Asymptotic results in censored zero-inflated Poisson regression

Nguyen, Van Trinh; Dupuy, Jean‐François

doi:10.1080/03610926.2019.1676442

Cited by 7 publications

(3 citation statements)

References 32 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Random effects ZIP models were studied by Min and Agresti [4] and Yau and Lee [5]. Furthermore, Saffari and Adnan [6], Yang and Simpson [7], and Nguyen and Dupuy [8] have applied ZIP models for censored data. Recently, a review of various ZIP models was presented in [9,10].…”

Section: Introductionmentioning

confidence: 99%

Application of Mixture Models for Doubly Inflated Count Data

Arora

Chaganty

2023

Analytics

View full text Add to dashboard Cite

In health and social science and other fields where count data analysis is important, zero-inflated models have been employed when the frequency of zero count is high (inflated). Due to multiple reasons, there are scenarios in which an additional count value of k > 0 occurs with high frequency. The zero- and k-inflated Poisson distribution model (ZkIP) is more appropriate for such situations. The ZkIP model is a mixture distribution with three components: degenerate distributions at 0 and k count and a Poisson distribution. In this article, we propose an alternative and computationally fast expectation–maximization (EM) algorithm to obtain the parameter estimates for grouped zero and k-inflated count data. The asymptotic standard errors are derived using the complete data approach. We compare the zero- and k-inflated Poisson model with its zero-inflated and non-inflated counterparts. The best model is selected based on commonly used criteria. The theoretical results are supplemented with the analysis of two real-life datasets from health sciences.

show abstract

Section: Introductionmentioning

confidence: 99%

Application of Mixture Models for Doubly Inflated Count Data

Arora

Chaganty

2023

Analytics

View full text Add to dashboard Cite

show abstract

“…The ZIP models using the Bayesian approach for spatial data were studied by Agarwal et al [10]. Furthermore, ZIP models for censored data were studied by Saffari and Adnan [11], Yang and Simpson [12], and Nguyen and Dupuy [13]. Altun [14] introduced a zero-inflated Poisson-Lindley regression model, while, recently, Bakouch et al [15] introduced COS-Poisson distribution and the corresponding regression model for zero-inflated count data.…”

Section: Introductionmentioning

confidence: 99%

EM Estimation for Zero- and k-Inflated Poisson Regression Model

Arora

Chaganty

2021

Computation

View full text Add to dashboard Cite

Count data with excessive zeros are ubiquitous in healthcare, medical, and scientific studies. There are numerous articles that show how to fit Poisson and other models which account for the excessive zeros. However, in many situations, besides zero, the frequency of another count k tends to be higher in the data. The zero- and k-inflated Poisson distribution model (ZkIP) is appropriate in such situations The ZkIP distribution essentially is a mixture distribution of Poisson and degenerate distributions at points zero and k. In this article, we study the fundamental properties of this mixture distribution. Using stochastic representation, we provide details for obtaining parameter estimates of the ZkIP regression model using the Expectation–Maximization (EM) algorithm for a given data. We derive the standard errors of the EM estimates by computing the complete, missing, and observed data information matrices. We present the analysis of two real-life data using the methods outlined in the paper.

show abstract

“…Ignoring censoring is known to yield biased estimates and thus, incorrect inferences. Statistical inference in censored Poisson regression and extensions was therefore addressed by several authors; see, for example, [39], [8], [11], [50], [26] for censored generalized Poisson regression, [22] for finite mixtures of censored Poisson regressions and [33], [29] for zero-inflated censored Poisson regression.…”

Section: Introductionmentioning

confidence: 99%

Censored count data regression with missing censoring information

Bousselmi

Dupuy

Karoui

2021

Electron. J. Statist.

Self Cite

View full text Add to dashboard Cite

We investigate estimation in Poisson regression model when the count response is right-censored and the censoring indicators are missing at random. We propose several estimators based on the regression calibration, multiple imputation and augmented inverse probability weighting methods. Under appropriate regularity conditions, we prove the consistency of our estimators and we derive their asymptotic distributions. Simulation experiments are carried out to investigate the finite sample behaviour and relative performance of the proposed estimates. These estimates are illustrated on a real data set.

show abstract

Asymptotic results in censored zero-inflated Poisson regression

Cited by 7 publications

References 32 publications

Application of Mixture Models for Doubly Inflated Count Data

Application of Mixture Models for Doubly Inflated Count Data

EM Estimation for Zero- and k-Inflated Poisson Regression Model

Censored count data regression with missing censoring information

Contact Info

Product

Resources

About