Skip to main content
SpringerLink
Log in

Finite Mixture Modelling Approach to Identify Factors Affecting Children Ever Born for 15–49 Year Old Women in Asian Country

  • Conference paper
  • First Online:
Statistics for Data Science and Policy Analysis

Abstract

The number of ever born children is one of the main components of population dynamics that determine the size, structure, as well as the composition of a countries’ population. Children ever born refer to the number of children born alive to the person up to a specified reference date and served as a response variable here. A secondary dataset is used in this paper that is obtained from a countrywide representative survey entitled Bangladesh Demographic and Health Survey (BDHS) 2014. This study aims to identify the socioeconomic and demographic factors influencing children ever born to the women of 15–49 years old in Bangladesh. The first attempt of this paper is to identify the best-fitted model among generalized Poisson, Negative Binomial, truncated, COM and finite mixture regression model form. The results suggest that among the model considered in this study Finite Mixture Negative Binomial Regression with three components gives the best-fitted model to estimate the number of ever born children in Bangladesh. It reveals that respondents age, residential status, family size and intention of using contraception have shown positive impact and respondents education, drinking water, toilet facility, religious status, household head age, wealth index, age at first birth, and husband education shows a negative impact on ever born children.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 219.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 279.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
USD 279.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  1. United Nations Development Programme.: Human Development Report 2015: work for human development. UN. http://hdr.undp.org/en/content/human-development-report-2015-work-human-development (2016). Accessed on 15 Mar 2019

  2. Sayem, A.M., Nury, A.T.M.S., Hossain, M.D.: Achieving the millennium development goal for under-five mortality in Bangladesh: current status and lessons for issues and challenges for further improvements. J. Health Popul. Nutr. 29(2), 92–102 (2011)

    Article  Google Scholar 

  3. WB Homepage.: Fertility rate, total (births per woman) | Data. https://data.worldbank.org/indicator/SP.DYN.TFRT.IN?locations=BD. Accessed on 4 May 2019

  4. Our World in Data Homepage.: Fertility rate. https://ourworldindata.org/fertility-rate. Accessed on 16 May 2019

  5. Kiser, H., Hossain, M.A.: Estimation of number of ever born children using zero truncated count model: evidence from Bangladesh demographic and health survey. Health Inform. Sci. Syst. 7(1), 3 (2019)

    Article  Google Scholar 

  6. Satyavada, A., Adamchak, D.J.: Determinants of current use of contraception and children ever born in Nepal. Soc. Biol. 47(1–2), 51–60 (2000)

    Google Scholar 

  7. Matsumoto, Y., Yamabe, S.: Family size preference and factors affecting the fertility rate in Hyogo, Japan. Reprod. Health. 10, 6 (2013)

    Article  Google Scholar 

  8. Haque, A., Hossain, T., Nasser, M.: Predicting the number of children ever born using logistic Regression model. Biometr. Biostat. Int. J. 2(4), 00034 (2015)

    Google Scholar 

  9. Uddin, I., Bhuyan, K.C., Islam, S.S.: Determinants of desired family size and children ever born in Bangladesh. J. Fam. Welf. 57(2), 39–47 (2011)

    Google Scholar 

  10. Matin, K.A..: The demographic dividend in Bangladesh: an illustrative study. In: 18th Biennial Conference of the Bangladesh Economic Association Proceedings, pp. 1–20. Dhaka, Bangladesh (2012)

    Google Scholar 

  11. Population Pyramids Homepage.: Population Pyramids of the World from 1950 to 2100. https://populationpyramid.net/bangladesh/2050/. Accessed 4 May 2019

  12. Dwivedi, V.K., Sediadie, T., Ama, N.O.: Factors affecting children ever born (CEB) in Botswana: application of Poisson Regression model. Res. J. Math. Stat. Sci. 4(10), 1–9 (2016)

