The inverse Gaussian distribution: statistical theory and applications
This book will appeal to probabilists and mathematical statisticians interested in the inverse Gaussian distribution. It will also be of value to those wishing to use the distibution in a particular subject matter. It provides a broad, up-to-date coverage of topics, an in-depth description of many examples, and a very large bibliography.
What people are saying - Write a review
We haven't found any reviews in the usual places.
18 other sections not shown
Other editions - View all
The Inverse Gaussian Distribution: Statistical Theory and Applications
Limited preview - 2012
analysis of reciprocals approximate assumed asymptotic Bayes estimator Bhattacharyya and Fries Birnbaum-Saunders Brownian motion Chaubey Chhikara and Folks coefficient computed confidence intervals consider constant corresponding covariance curve Cusum data set defined degrees of freedom denote density detection distribution function drift equation error Example exponential failure frequencies given hazard rate Hence hypothesis IG law IG model IG(fi IG(n independent inverse Gaussian distribution inverse Gaussian law known Laplace transform likelihood function likelihood ratio test linear log-likelihood lognormal matrix maximum likelihood estimates mean method normal law observed obtain order statistic P-IG p-value Padgett parameters population posterior prediction interval probability random sample random variable reciprocal gamma reject sequential simulated slug length soft diet soil column solution solving stage statistic stochastic stress level Table theory threshold tion unknown variance vector Whitmore Wiener process zero