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Fitting Statistical Distributions - The Generalized Lambda Distribution and Generalized Bootstrap Methods (Paperback)
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Fitting Statistical Distributions - The Generalized Lambda Distribution and Generalized Bootstrap Methods (Paperback)
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Throughout the physical and social sciences, researchers face the
challenge of fitting statistical distributions to their data.
Although the study of statistical modelling has made great strides
in recent years, the number and variety of distributions to choose
from-all with their own formulas, tables, diagrams, and general
properties-continue to create problems. For a specific application,
which of the dozens of distributions should one use? What if none
of them fit well? Fitting Statistical Distributions helps answer
those questions. Focusing on techniques used successfully across
many fields, the authors present all of the relevant results
related to the Generalized Lambda Distribution (GLD), the
Generalized Bootstrap (GB), and Monte Carlo simulation (MC). They
provide the tables, algorithms, and computer programs needed for
fitting continuous probability distributions to data in a wide
variety of circumstances-covering bivariate as well as univariate
distributions, and including situations where moments do not exist.
Regardless of your specific field-physical science, social science,
or statistics, practitioner or theorist-Fitting Statistical
Distributions is required reading. It includes wide-ranging
applications illustrating the methods in practice and offers proofs
of key results for those involved in theoretical development.
Without it, you may be using obsolete methods, wasting time, and
risking incorrect results.
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