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Regression Analysis for Zero Inflated Population Under Complex Sampling Designs

Paneru, Khyam Narayan
http://rave.ohiolink.edu/etdc/view?acc_num=bgsu1375197852

Abstract Details

2013, Doctor of Philosophy (Ph.D.), Bowling Green State University, Statistics.
An underlying population may contain a large proportion of zero values which cause the population distribution spiked at zero and such population is referred to as zero-inflated population. This type of population can be seen in many applications such as insurance, meteorology, auditing, ecology, and manufacturing. Zero-inflated population is often analyzed via a two-component mixture model: probabilistic mixture of zero and a regular component with specific probability distribution. The confidence interval problems for the zero-inflated population mean under regular models have existed in the literature. Regression models have also been developed for zero-inflated populations. However, many of these models aim at count data, though the regression models with continuous-type responses are more often to be seen in application. Furthermore, these regression models for zero-inflated populations do not address the situations when the data available for analysis are obtained through complex probability sampling designs. This dissertation investigates the estimation problem (both point and confidence interval) in generalized linear regression models (continuous-type or discrete-type) associated with complex probability sampling designs. This dissertation develops the zero-inflated mixture (ZIM) regression model under complex sampling design via two-component mixture model where the probability distribution of non-zero component is supposed to be parametric. It proposes a maximum pseudo-likelihood procedure to estimate the expected responses at "future" covariate values/vectors. The limiting distribution of the pseudo-likelihood ratio statistic is derived so that a large-sample theory for constructing the confidence intervals for the expected responses is established. Simulation studies are conducted to assess the performance of the proposed procedure. The simulation results show that under some complex probability sampling designs, the new confidence intervals based on the pseudo-likelihood function perform significantly better than the standard procedure. The proposed pseudo-likelihood new procedure is applied to a real-life data set that was analyzed by many other authors. Again, the confidence interval with the new procedure appears to be more useful than other classic procedures.
Hanfeng Chen, Dr. (Advisor)
Christopher Rump, Dr. (Committee Member)
Maria Rizzo, Dr. (Committee Member)
Junfeng Shang, Dr. (Committee Member)
113 p.
Statistics
zero-inflated; pseudo-likelihood; regression; generalized linear models; link function; complex sampling desings

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Citations

  • Paneru, K. N. (2013). Regression Analysis for Zero Inflated Population Under Complex Sampling Designs [Doctoral dissertation, Bowling Green State University]. OhioLINK Electronic Theses and Dissertations Center. http://rave.ohiolink.edu/etdc/view?acc_num=bgsu1375197852

    APA Style (7th edition)

  • Paneru, Khyam. Regression Analysis for Zero Inflated Population Under Complex Sampling Designs. 2013. Bowling Green State University, Doctoral dissertation. OhioLINK Electronic Theses and Dissertations Center, http://rave.ohiolink.edu/etdc/view?acc_num=bgsu1375197852.

    MLA Style (8th edition)

  • Paneru, Khyam. "Regression Analysis for Zero Inflated Population Under Complex Sampling Designs." Doctoral dissertation, Bowling Green State University, 2013. http://rave.ohiolink.edu/etdc/view?acc_num=bgsu1375197852

    Chicago Manual of Style (17th edition)

bgsu1375197852
2,412
© 2013, all rights reserved.
This open access ETD is published by Bowling Green State University and OhioLINK.