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Handbook of Fitting Statistical Distributions with R

Authored by: Zaven A. Karian , Edward J. Dudewicz , Kunio Shimizu

Print publication date:  October  2010
Online publication date:  April  2016

Print ISBN: 9781584887119
eBook ISBN: 9781584887126
Adobe ISBN:

10.1201/b10159
 Cite  Marc Record

Book description

With the development of new fitting methods, their increased use in applications, and improved computer languages, the fitting of statistical distributions to data has come a long way since the introduction of the generalized lambda distribution (GLD) in 1969. Handbook of Fitting Statistical Distributions with R presents the latest and best methods, algorithms, and computations for fitting distributions to data. It also provides in-depth coverage of cutting-edge applications.

The book begins with commentary by three GLD pioneers: John S. Ramberg, Bruce Schmeiser, and Pandu R. Tadikamalla. These leaders of the field give their perspectives on the development of the GLD. The book then covers GLD methodology and Johnson, kappa, and response modeling methodology fitting systems. It also describes recent additions to GLD and generalized bootstrap methods as well as a new approach to goodness-of-fit assessment. The final group of chapters explores real-world applications in agriculture, reliability estimation, hurricanes/typhoons/cyclones, hail storms, water systems, insurance and inventory management, and materials science. The applications in these chapters complement others in the book that deal with competitive bidding, medicine, biology, meteorology, bioassays, economics, quality management, engineering, control, and planning.

New results in the field have generated a rich array of methods for practitioners. Making sense of this extensive growth, this comprehensive and authoritative handbook improves your understanding of the methodology and applications of fitting statistical distributions. The accompanying CD-ROM includes the R programs used for many of the computations.

Table of contents

Chapter  1:  Fitting Statistical Distributions: An Overview Download PDF
Chapter  2:  The Generalized Lambda Family of Distributions Download PDF
Chapter  3:  Fitting Distributions and Data with the GLD via the Method of Moments Download PDF
Chapter  4:  The Extended GLD System, the EGLD: Fitting by the Method of Moments Download PDF
Chapter  5:  A Percentile-Based Approach to Fitting Distributions and Data with the GLD Download PDF
Chapter  6:  Fitting Distributions and Data with the GLD through L-Moments Download PDF
Chapter  7:  Fitting a Generalized Lambda Distribution Using a Percentile-KS (P-KS) Adequacy Criterion Download PDF
Chapter  8:  Fitting Mixture Distributions Using a Mixture of Generalized Lambda Distributions with Computer Code Download PDF
Chapter  9:  GLD–2: The Bivariate GLD Distribution Download PDF
Chapter  10:  Fitting the Generalized Lambda Distribution with Location and Scale-Free Shape Functionals  Download PDF
Chapter  11:  Statistical Design of Experiments: A Short Review Download PDF
Chapter  12:  Statistical Modeling Based on Quantile Distribution Functions Download PDF
Chapter  13:  Distribution Fitting with the Quantile Function of Response Modeling Methodology (RMM) Download PDF
Chapter  14:  Fitting GLDs and Mixture of GLDs to Data Using Quantile Matching Method Download PDF
Chapter  15:  Fitting GLD to Data Using GLDEX 1.0.4 in R Download PDF
Chapter  16:  Fitting Distributions and Data with the Johnson System via the Method of Moments Download PDF
Chapter  17:  Fitting Distributions and Data with the Kappa Distribution through L-Moments and Percentiles Download PDF
Chapter  18:  Weighted Distributional Lα Estimates Download PDF
Chapter  19:  A Multivariate Gamma Distribution for Linearly Related Proportional Outcomes  Download PDF
Chapter  20:  The Generalized Bootstrap (GB) and Monte Carlo (MC) Methods Download PDF
Chapter  21:  The Generalized Bootstrap: A New Fitting Strategy and Simulation Study Showing Advantage over Bootstrap Percentile Methods  Download PDF
Chapter  22:  Generalized Bootstrap Confidence Intervals for High Quantiles Download PDF
Chapter  23:  Goodness-of-Fit Criteria Based on Observations Quantized by Hypothetical and Empirical Percentiles Download PDF
Chapter  24:  Evidential Support Continuum (ESC): A New Approach to Goodness-of-Fit Assessment, which Addresses Conceptual and Practical Challenges Download PDF
Chapter  25:  Estimation of Sampling Distributions of the Overlapping Coefficient and Other Similarity Measures Download PDF
Chapter  26:  Fitting Statistical Distribution Functions to Small Datasets Download PDF
Chapter  27:  Mixed Truncated Random Variable Fitting with the GLD, and Applications in Insurance and Inventory Management Download PDF
Chapter  28:  Distributional Modeling of Pipeline Leakage Repair Costs for a Water Utility Company  Download PDF
Chapter  29:  Use of the Generalized Lambda Distribution in Materials Science, with Examples in Fatigue Lifetime, Fracture Mechanics, Polycrystalline Calculations, and Pitting Corrosion Download PDF
Chapter  30:  Fitting Statistical Distributions to Data in Hurricane Modeling  Download PDF
Chapter  31:  A Rainfall-Based Model for Predicting the Regional Incidence of Wheat Seed Infection by Stagonospora nodorum in New York  Download PDF
Chapter  32:  Reliability Estimation Using Univariate Dimension Reduction and Extended Generalized Lambda Distribution  Download PDF
Chapter  33:  Statistical Analyses of Environmental Pressure Surrounding Atlantic Tropical Cyclones Download PDF
Chapter  34:  Simulating Hail Storms Using Simultaneous Efficient Random Number Generators Download PDF
AppendixE Download PDF
AppendixA Download PDF
AppendixI Download PDF
prelims Download PDF
AppendixC Download PDF
AppendixK Download PDF
AppendixG Download PDF
AppendixD Download PDF
AppendixH Download PDF
AppendixB Download PDF
AppendixJ Download PDF
AppendixF Download PDF
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