ] : fighting the Second World War / / Williamson Murray, Allan R. Millett

• Record Nr.: UNINA9910454827503321
• Autore: Murray Williamson
• Pubbl/distr/stampa: Cambridge, MA, : Belknap Press of Harvard University Press, 2000
• ISBN: 0-674-04130-5
• Descrizione fisica: 1 online resource (736 p.)
• Altri autori (Persone): MillettAllan Reed
• Disciplina: 940.53
• Soggetti: World War, 1939-1945; History, Modern - 20th century; Electronic books.
• Lingua di pubblicazione: Inglese
• Formato: Materiale a stampa
• Livello bibliografico: Monografia
• Note generali: Description based upon print version of record.
• Nota di bibliografia: Includes bibliographical references (p. 613-637) and index.
• Nota di contenuto: Frontmatter -- PREFACE -- CONTENTS -- MAPS -- 1 Origins of a Catastrophe -- 2 The Revolution in Military Operations, 1919-1939 -- 3 German Designs, 1939-1940 -- 4 Germany Triumphant, 1940 -- 5 Diversions in the Mediterranean and Balkans, 1940-1941 -- 6 Barbarossa, 1941 -- 7 The Origins of the Asia-Pacific War, 1919-1941 -- 8 The Japanese War of Conquest, 1941-1942 -- 9 The Asia-Pacific War, 1942-1944 -- 10 The Battle of the Atlantic, 1939-1943 -- 11 Year of Decision for Germany, 1942 -- 12 The Combined Bomber Offensive, 1941-1945 -- 13 The Destruction of Japanese Naval Power, 1943-1944 -- 14 The Killing Time, 1943-1944 -- 15 The Invasion of France, 1944 -- 16 The End in Europe, 1944-1945 -- 17 The Destruction of the Japanese Empire, 1944-1945 -- 18 The End of the Asia-Pacific War, 1945 -- 19 Peoples at War, 1937-1945 -- 20 The Aftermath of War -- Epilogue: In Retrospect -- Appendixes: -- Notes -- Suggested Reading -- Acknowledgments -- Illustration Credits -- Index
• Sommario/riassunto: In the course of the twentieth century, no war looms as profoundly transformative or as destructive as World War II. Its global scope and human toll reveal the true face of modern, industrialized warfare. Now, for the first time, we have a comprehensive, single-volume account of how and why this global conflict evolved as it did. A War To Be Won is a unique and powerful operational history of the Second World War that ; tells the full story of battle on land, on sea, and in the air. Williamson Murray and Allan R. Millett analyze the operations and tactics that defined the conduct of the war in both the European and Pacific Theaters. Moving between the war room and the battlefield, we see how strategies were crafted and revised, and how the multitudes of combat troops struggled to discharge their orders. The authors present incisive portraits of the military leaders, on both sides of the struggle, demonstrating the ambiguities they faced, the opportunities they took, and those they missed. Throughout, we see the relationship between the actual operations of the war and their political and moral implications. A War To Be Won is the culmination of decades of research by two of America's premier military historians. It avoids a celebratory view of the war but preserves a profound respect for the problems the Allies faced and overcame as well as a realistic assessment of the Axis accomplishments and failures. It is the essential military history of World War II-from the Sino-Japanese War in 1937 to the surrender of Japan in 1945-for students, scholars, and general readers alike.

Analyzing the large numbers of variables in biomedical and satellite imagery / / Phillip I. Good

• Record Nr.: UNINA9910825098303321
• Autore: Good Phillip I
• Pubbl/distr/stampa: Hoboken, N.J., : Wiley, c2011
• ISBN: 9786613138774; 9781283138772; 1283138778; 9780470937259; 0470937254; 9780470937273; 0470937270; 9781118002148; 1118002148
• Edizione: [1st ed.]