    Google Scholar 

  13. Mekonnen, W., Worku, A.: Determinants of fertility in rural Ethiopia: the case of Butajira Demographic Surveillance System (DSS). BMC Publ. Health. 11, 782 (2011)

    Article  Google Scholar 

  14. Kareem, Y.O., Yusuf, O.B.: Statistical modeling of fertility experience among women of reproductive age in Nigeria. Int. J. Stat. Appl. 8(1), 23–33 (2018)

    Google Scholar 

  15. Ahmed, H.M.M., ALI, H.M.H.: Using count Regression models to determinate factors influencing fertility of Sudanese women. J. Glob. Econ. Manag. Bus. Res. 6(4), 277–285 (2015)

    Google Scholar 

  16. Mullahy, J.: Specification and testing of some modified count data models. J. Econ. 33(3), 341–365 (1986)

    Article  MathSciNet  Google Scholar 

  17. Lambert, D.: Zero-inflated Poisson Regression, with an application to defects in manufacturing. Technometrics. 34(1), 1–14 (1992)

    Article  Google Scholar 

  18. Poston, D.L., McKibben, S.L.: Using zero-inflated count Regression models to estimate the fertility of U. S. Women. J. Mod. Appl. Stat. Methods. 2(2), 371–379 (2003)

    Article  Google Scholar 

  19. Islam, M.R., Islam, M.R., Alam, M.R., Hossain, M.M.: Affecting socio-demographic factors on children ever born for women who have experienced domestic violence and women who have not experienced domestic violence in Bangladesh. Am. J. Sociolog. Res. 2(5), 113–119 (2012)

    Article  Google Scholar 

  20. Ahmmed, F., Naser, M.: Modeling and predicting of children ever born in Bangladesh. In: International Conference on Statistical Data Mining for Bioinformatics Health Agriculture and Environment Proceedings, pp. 1–10. Rajshahi, Bangladesh (2012)

    Google Scholar 

  21. Sellers, K.F., Borle, S., Shmueli, G.: The COM-Poisson model for count data: a survey of methods and applications. Appl. Stoch. Model. Bus. Ind. 28(2), 104–116 (2012)

    Article  MathSciNet  Google Scholar 

  22. Bonat, W.H., Zeviani, W.M., Ribeiro Jr, E.E.: Regression Models for Count Data: Beyond the Poisson model. Statistics and Geoinformation Laboratory Federal University of Parana, Brazil, http://cursos.leg.ufpr.br/rmcd/rmcdbook.pdf (2017), Accessed on 12 Apr 2019

  23. Park, B.-J., Lord, D.: Application of finite mixture models for vehicle crash data analysis. Accid. Anal. Prev. 41(4), 683–691 (2009)

    Article  Google Scholar 

  24. McLachlan, G.J., Lee, S.X., Rathnayake, S.I.: Finite mixture models. Ann. Rev. Stat. Appl. 6(1), 355–378 (2019)

    Article  MathSciNet  Google Scholar 

  25. Stata Finite Mixture Models Reference Manual Release 16. Stata Press, Texas, 2019

    Google Scholar 

  26. Dalrymple, M.L., Hudson, I.L., Ford, R.P.K.: Finite mixture, zero-inflated Poisson and hurdle models with application to SIDS. Comput. Stat. Data Anal. 41(3–4), 491–504 (2003)

    Article  MathSciNet  Google Scholar 

  27. Land, K.C., McCall, P.L., Nagin, D.S.: A comparison of Poisson, negative binomial, and semiparametric mixed Poisson Regression models: with empirical applications to criminal careers data. Sociol. Method. Res. 24(4), 387–442 (1996)

    Article  Google Scholar 

  28. Bermúdez, L., Karlis, D.: A finite mixture of bivariate Poisson regression models with an application to insurance ratemaking. Comput. Stat. Data Anal. 56(12), 3988–3999 (2012)

    Article  MathSciNet  Google Scholar 

  29. Park, B.-J., Lord, D., Wu, L.: Finite mixture modeling approach for developing crash modification factors in highway safety analysis. Accid. Anal. Prev. 97(74–287), 274–287 (2016)

