Modelling Inverse Gaussian Data with Censored Response Values: EM versus MCMC | Zendy

Ross Sparks | Zendy; Gordon J. Sutton | Zendy; Peter Toscas | Zendy; John T. Ormerod | Zendy

AI Assistant Blog Pricing

Home ZAIA Blog

Open Access

Modelling Inverse Gaussian Data with Censored Response Values: EM versus MCMC

Author(s) -

Ross Sparks,

Gordon J. Sutton,

Peter Toscas,

John T. Ormerod

Publication year - 2011

Publication title -

advances in decision sciences

Language(s) - English

Resource type - Journals

SCImago Journal Rank - 0.178

H-Index - 13

eISSN - 2090-3367

pISSN - 2090-3359

DOI - 10.1155/2011/571768

Subject(s) - censoring (clinical trials) , markov chain monte carlo , gaussian , computer science , statistics , inverse gaussian distribution , econometrics , mathematics , bayesian probability , distribution (mathematics) , mathematical analysis , physics , quantum mechanics

Low detection limits are common in measure environmental variables. Building models using data containing low or high detection limits without adjusting for the censoring produces biased models. This paper offers approaches to estimate an inverse Gaussian distribution when some of the data used are censored because of low or high detection limits. Adjustments for the censoring can be made if there is between 2% and 20% censoring using either the EM algorithm or MCMC. This paper compares these approaches

The content you want is available to Zendy users.

Already have an account? Click here to sign in.

Having issues? You can contact us here

Accelerating Research