ANALYSIS OF THE REGRESSION MODEL FOR ZERO-INFLATION DATA
DOI:
https://doi.org/10.53555/4zfphn62Abstract
The community may contain a large percentage of zero values that cause the community distribution to move away from zero, and this group is referred to as not following a normal distribution, so one of the conditions of the linear regression models is permeated. This type of society can be seen in many applications such as insurance, meteorology, auditing, environment, and manufacturing. The zero-community number is often analyzed via a two-part admixture model: The first part is probabilistic from zero and the second part is regular with a specific probability distribution. Problems of confidence estimation of the zero-classifier population mean under normal models have been present in research. Regression models have also been developed for the zero population groups. However, many of these models are aimed at counting data, although regression models with responses of a continuous type can be seen in application quite often. Moreover, these regression models for homeless populations do not address situations in which the data available for analysis were obtained through complex probability sampling designs.
Different statistical methods and models have been developed for the statistical analysis of such population. Based on the current research, most of the special studies focus on estimating the population mean and developing regression models. This dissertation will also focus on developing regression models.
This dissertation will also focus on developing regression models. Most of the regression models developed for the null population found in research have given more attention to population data in which observations can take only non-negative integer values that arise from counting rather than ordering. They also use maximum possibility methods and pseudo greatest possibility methods to estimate expected responses in Value . Variable / future variables.
References
Abadir, K. M. and Magnus, J. R. (2005). Matrix algebra, Cambridge University Press.
Chai, H. S. and Bailey, K. R. (2008). Use of log-skew-normal distribution in analysis of continuous data with a discrete component at zero. Statistics in Medicine, 27, 3643-3655.
Chen, H., Chen, J., and Chen, S. (2010). Con_dence intervals for the mean of a population containing many zero values under unequal-probability sampling. Canadian Journal of Statistics, 38, 582-597.
Cui, Y. and Yang, W. (2009). Zero-inated generalized poisson regression mixture model for mapping quantitative trait loci underlying count trait with many zeros. Journal of Theoretical Biology, 256, 276-285.
Dobbie, M. J. and Welsh, A. H. (2001). Modelling correlated zero-inflated count data. Australian and New Zealand Journal of Statistics, 43, 431-444.
Hall, D. B. (2000). Zero-inflated poisson and binomial regression with random e_ects:A case study. Biometrics, 56, 1030-1039.
Hall, D. B. (2000). Zero-inflated poisson and binomial regression with random effects: A case study. Biometrics, 56, 1030-1039.
Lee, A. H., Wang, K., and Yau, K. K. W. (2001). Analysis of zero-inflated poisson data incorporating extent of exposure. Biometrical Journal, 43, 963-975.
Murray, M. D., Harris, L. E., Overhage, J. M., Zhou, X., Eckert, G. J., Smith, F. E.,Buchanan,N. N., Wolinsky, F. D., McDonald, C. J., and Tierney, W. M. (2004). Failure of computerized treatment suggestions to improve health outcomes of outpatients with uncomplicated hypertension: Results of a randomized controlled trial. Pharmacotherapy: The Journal of Human Pharmacology and Drug Therapy, 24, 324-337.
Ridout, M., Hinde, J., and Dem_etrio, C. G. B. (2001). A score test for testing a zero-inflated poisson regression model against zero-inflated negative binomial alternatives. Biometrics, 57, 219-223.
Rizzo, M. L. (2007). Statistical Computing with R, Chapman & Hall/CRC.
Welsh, A. H. and Zhou, X. H. (2006). Estimating the retransformed mean in a heteroscedastic two-part model. Journal of Statistical Planning and Inference, 136, 860-881.
Welsh, A. H., Cunningham, R. B., Donnelly, C. F., and Lindenmayer, D. B. (1996). Modelling the abundance of rare species: Statistical models for counts with extra zeros. Ecological Modelling, 88, 297-308.
Yau, K. K. W. and Lee, A. H. (2001). Zero-inflated poisson regression with random effects to evaluate an occupational injury prevention programme. Statistics in Medicine, 20, 2907-2920.
Yau, K. K. W., Wang, K., and Lee, A. H. (2003). Zero-inflated negative binomial mixed regression modeling of over-dispersed count data with extra zeros. Biometrical Journal, 45, 437-452.
Zhou, X. and Cheng, H. (2008). A computer program for estimating the retransformed mean in heteroscedastic two-part models. Computer Methods and Programs in Biomedicine, 90, 210-216.
Downloads
Published
Issue
Section
License
Copyright (c) 2023 Journal of Advance Research in Mathematics And Statistics (ISSN 2208-2409)

This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.
Terms & Condition
Submission -
Author can submit the manuscript through our online submission process or email us at the designated email id in contact details.
The other mode of submission not accepted than online and email.
Before submission please read the submission guidelines.
NN Publication accepts only article submitted in pdf/doc/docx/rtf file format. Another format except given file formats will no be considered .
Author will be responsible for the error mistakes in the submission files. The minor changes can be done without any cost after publication. But for major changes NN Publication may charges you the editing charges.
Publication (Online) -
The online publication is scheduled on last date of every month, but it can be delayed by 24 to 48 hours due to editorial process if huge number of articles comes to publish in single issue.
Automatic notificatation email will be sent to the all users on publication of an issue, so its author’s duty to check their email inbox or SPAM folder to get this notification.
After publication of article author can not withdraw their article.
If editor’s found any issue after publication of article then the NN Publication have the authority to remove the article from online website.
No refund will be provided after online publication of article.
Publication (Print) -
The print copy publication are sent as per the author’s request after 2 weeks of online publication of that issue.
NN Publication will ship the article by India Post and provide the consignment number on dispatch of print copy.
NN Publication follows all the guidelines of delivery provided by IndiaPost and hence not responsible for delay in delivery due to any kind of reasons.
Refund of hard copy will not be provided after dispatch or print of the journal.
NN Publication will be responsible for raise a complain if there is any issue occurs in delivery, but still will not be responsible for providing the refund.
NN Publication will be responsible to resend the print copy only and only if the print copy is lost or print copy is damaged in delivery / or there is delay more than 6 months.
According to India Post the delivery should be completed with in 1-3 weeks after dispatch of articles.
Privacy Policy-
NN Publicationl uses the email ids of authors and editors and readers for sending editorial or publication notification only, we do not reveal or sell the email ids to any other website or company.