    Article  Google Scholar 

  30. Azagba, S., Wolfson, M.: E-cigarette use and quantity of cigarette smoking among adolescent cigarette smokers: a finite mixture model analysis. Drug Alcohol Depend. 185, 33–39 (2018)

    Article  Google Scholar 

  31. National Institute of Population Research and Training (NIPORT).: Bangladesh Demographic and Health Survey 2014. NIPORT, Mitra and Associates, and ICF International, Bangladesh, Dhaka. https://dhsprogram.com/pubs/pdf/FR311/FR311.pdf accessed on 2018/06/25 (2016)

  32. Sahle, G.: Ethiopic maternal care data mining: discovering the factors that affect postnatal care visit in Ethiopia. Health Inform. Sci. Syst. 4(1), 4 (2016)

    Article  Google Scholar 

  33. Cameron, A.C., Trivedi, P.K.: Regression Analysis of Count Data. Cambridge University Press, Cambridge (2013)

    Book  Google Scholar 

  34. Poisson Regression, Chapter 325, NCSS Statistical Software. https://ncss-wpengine.netdna-ssl.com/wp-content/themes/ncss/pdf/Procedures/NCSS/Poisson_Regression.pdf. Accessed 10 Mar 2019

  35. Hilbe, J.M.: Modeling Count Data. Cambridge University Press, Cambridge (2014)

    Book  Google Scholar 

  36. Negative Binomial Regression, Chapter 326, NCSS Statistical Software. https://ncss-wpengine.netdna-ssl.com/wp-content/themes/ncss/pdf/Procedures/NCSS/Negative_Binomial_Regression.pdf. Accessed 11 Mar 2019

  37. Dávila, V.H.L., Cabral, C.R.B., Zeller, C.B.: Finite Mixture of Skewed Distributions. Springer, Cham (2018)

    Book  Google Scholar 

  38. McLachlan, G., Peel, D.: Finite Mixture Models. Wiley, New York (2000)

    Book  Google Scholar 

  39. Sellers, K.F., Shmueli, G.: Predicting Censored Count Data with COM-Poisson Regression. Robert H. Smith School Research Paper No. RHS-06-129, (2010). https://ssrn.com/abstract=1702845. Accessed on 17 Mar 2019

  40. Cameron, A., Trivedi, P.: Generalized Count Regression. In Regression Analysis of Count Data (Econometric Society Monographs, pp. 111–176). Cambridge University Press, UK, Cambridge (2013)

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Jahangirnagar University, Savar, Dhaka, Bangladesh

    Md. Karimuzzaman

  2. School of Computing and Mathematics, Charles Sturt University, Wagga Wagga, Australia

    Azizur Rahman

  3. School of Mathematics, Statistics and Physics, Newcastle University, Newcastle upon Tyne, UK

    Md. Moyazzem Hossain

  4. Department of Statistics, Jahangirnagar University, Savar, Dhaka, Bangladesh

    Md. Moyazzem Hossain

Authors
  1. Md. Karimuzzaman
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Md. Moyazzem Hossain
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Azizur Rahman
    View author publications

    You can also search for this author in PubMed Google Scholar

Editor information

Editors and Affiliations

  1. School of Computing and Mathematics, Charles Sturt University, Wagga Wagga, NSW, Australia

    Azizur Rahman

Rights and permissions

Reprints and permissions

Copyright information

© 2020 Springer Nature Singapore Pte Ltd.

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Karimuzzaman, M., Moyazzem Hossain, M., Rahman, A. (2020). Finite Mixture Modelling Approach to Identify Factors Affecting Children Ever Born for 15–49 Year Old Women in Asian Country. In: Rahman, A. (eds) Statistics for Data Science and Policy Analysis. Springer, Singapore. https://doi.org/10.1007/978-981-15-1735-8_17

Download citation

Publish with us

Policies and ethics

Search

Navigation