• Descrizione fisica: xii, 185 p. : ill
• Disciplina: 006.3/12
• Soggetti: Data mining; Mathematical statistics; Biomedical engineering - Data processing; Remote sensing - Data processing; Functions of several complex variables; R (Computer program language)
• Lingua di pubblicazione: Inglese
• Formato: Materiale a stampa
• Livello bibliografico: Monografia
• Nota di bibliografia: Includes bibliographical references and indexes.
• Nota di contenuto: Machine generated contents note: ; 1. Very Large Arrays -- ; 1.1. Applications -- ; 1.2. Problems -- ; 1.3. Solutions -- ; 2. Permutation Tests -- ; 2.1. Two-Sample Comparison -- ; 2.1.1. Blocks -- ; 2.2. k-Sample Comparison -- ; 2.3. Computing The p-Value -- ; 2.3.1. Monte Carlo Method -- ; 2.3.2. An R Program -- ; 2.4. Multiple-Variable Comparisons -- ; 2.4.1. Euclidean Distance Matrix Analysis -- ; 2.4.2. Hotelling's T2 -- ; 2.4.3. Mantel's U -- ; 2.4.4. Combining Univariate Tests -- ; 2.4.5. Gene Set Enrichment Analysis -- ; 2.5. Categorical Data -- ; 2.6. Software -- ; 2.7. Summary -- ; 3. Applying the Permutation Test -- ; 3.1. Which Variables Should Be Included? -- ; 3.2. Single-Value Test Statistics -- ; 3.2.1. Categorical Data -- ; 3.2.2. A Multivariate Comparison Based on a Summary Statistic -- ; 3.2.3. A Multivariate Comparison Based on Variants of Hotelling's T2; ; 3.2.4. Adjusting for Covariates -- ; 3.2.5. Pre-Post Comparisons -- ; 3.2.6. Choosing a Statistic: Time-Course Microarrays -- ; 3.3. Recommended Approaches -- ; 3.4. To Learn More -- ; 4. Biological Background -- ; 4.1. Medical Imaging -- ; 4.1.1. Ultrasound -- ; 4.1.2. EEG/MEG -- ; 4.1.3. Magnetic Resonance Imaging -- ; 4.1.3.1. MRI -- ; 4.1.3.2. fMRI -- ; 4.1.4. Positron Emission Tomography -- ; 4.2. Microarrays -- ; 4.3. To Learn More -- ; 5. Multiple Tests -- ; 5.1. Reducing the Number of Hypotheses to Be Tested -- ; 5.1.1. Normalization -- ; 5.1.2. Selection Methods -- ; 5.1.2.1. Univariate Statistics -- ; 5.1.2.2. Which Statistic? -- ; 5.1.2.3. Heuristic Methods -- ; 5.1.2.4. Which Method? -- ; 5.2. Controlling the Over All Error Rate -- ; 5.2.1. An Example: Analyzing Data from Microarrays -- ; 5.3. Controlling the False Discovery Rate -- ; 5.3.1. An Example: Analyzing Time-Course Data from Microarrays -- ; 5.4. Gene Set Enrichment Analysis; ; 5.5. Software for Performing Multiple Simultaneous Tests -- ; 5.5.1. AFNI -- ; 5.5.2. Cyber-T -- ; 5.5.3. dChip -- ; 5.5.4. ExactFDR -- ; 5.5.5. GESS -- ; 5.5.6. HaploView -- ; 5.5.7. MatLab -- ; 5.5.8. R -- ; 5.5.9. SAM -- ; 5.5.10. ParaSam -- ; 5.6. Summary -- ; 5.7. To Learn More -- ; 6. The Bootstrap -- ; 6.1. Samples and Populations -- ; 6.2. Precision of an Estimate -- ; 6.2.1. R Code -- ; 6.2.2. Applying the Bootstrap -- ; 6.2.3. Bootstrap Reproducibility Index -- ; 6.2.4. Estimation in Regression Models -- ; 6.3. Confidence Intervals -- ; 6.3.1. Testing for Equivalence -- ; 6.3.2. Parametric Bootstrap -- ; 6.3.3. Blocked Bootstrap -- ; 6.3.4. Balanced Bootstrap -- ; 6.3.5. Adjusted Bootstrap -- ; 6.3.6. Which Test? -- ; 6.4. Determining Sample Size -- ; 6.4.1. Establish a Threshold -- ; 6.5. Validation -- ; 6.5.1. Cluster Analysis -- ; 6.5.2. Correspondence Analysis -- ; 6.6. Building a Model -- ; 6.7. How Large Should The Samples Be?; ; 6.8. Summary -- ; 6.9. To Learn More -- ; 7. Classification Methods -- ; 7.1. Nearest Neighbor Methods -- ; 7.2. Discriminant Analysis -- ; 7.3. Logistic Regression -- ; 7.4. Principal Components -- ; 7.5. Naive Bayes Classifier -- ; 7.6. Heuristic Methods -- ; 7.7. Decision Trees -- ; 7.7.1. A Worked-Through Example -- ; 7.8. Which Algorithm Is Best for ; Your Application? -- ; 7.8.1. Some Further Comparisons -- ; 7.8.2. Validation Versus Cross-validation -- ; 7.9. Improving Diagnostic Effectiveness -- ; 7.9.1. Boosting -- ; 7.9.2. Ensemble Methods -- ; 7.9.3. Random Forests -- ; 7.10. Software for Decision Trees -- ; 7.11. Summary -- ; 8. Applying Decision Trees -- ; 8.1. Photographs -- ; 8.2. Ultrasound -- ; 8.3. MRI Images -- ; 8.4. EEGs and EMGs -- ; 8.5. Misclassification Costs -- ; 8.6. Receiver Operating Characteristic -- ; 8.7. When the Categories Are As Yet Undefined -- ; 8.7.1. Unsupervised Principal Components Applied to fMRI; ; 8.7.2. Supervised Principal Components Applied to Microarrays -- ; 8.8. Ensemble Methods -- ; 8.9. Maximally Diversified Multiple Trees -- ; 8.10. Putting It All Together -- ; 8.11. Summary -- ; 8.12. To Learn More -- Glossary of Biomedical Terminology -- Glossary of Statistical Terminology -- Appendix: An R Primer -- ; R1. Getting Started -- ; R1.1. R Functions -- ; R1.2. Vector Arithmetic -- ; R2. Store and Retrieve Data -- ; R2.1. Storing and Retrieving Files from Within R -- ; R2.2. The Tabular Format -- ; R2.3. Comma Separated Format -- ; R3. Resampling -- ; R3.1. The While Command -- ; R4. Expanding R's Capabilities -- ; R4.1. Downloading Libraries of R Functions -- ; R4.2. Programming Your Own Functions.
• Sommario/riassunto: This book grew out of an online interactive offered through statcourse.com, and it soon became apparent to the author that the course was too limited in terms of time and length in light of the broad backgrounds of the enrolled students. The statisticians who took the course needed to be brought up to speed both on the biological context as well as on the specialized statistical methods needed to handle large arrays. Biologists and physicians, even though fully knowledgeable concerning the procedures used to generate microaarrays, EEGs, or MRIs, needed a full introduction to the resampling methods-the bootstrap, decision trees, and permutation tests, before the specialized methods applicable to large arrays could be introduced. As the intended audience for this book consists both of statisticians and of medical and biological research workers as well as all those research workers who make use of satellite imagery including agronomists and meteorologists, the book provides a step-by-step approach to not only the specialized methods needed to analyze the data from microarrays and images, but also to the resampling methods, step-down multi-comparison procedures, multivariate analysis, as well as data collection and pre-processing. While many alternate techniques for analysis have been introduced in the past decade, the author has selected only those techniques for which software is available along with a list of the available links from which the software may be purchased or downloaded without charge. Topical coverage includes: very large arrays; permutation tests; applying permutation tests; gathering and preparing data for analysis; multiple tests; bootstrap; applying the bootstrap; classification methods; decision trees; and applying decision trees.