Sobre el modelo Gaussiano inverso mezclado t-student y una aplicación a producción de proteínas

Translated title of the contribution: On the student-t mixture inverse Gaussian model with an application to protein production

Antonio Sanhueza, VICTOR ELISEO LEIVA SANCHEZ, Liliana López-Kleine

Research output: Contribution to journalArticlepeer-review

6 Scopus citations

Abstract

In this article, we introduce a mixture inverse Gaussian (MIG) model based on the Student-t distribution and apply it to bacterium-based protein production for food industry. This model is mainly useful to describe data that follow positively skewed distributions and accommodate atypical observations in a better way than its classical version. Specifically, we present a characterization of the MIG-t distribution. In addition, we carry out a hazard analysis of this distribution centered mainly on its hazard rate. Furthermore, we discuss the maximum likelihood method, which produces-in this case-robust parameter estimates. Moreover, to evaluate the potential influence of atypical observations, we produce a diagnostic analysis for the model. Finally, we apply the obtained results to novel bacterium-based protein production data and statistically compare two types of protein producers using the likelihood ratio test based on the MIG-t model as an alternative methodology to the procedures available until now. This fact is very important, since the evaluation of protein production using both constructions allows practitioners to choose the most productive one before the bacterial culture is scaled to an industrial level.

Translated title of the contributionOn the student-t mixture inverse Gaussian model with an application to protein production
Original languageSpanish
Pages (from-to)177-195
Number of pages19
JournalRevista Colombiana de Estadistica
Volume34
Issue number1
StatePublished - 1 Jun 2011

Fingerprint

Dive into the research topics of 'On the student-t mixture inverse Gaussian model with an application to protein production'. Together they form a unique fingerprint.

Cite this