LEADER 05527oam 2200709I 450 001 9910458777703321 005 20200520144314.0 010 $a0-429-14078-9 010 $a1-58488-712-5 024 7 $a10.1201/b10159 035 $a(CKB)2670000000060716 035 $a(EBL)624984 035 $a(OCoLC)693330331 035 $a(SSID)ssj0000419201 035 $a(PQKBManifestationID)11267896 035 $a(PQKBTitleCode)TC0000419201 035 $a(PQKBWorkID)10382145 035 $a(PQKB)11762303 035 $a(MiAaPQ)EBC624984 035 $a(MiAaPQ)EBC4009999 035 $a(PPN)165238194 035 $a(Au-PeEL)EBL624984 035 $a(CaPaEBR)ebr10433633 035 $a(CaONFJC)MIL698053 035 $a(Au-PeEL)EBL4009999 035 $a(OCoLC)781319007 035 $a(EXLCZ)992670000000060716 100 $a20180331d2011 uy 0 101 0 $aeng 135 $aur|n|---||||| 181 $ctxt 182 $cc 183 $acr 200 10$aHandbook of fitting statistical distributions with R /$fZaven A. Karian, Edward J. Dudewicz 210 1$aBoca Raton :$cCRC Press,$d2011. 215 $a1 online resource (1722 p.) 300 $aDescription based upon print version of record. 311 $a1-322-66771-3 311 $a1-58488-711-7 320 $aIncludes bibliographical references and index. 327 $aPreface; About the Authors; Dedication; Comments from GLD Pioneers; CONTENTS; PART I: Overview; 1 Fitting Statistical Distributions: An Overview; PART II: The Generalized Lambda Distribution; 2 The Generalized Lambda Family of Distributions; 3 Fitting Distributions and Data with the GLDvia the Method of Moments; 4 The Extended GLD System, the EGLD:Fitting by the Method of Moments; 5 A Percentile-Based Approach to Fitting Distributionsand Data with the GLD; 6 Fitting Distributions and Data with the GLDthrough L-Moments 327 $a7 Fitting a Generalized Lambda Distribution Using aPercentile-KS (P-KS) Adequacy Criterion8 Fitting Mixture Distributions Using a Mixture of GeneralizedLambda Distributions with Computer Code; 9 GLD-2: The Bivariate GLD Distribution; 10 Fitting the Generalized Lambda Distribution with Locationand Scale-Free Shape Functionals; 11 Statistical Design of Experiments: A Short Review; PART III: Quantile Distribution Methods; 12 Statistical Modeling Based on Quantile DistributionFunctions; 13 Distribution Fitting with the Quantile Function ofResponse Modeling Methodology (RMM) 327 $a14 Fitting GLDs and Mixture of GLDs to Data UsingQuantile Matching Method15 Fitting GLD to Data Using GLDEX 1.0.4 in R; PART IV: Other Families of Distributions; 16 Fitting Distributions and Data with the Johnson Systemvia the Method of Moments; 17 Fitting Distributions and Data with the Kappa Distributionthrough L-Moments and Percentiles; 18 Weighted Distributional L? Estimates; 19 A Multivariate Gamma Distribution for Linearly Related Proportional Outcomes; PART V: The Generalized Bootstrap and Monte Carlo Methods; 20 The Generalized Bootstrap (GB) and Monte Carlo (MC) Methods 327 $a21 The Generalized Bootstrap: A New Fitting Strategy and Simulation Study Showing Advantage over Bootstrap Percentile Methods22 Generalized Bootstrap Confidence Intervals for High Quantiles; PART VI: Assessment of the Quality of Fits; 23 Goodness-of-Fit Criteria Based on Observations Quantized by Hypothetical and Empirical Percentiles; 24 Evidential Support Continuum (ESC): A New Approach to Goodness-of-Fit Assessment, which Addresses Conceptual and Practical Challenges; 25 Estimation of Sampling Distributions of the Overlapping Coefficient and Other Similarity Measures 327 $aPART VII: Applications26 Fitting Statistical Distribution Functions to Small Datasets; 27 Mixed Truncated Random Variable Fitting with the GLD, and Applications in Insurance and Inventory Management; 28 Distributional Modeling of Pipeline Leakage Repair Costs for a Water Utility Company; 29 Use of the Generalized Lambda Distribution in Materials Science, with Examples in Fatigue Lifetime, Fracture Mechanics, Polycrystalline Calculations, and Pitting Corrosion; 30 Fitting Statistical Distributions to Data in Hurricane Modeling 327 $a31 A Rainfall-Based Model for Predicting the Regional Incidence of Wheat Seed Infection by Stagonospora nodorum in New York 330 $aStrengthened by examples taken from the scientific literature, this handbook provides statisticians and researchers across the physical and social sciences with cutting-edge methods for fitting continuous probability distributions. It presents families with wide-ranging applicability, including Johnson's system, kappa distribution, and generalized lambda distribution. By providing the necessary R programs, the book enables practitioners to implement the techniques using R computer code. 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