Censored Hurdle Negative Binomial Regression (Case Study: Neonatorum Tetanus Case in Indonesia)

Riza Yuli Rusdiana*, Ismaini Zain, Santi Wulan Purnami

*Corresponding author for this work

Research output: Contribution to journalConference articlepeer-review

2 Citations (Scopus)

Abstract

Hurdle negative binomial model regression is a method that can be used for discreate dependent variable, excess zero and under- and overdispersion. It uses two parts approach. The first part estimates zero elements from dependent variable is zero hurdle model and the second part estimates not zero elements (non-negative integer) from dependent variable is called truncated negative binomial models. The discrete dependent variable in such cases is censored for some values. The type of censor that will be studied in this research is right censored. This study aims to obtain the parameter estimator hurdle negative binomial regression for right censored dependent variable. In the assessment of parameter estimation methods used Maximum Likelihood Estimator (MLE). Hurdle negative binomial model regression for right censored dependent variable is applied on the number of neonatorum tetanus cases in Indonesia. The type data is count data which contains zero values in some observations and other variety value. This study also aims to obtain the parameter estimator and test statistic censored hurdle negative binomial model. Based on the regression results, the factors that influence neonatorum tetanus case in Indonesia is the percentage of baby health care coverage and neonatal visits.

Original languageEnglish
Article number012039
JournalJournal of Physics: Conference Series
Volume855
Issue number1
DOIs
Publication statusPublished - 12 Jun 2017
Event1st International Conference on Mathematics: Education, Theory, and Application, ICMETA 2016 - Surakarta, Indonesia
Duration: 6 Dec 20167 Dec 2016

Fingerprint

Dive into the research topics of 'Censored Hurdle Negative Binomial Regression (Case Study: Neonatorum Tetanus Case in Indonesia)'. Together they form a unique fingerprint.

Cite this