Open AccessGeneralized Quasilikelihood Inference for Zero Inflated Longitudinal Count Data
Author(s) -
Jannatul Ferdous Antu,
Sabina Sharmin,
Taslim Sazzad Mallick
Publication year - 2020
Publication title -
the dhaka university journal of science
Language(s) - English
Resource type - Journals
eISSN - 2408-8528
pISSN - 1022-2502
DOI - 10.3329/dujs.v68i1.54602
Subject(s) - zero (linguistics) , inference , count data , statistical inference , mathematics , series (stratigraphy) , statistics , longitudinal data , standard error , regression , regression analysis , generalized estimating equation , algorithm , computer science , data mining , artificial intelligence , paleontology , philosophy , linguistics , poisson distribution , biology
In this paper, we extend an observation-driven model for time series of zero inflated count data to longitudinal data setup. Basic properties of the models are discussed. For statistical inference of the proposed model, a generalized quasilikelihood (GQL) estimating equation has been derived for the regression parameter. A pharmaceutical data has been reanalyzed using the proposed approach and results are compared. The proposed approach produces similar estimates as given in the earlier work with much smaller standard errors.
Dhaka Univ. J. Sci. 68(1): 95-99, 2020 (January)