AUTOMATED EVALUATION OF SUPERVISED LEARNING ALGORITHM FOR ENDOMETRIOSIS PREDICTION

| Keywords and phrases: ANC visits, zero-inflated, hurdle, NFHS IV. Received: February 24, 2023; Revised: March 17, 2023; Accepted: April 8, 2023; Published: May 15, 2023 How to cite this article: V. Suriya and R. Geetha, Prediction of the determinants of the number of antenatal care visits in NFHS IV survey of India: modeling excess zero of count data, JP Journal of Biostatistics 23(2) (2023), 173-200. http://dx.doi.org/10.17654/0973514323010 This Open Access Article is Licensed under Creative Commons Attribution 4.0 International License References: [1] M. A. Beydoun et al., Antioxidant status and its association with elevated depressive symptoms among US adults: National Health and Nutrition Examination Surveys 2005-06, British Journal of Nutrition 109 (2013), 1714-1729. Advance online publication. doi: 10.1017/S0007114512003467. [2] Cindy Xin Feng, A comparison of zero-inflated and hurdle models for modeling zero-inflated count data, Journal of Statistical Distributions and Applications 8(8) (2021), 1-19. [3] C. E. Rose, On the use of zero-inflated and hurdle models for modeling vaccine adverse event count data, Journal of Biopharmaceutical Statistics 16 (2006), 463-481. DOI: 10.1080/10543400600719384. [4] Diane Lambert, Zero-inflated Poisson regression, with an application to defects in manufacturing Technometrics 34(1) (1992), 1-14. [5] Daniel Biftu Bekalo, Zero-inflated models for count data: an application to number of antenatal care service visits, Annals of Data Science 8 (2021), 683-708. [6] Bruce A. Desmarais and J. J. Harden, Testing for zero-inflation in count models: bias correction for the Vuong test, The Stata Journal 13(4) (2013), 810-835. [7] M. Genius and E. Strazzera, A note about model selection and tests for non-nested contingent valuation models, Econom. Lett. 74(3) (2002), 363-370. [8] G. Nanjundan and Sadiq Pasha, A note on the characterization of zero-inflated Poisson mode, Open Journal of Statistics 5 (2015), 140-142. http://www.scirp.org/journal/ojs; http://dx.doi.org/10.4236/ojs.2015.52017. [9] John Mullahy, Specification and testing of some modified count data models, J. Econometrics 33(3) (1986), 341-365. [10] John Haslett, A. C. Parnell, J. Hinde and R. de A. Moral, Modelling excess zeros in count data: a new perspective on modelling approaches, International Statistical Review 90 (2022), 216-236. https://doi.org/10.1111/insr.12479. [11] Kakoli Rani Bhowmik, Sumonkanti Das and Md. Atiqul Islam, Modelling the number of antenatal care visits in Bangladesh to determine the risk factors for reduced antenatal care attendance, PLOS ONE 15(1) (2020), 1-19. https://doi.org/10.1371/journal.pone.0228215. [12] Lili Puspita Rahayua, K. Sadik and I. Indahwati, Overdispersion study of Poisson and zero-inflated Poisson regression for some characteristics of the data on lamda, International Journal of Advances in Intelligent Informatics 2(3) (2016), 140-148. DOI: http://dx.doi.org/10.26555/ijain.v2i3.73 W: http://ijain.org; E: [email protected]. [13] J. R. Mahalik et al., Changes in health risk behaviors for males and females from early adolescence through early adulthood, Health Psychology 32 (2013), 685-694. doi: 10.1037/a0031658. [14] Y. Min and A. Agresti, Random effect models for repeated measures of zero inflated count data, Stat. Model. 5 (2005), 1-19. [15] Mei-Chen Hu, M. Pavlicova and E. V. Nunes, Zero-inflated and hurdle models of count data with extra zeros: examples from an HIV-risk reduction intervention trial, Am. J. Drug Alcohol Abuse 37(5) (2011), 367-375. doi: 10.3109/00952990.2011.597280. [16] N. Ismail, Handling overdispersion with negative binomial and generalized Poisson regression models, Casualty Actuarial Society Forum (2007), 103-158. [17] Oyindamola B. Yusuf et al., On the performance of the Poisson, negative binomial and generalized Poisson regression models in the prediction of antenatal care visits in Nigeria, American Journal of Mathematics and Statistics 5(3) (2015), 128-136. DOI: 10.5923/j.ajms.20150503.04. [18] R Core Team, R: A Language and Environment for Statistical Computing, R Foundation for Statistical Computing, Vienna, Austria, 2013. [19] Remi Mrume Sakia, Application of the power series probability distributions for the analysis of zero-inflated insect count data, Open Access Library Journal 5 (2018), e4735. DOI: 10.4236/oalib.1104735. [20] S. Yang, L. I. Harlow, G. Puggioni and C. A. Redding, A comparison of different methods of zero-inflated data analysis and an application in health surveys, Journal of Modern Applied Statistical Methods 16(1) (2017), 518-543. doi: 10.22237/jmasm/1493598600. [21] Sujan Rudra and Soma Chowdhury Biswas, Models for analyzing over-dispersed hurdle negative binomial regression model: application to manufactured cigarette use, Journal of Reliability and Statistical Studies 12(2) (2019), 51-60. [22] D. I. Warton, Many zeros does not mean zero-inflation: comparing the goodness-of-fit of parametric models to multivariate abundance data, Environmetrics 16 (2005), 275-289. [23] Gary King, Event count models for international relations: generalizations and applications, International Studies Quarterly 33(2) (1989), 123-147. [24] John M. Williamson, Hung-Mo Lin, Robert H. Lyles and Allen W. Hightower, Power calculations for ZIP and ZINB models, Journal of Data Science 5 (2007), 519-534. [25] Ting Hsiang Lin and Min-Hsiao Tsai, Modeling health survey data with excessive zero and K responses, Stat. Med. 32 (2013), 1572-1583. https://doi.org/10.1002/sim.5650. |