Handbook of Fitting Statistical Distributions with R PDF Download
Are you looking for read ebook online? Search for your book and save it on your Kindle device, PC, phones or tablets. Download Handbook of Fitting Statistical Distributions with R PDF full book. Access full book title Handbook of Fitting Statistical Distributions with R by Zaven A. Karian. Download full books in PDF and EPUB format.
Author: Zaven A. Karian Publisher: CRC Press ISBN: 1584887125 Category : Mathematics Languages : en Pages : 1722
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
Author: Publisher: ISBN: Category : Languages : en Pages : 205
Book Description
The Generalized Lambda Distribution (GLD) is a four-parameter, continuous probability distribution that is useful for simulation analysis. The strengths of the GLD lie in its abilities to approximate many distributions, represent data when the underlying distribution is unknown, and fit or generate random variates. The method of moments is presently the accepted technique for estimating the parameters of this distribution. However, it is sensitive to extreme observations and subject to large sampling variability as the sample size decreases. L-moments are expectations of certain linear combinations of order statistics. They can be used to estimate parameters and quantiles of probability distributions. Their main advantage over conventional moments is that they suffer less from the effects of sampling variability, and are theoretically more robust to outliers than conventional moments. Estimating the parameters of the GLD by matching its L-moments to those of the sample is known as the method of L-moments. This appears to be an attractive alternative to the method of moments and is developed in this thesis. A Monte Carlo experiment compared the method of L-moments to the method of conventional moments and a third method which uses alternate measures of symmetry and tailweight. Experiment results showed that L-moments are better than conventional and alternate moments for fitting distributions to sample data, particularly when the skewness and kurtosis of the sample distribution are large. Generalized Lambda distribution, Linear moments.
Author: Zaven A. Karian Publisher: CRC Press ISBN: 1584887125 Category : Mathematics Languages : en Pages : 1722
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
Author: Harvey J. E. Rodda Publisher: John Wiley & Sons ISBN: 1119076595 Category : Technology & Engineering Languages : en Pages : 104
Book Description
Pick up any hydrology textbook and it will not be long before you encounter pages listing sequences of equations representing complex mathematical concepts. Students and practitioners of hydrology will not find this very helpful, as their aim, generally, is to study and understand hydrology, and not to find themselves confronted with material that even students of mathematics would find challenging. Often, equations appear to be copied and pasted into hydrological texts in an attempt to give a more rigorous scientific basis to the narrative. However, they are commonly wrong, poorly explained, without context or background, and more likely to confuse and distance the reader than to enlighten and engage them in the topic. Understanding Mathematical and Statistical Techniques in Hydrology provides full and detailed expositions of such equations and mathematical concepts, commonly used in hydrology. In contrast to other hydrological texts, instead of presenting abstract mathematical hydrology, the essential mathematics is explained with the help of real-world hydrological examples.
Author: Carsten Dormann Publisher: Springer Nature ISBN: 3030550206 Category : Medical Languages : en Pages : 264
Book Description
Environmental Data Analysis is an introductory statistics textbook for environmental science. It covers descriptive, inferential and predictive statistics, centred on the Generalized Linear Model. The key idea behind this book is to approach statistical analyses from the perspective of maximum likelihood, essentially treating most analyses as (multiple) regression problems. The reader will be introduced to statistical distributions early on, and will learn to deploy models suitable for the data at hand, which in environmental science are often not normally distributed. To make the initially steep learning curve more manageable, each statistical chapter is followed by a walk-through in a corresponding R-based how-to chapter, which reviews the theory and applies it to environmental data. In this way, a coherent and expandable foundation in parametric statistics is laid, which can be expanded in advanced courses.The content has been “field-tested” in several years of courses on statistics for Environmental Science, Geography and Forestry taught at the University of Freiburg.
Author: Zaven A Karian Publisher: CRC Press ISBN: 9780367398613 Category : Languages : en Pages : 438
Book Description
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.
Author: Alina A. von Davier Publisher: Springer Science & Business Media ISBN: 0387217193 Category : Business & Economics Languages : en Pages : 244
Book Description
KE is applied to the four major equating designs and to both Chain Equating and Post-Stratification Equating for the Non-Equivalent groups with Anchor Test Design. It will be an important reference for several groups: (a) Statisticians (b) Practitioners and (c) Instructors in psychometric and measurement programs. The authors assume some familiarity with linear and equipercentile test equating, and with matrix algebra.
Author: Publisher: ISBN: Category : Languages : en Pages : 44
Book Description
FITTING is a Fortran subroutine that constructs a smooth, generalized four-parameter probability distribution model. It is fit to the first four statistical moments of the random variable X (i.e., average values of X, X2, X3, and X4) which can be calculated from data using the associated subroutine CALMOM. The generalized model is produced from a cubic distortion of the parent model, calibrated to match the first four moments of the data. This four-moment matching is intended to provide models that are more faithful to the data in the upper tail of the distribution. Examples are shown for two specific cases.
Author: Jessica M. Utts Publisher: Duxbury Resource Center ISBN: 9780534393052 Category : CD-ROMs Languages : en Pages : 0
Book Description
Emphasizing the conceptual development of statistical ideas, MIND ON STATISTICS actively engages students and explains topics in the context of excellent examples and case studies. This text balances the spirit of statistical literacy with statistical methodology taught in the introductory statistics course. Jessica Utts and Robert Heckard built the book on two learning premises: (1) New material is much easier to learn and remember if it is related to something interesting or previously known; (2) New material is easier to learn if you actively ask questions and answer them for yourself. More than any other text available, MIND ON STATISTICS motivates students to develop their statistical intuition by focusing on analyzing data and interpreting results as opposed to focusing on mathematical formulation. The new edition of this exciting text, enhanced with new material and features, appeals to a wide array of students and instructors alike.
Author: Zaven A. Karian
Author: Chun-Yuan Cheng
Author: 
Author: Zaven A. Karian
Author: Harvey J. E. Rodda
Author: Carsten Dormann
Author: Zaven A Karian
Author: Alina A. von Davier
Author: 
Author: 
Author: Jessica M. Utts