Analysis of NMR Spin-Relaxation Data Using an Inverse Gaussian Distribution Function

FAST-Modelfree: a program for rapid automated analysis of solution NMR spin-relaxation data.

Herein we describe the program FAST-Modelfree for the fully automated, high throughput analysis of NMR spin-relaxation data. This program interfaces with the program Modelfree 4.1 and provides an intuitive graphical user interface for configuration as well as complete standalone operation during the model selection and rotational diffusion parameter optimization processes. FAST-Modelfree is als...

Regression Analysis under Inverse Gaussian Model: Repeated Observation Case

 Traditional regression analyses assume normality of observations and independence of mean and variance. However, there are many examples in science and Technology where the observations come from a skewed distribution and moreover there is a functional dependence between variance and mean. In this article, we propose a method for regression analysis under Inverse Gaussian model when th...

An Extension of the Kratzel Function and Associated Inverse Gaussian Probability Distribution Occurring in Reliability Theory

In view of their importance and usefulness in reliability theory and probability distributions, several generalizations of the inverse Gaussian distribution and the Krätzel function are investigated in recent years. This has motivated the authors to introduce and study a new generalization of the inverse Gaussian distribution and the Krätzel function associated with a product of a Bessel functi...

NMR Spin-Spin Coupling Constants in Gaussian 03 for dummies

Bayesian Estimation of Shift Point in Shape Parameter of Inverse Gaussian Distribution Under Different Loss Functions

In this paper, a Bayesian approach is proposed for shift point detection in an inverse Gaussian distribution. In this study, the mean parameter of inverse Gaussian distribution is assumed to be constant and shift points in shape parameter is considered. First the posterior distribution of shape parameter is obtained. Then the Bayes estimators are derived under a class of priors and using variou...