The Folded Normal Distribution
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Author | : F. C. Leone |
Publisher | : |
Total Pages | : 44 |
Release | : 1961 |
Genre | : Analysis of variance |
ISBN | : |
We consider first the folded normal probability density function, especially as it relates to the original normal population from which it came. We present some maximum likelihood estimates, followed by other estimating procedures which are simpler to handle...Finally, an example of real camber data is presented with the appropriate estimation of the theoretical distributions. Some remarks of the folded normal and other work being done on this conclude the paper.
Author | : N. L. Johnson |
Publisher | : |
Total Pages | : 30 |
Release | : 1961 |
Genre | : Analysis of variance |
ISBN | : |
Formulae for the asymptotic variances and covariance of the maximum likelihood estimators of the parameters of the folded normal distribution are obtained. Numerical comparisons with the asymptotic variances of moments estimators are made. (Author).
Author | : Regina C. Elandt |
Publisher | : |
Total Pages | : 62 |
Release | : 1961 |
Genre | : Gaussian distribution |
ISBN | : |
The general formula for the rth moment of the folded normal distribution is obtained, and formulae for the first four non-central and central moments are calculated explicitly. To illustrate the mode of convergence of teh folded normal to the normal distribution, as [mu]/ơ = [theta] increases, the shape factors ßf1 and ßf2 were calculated and the relationship between them represented graphically. Two methods, one using first and second moments (Method I) and the other using second and fourth moments (Method II) of estimating the parameters of [mu] and ơ of the parent normal distribution are presented and their standard errors calculated. The accuracy of both methods, for various values of [theta], are discussed.
Author | : Mohammad Ahsanullah |
Publisher | : Springer Science & Business Media |
Total Pages | : 163 |
Release | : 2014-02-07 |
Genre | : Mathematics |
ISBN | : 9462390614 |
The most important properties of normal and Student t-distributions are presented. A number of applications of these properties are demonstrated. New related results dealing with the distributions of the sum, product and ratio of the independent normal and Student distributions are presented. The materials will be useful to the advanced undergraduate and graduate students and practitioners in the various fields of science and engineering.
Author | : Catherine Lynch Pleil |
Publisher | : |
Total Pages | : 50 |
Release | : 1979 |
Genre | : |
ISBN | : |
Author | : Stelios Psarakis |
Publisher | : |
Total Pages | : 18 |
Release | : 2006 |
Genre | : |
ISBN | : |
Measurements are frequently recorder without their algebraic sign. As a consequence the underlying distribution of measurements is replaced by a distribution of absolute measurements. When the underlying distribution is t the resulting distribution is called the quot;folded-t distributionquot;. Here we study this distribution, we find the relationship between the folded-t distribution and a special case of the folded normal distribution and we derive relationships of the folded-t distribution to other distributions pertaining to computer generation. Also tables are presented which give areas of the folded-t distribution.
Author | : Stelios Psarakis |
Publisher | : |
Total Pages | : 17 |
Release | : 2006 |
Genre | : |
ISBN | : |
In this paper two new bivariate distributions are defined and studied. They are two-variate versions of the folded normal distribution (Leone et al. 1961) and the folded t distribution (Psarakis and Panaretos 1990).They both arise in the context of evaluating the predictive behaviour of two competing linear models with the aim to select the one that leads to predictions closer to the actual value of the dependent variable.
Author | : Samuel Kotz |
Publisher | : Springer Science & Business Media |
Total Pages | : 358 |
Release | : 2012-12-06 |
Genre | : Mathematics |
ISBN | : 146120173X |
This book describes the inferential and modeling advantages that this distribution, together with its generalizations and modifications, offers. The exposition systematically unfolds with many examples, tables, illustrations, and exercises. A comprehensive index and extensive bibliography also make this book an ideal text for a senior undergraduate and graduate seminar on statistical distributions, or for a short half-term academic course in statistics, applied probability, and finance.
Author | : Gavin E Crooks |
Publisher | : |
Total Pages | : 210 |
Release | : 2019-04 |
Genre | : |
ISBN | : 9781733938105 |
A common problem is that of describing the probability distribution of a single, continuous variable. A few distributions, such as the normal and exponential, were discovered in the 1800's or earlier. But about a century ago the great statistician, Karl Pearson, realized that the known probability distributions were not sufficient to handle all of the phenomena then under investigation, and set out to create new distributions with useful properties. During the 20th century this process continued with abandon and a vast menagerie of distinct mathematical forms were discovered and invented, investigated, analyzed, rediscovered and renamed, all for the purpose of describing the probability of some interesting variable. There are hundreds of named distributions and synonyms in current usage. The apparent diversity is unending and disorienting. Fortunately, the situation is less confused than it might at first appear. Most common, continuous, univariate, unimodal distributions can be organized into a small number of distinct families, which are all special cases of a single Grand Unified Distribution. This compendium details these hundred or so simple distributions, their properties and their interrelations.
Author | : Norman L. Johnson |
Publisher | : John Wiley & Sons |
Total Pages | : 747 |
Release | : 1995-05-08 |
Genre | : Mathematics |
ISBN | : 0471584940 |
Comprehensive reference for statistical distributions Continuous Univariate Distributions, Volume 2 provides in-depth reference for anyone who applies statistical distributions in fields including engineering, business, economics, and the sciences. Covering a range of distributions, both common and uncommon, this book includes guidance toward extreme value, logistics, Laplace, beta, rectangular, noncentral distributions and more. Each distribution is presented individually for ease of reference, with clear explanations of methods of inference, tolerance limits, applications, characterizations, and other important aspects, including reference to other related distributions.