02897nam 2200565 450 991046001480332120200520144314.01-61251-883-4(CKB)3710000000250219(EBL)1801457(OCoLC)892244081(SSID)ssj0001350829(PQKBManifestationID)12571024(PQKBTitleCode)TC0001350829(PQKBWorkID)11296151(PQKB)10579264(MiAaPQ)EBC1801457(Au-PeEL)EBL1801457(CaPaEBR)ebr10953749(CaONFJC)MIL869281(EXLCZ)99371000000025021920141023h20082008 uy 0engur|n|---|||||txtccrThrough the wheat the U.S. Marines in World War I /Edwin H. Simmons and Joseph H. AlexanderAnnapolis, Maryland :Naval Institute Press,2008.©20081 online resource (327 p.)Description based upon print version of record.1-59114-831-6 Includes bibliographical references and index.Table of Contents; List of Maps ; Foreword by Col. Allan R. Millett, USMCR (Ret.) ; Preface and Acknowledgments; Prologue: Les Mares Farm, Northern France, June 3, 1918; 1. ""The War to End All Wars"" ; 2. Fivefold Expansion ; 3. New Frontiers; 4. ""Over There"" ; 5. The Trenches of Verdun ; 6. ""Retreat, Hell!"" ; 7. Bellau Wood ; 8. ""In Every Clime and Place"" ; 9. Soissons: The First Day ; 10. Soissons: The Second Day ; 11. Marbache and St. Mihiel ; 12. Blanc Mont ; 13. The Meuse-Argonne Campaign ; 14. The Watch on the Rhine ; Epilogue ; Appendix: Medals of Honor Awarded ; NotesBibliography Index ; About the AuthorsU.S. Marine participation in World War I is known as a defining moment in the Marine Corps'' great history. It is a story of exceptional heroism and significant operational achievements, along with lessons learned the hard way. The Marines entered World War I as a small force of seagoing light infantry that had rarely faced a well-armed enemy. On a single June day, in their initial assault ""through the wheat"" on Belleau Wood against German machine-guns and poison gas shells, the Marines suffered more casualties than they had experienced in all their previous 142 years. Yet at Belleau Wood, SWorld War, 1914-1918Regimental historiesUnited StatesElectronic books.World War, 1914-1918Regimental histories940.41273Simmons Edwin H.884780Alexander Joseph H.MiAaPQMiAaPQMiAaPQBOOK9910460014803321Through the wheat1975706UNINA05495nam 2200673 a 450 991087744830332120200520144314.01-281-84100-597866118410030-470-77077-50-470-77078-3(CKB)1000000000549390(EBL)366774(OCoLC)476201818(SSID)ssj0000206842(PQKBManifestationID)11180050(PQKBTitleCode)TC0000206842(PQKBWorkID)10246504(PQKB)10229985(MiAaPQ)EBC366774(PPN)263348644(EXLCZ)99100000000054939020080124d2008 uy 0engur|n|---|||||txtccrMultivariable model-building a pragmatic approach to regression analysis based on fractional polynomials for modelling continuous variables /Patrick Royston, Willi SauerbreiChichester, England ;Hoboken, NJ John Wileyc20081 online resource (323 p.)Wiley series in probability and statisticsDescription based upon print version of record.0-470-02842-4 Includes bibliographical references (p. 271-283) and index.Multivariable Model-Building; Contents; Preface; 1 Introduction; 1.1 Real-Life Problems as Motivation for Model Building; 1.1.1 Many Candidate Models; 1.1.2 Functional Form for Continuous Predictors; 1.1.3 Example 1: Continuous Response; 1.1.4 Example 2: Multivariable Model for Survival Data; 1.2 Issues in Modelling Continuous Predictors; 1.2.1 Effects of Assumptions; 1.2.2 Global versus Local Influence Models; 1.2.3 Disadvantages of Fractional Polynomial Modelling; 1.2.4 Controlling Model Complexity; 1.3 Types of Regression Model Considered; 1.3.1 Normal-Errors Regression1.3.2 Logistic Regression1.3.3 Cox Regression; 1.3.4 Generalized Linear Models; 1.3.5 Linear and Additive Predictors; 1.4 Role of Residuals; 1.4.1 Uses of Residuals; 1.4.2 Graphical Analysis of Residuals; 1.5 Role of Subject-Matter Knowledge in Model Development; 1.6 Scope of Model Building in our Book; 1.7 Modelling Preferences; 1.7.1 General Issues; 1.7.2 Criteria for a Good Model; 1.7.3 Personal Preferences; 1.8 General Notation; 2 Selection of Variables; 2.1 Introduction; 2.2 Background; 2.3 Preliminaries for a Multivariable Analysis; 2.4 Aims of Multivariable Models2.5 Prediction: Summary Statistics and Comparisons2.6 Procedures for Selecting Variables; 2.6.1 Strength of Predictors; 2.6.2 Stepwise Procedures; 2.6.3 All-Subsets Model Selection Using Information Criteria; 2.6.4 Further Considerations; 2.7 Comparison of Selection Strategies in Examples; 2.7.1 Myeloma Study; 2.7.2 Educational Body-Fat Data; 2.7.3 Glioma Study; 2.8 Selection and Shrinkage; 2.8.1 Selection Bias; 2.8.2 Simulation Study; 2.8.3 Shrinkage to Correct for Selection Bias; 2.8.4 Post-estimation Shrinkage; 2.8.5 Reducing Selection Bias; 2.8.6 Example; 2.9 Discussion2.9.1 Model Building in Small Datasets2.9.2 Full, Pre-specified or Selected Model?; 2.9.3 Comparison of Selection Procedures; 2.9.4 Complexity, Stability and Interpretability; 2.9.5 Conclusions and Outlook; 3 Handling Categorical and Continuous Predictors; 3.1 Introduction; 3.2 Types of Predictor; 3.2.1 Binary; 3.2.2 Nominal; 3.2.3 Ordinal, Counting, Continuous; 3.2.4 Derived; 3.3 Handling Ordinal Predictors; 3.3.1 Coding Schemes; 3.3.2 Effect of Coding Schemes on Variable Selection; 3.4 Handling Counting and Continuous Predictors: Categorization3.4.1 'Optimal' Cutpoints: A Dangerous Analysis3.4.2 Other Ways of Choosing a Cutpoint; 3.5 Example: Issues in Model Building with Categorized Variables; 3.5.1 One Ordinal Variable; 3.5.2 Several Ordinal Variables; 3.6 Handling Counting and Continuous Predictors: Functional Form; 3.6.1 Beyond Linearity; 3.6.2 Does Nonlinearity Matter?; 3.6.3 Simple versus Complex Functions; 3.6.4 Interpretability and Transportability; 3.7 Empirical Curve Fitting; 3.7.1 General Approaches to Smoothing; 3.7.2 Critique of Local and Global Influence Models; 3.8 Discussion; 3.8.1 Sparse Categories3.8.2 Choice of Coding SchemeMultivariable regression models are of fundamental importance in all areas of science in which empirical data must be analyzed. This book proposes a systematic approach to building such models based on standard principles of statistical modeling. The main emphasis is on the fractional polynomial method for modeling the influence of continuous variables in a multivariable context, a topic for which there is no standard approach. Existing options range from very simple step functions to highly complex adaptive methods such as multivariate splines with many knots and penalisation. This new approaWiley series in probability and statistics.Regression analysisPolynomialsVariables (Mathematics)Regression analysis.Polynomials.Variables (Mathematics)519.5/36Royston Patrick1341128Sauerbrei Willi1754150MiAaPQMiAaPQMiAaPQBOOK9910877448303321Multivariable model-building4190363UNINA