Journal ArticleDOI
Tables of Inverse Gaussian Percentage Points
M. T. Wasan,L. K. Roy +1 more
TLDR
In this article, the authors present a table of the values of the percentage points of this Inverse Gaussian distribution with parameter t for values of t ranging from 0.1 to 4000.Abstract:
where the parameters ,u > 0 and X > 0 are given by u, = E(X) and X = A.3[Var (X)]-'. Some applications of this distribution and techniques of parametric estimation have been presented by Roy and Wasan [3], and recently Wasan [6] described some of the properties of an Inverse Gaussian process with ,u = t and X = t2. In this paper we present a table of the values of the percentage points of this Inverse Gaussian distribution with parameter t for values of t ranging from 0.1 to 4000. The cases of t > 4000 and t < 0.1 are also considered. We will firstly state some interesting properties which we shall find useful in our discussion of the tables.read more
Citations
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Journal ArticleDOI
The Inverse Gaussian Distribution and its Statistical Application—A Review
John Leroy Folks,Raj S. Chhikara +1 more
Journal ArticleDOI
Weak convergence of first passage time processes
TL;DR: Theorem 5.1 of Billingsley as mentioned in this paper shows that weak convergence X n ⇒ X in D implies weak convergence S (X n ) ⇒ S ( X ) in D by virtue of the continuous mapping theorem.
Journal ArticleDOI
A Normalizing Logarithmic Transformation for Inverse Gaussian Random Variables
G. A. Whitmore,M. Yalovsky +1 more
TL;DR: In this article, a logarithmic transformation for inverse Gaussian variates is presented, which produces approximate normality for large values of the concentration parameter, for a large number of parameters.
Journal ArticleDOI
Statistical distributions related to the inverse gaussian
Raj S. Chhikara,J. L. Floks +1 more
TL;DR: In this article, the authors derived the statistical distribution of the inverse Gaussian variate and its related distribution for the normal variate, and showed that these distributions have a certain anaogy vita.
Journal ArticleDOI
Application of inverse Gaussian distribution to occupational exposure data
TL;DR: In this article, the authors proposed the application of inverse Gaussian distribution to occupational exposure data and compared it with the log-normal distribution for timeweighted average (TWA) values when the averaging time varies.
References
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Journal ArticleDOI
Statistical Properties of Inverse Gaussian Distributions. II
TL;DR: In this paper, it was shown that the conditional expectation of any unbiased estimator of the $r$th cumulant of the Inverse Gaussian probability density function for a fixed number of observations on a variate $x$ has been derived.
Journal ArticleDOI
On an inverse Gaussian process
TL;DR: In this article, the density function of the functions of Inverse Gaussian variates is found for the discrete case and the covariance function and stochastic integral as well as conditional density functions of an Inverse GAs are obtained.