# ANALYSIS OF THE REGRESSION MODEL FOR ZERO-INFLATION DATA

## DOI:

https://doi.org/10.53555/4zfphn62## Abstract

*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.

## You are free to:

**Share**— copy and redistribute the material in any medium or format for any purpose, even commercially.**Adapt**— remix, transform, and build upon the material for any purpose, even commercially.- The licensor cannot revoke these freedoms as long as you follow the license terms.

## Under the following terms:

**Attribution**— You must give appropriate credit , provide a link to the license, and indicate if changes were made . You may do so in any reasonable manner, but not in any way that suggests the licensor endorses you or your use.**No additional restrictions**— You may not apply legal terms or technological measures that legally restrict others from doing anything the license permits.

## Notices:

You do not have to comply with the license for elements of the material in the public domain or where your use is permitted by an applicable exception or limitation .

No warranties are given. The license may not give you all of the permissions necessary for your intended use. For example, other rights such as publicity, privacy, or moral rights may limit how you use the material.

## How to Cite

*Journal of Advance Research in Mathematics and Statistics (ISSN 2208-2409)*,

*10*(5). https://doi.org/10.53555/4zfphn62