On a Statistical Model Useful for Demographics: Estimating the Mean Number of Children Ever Born Through the Distribution of Male Births with an Application to Data from India

Authors

  • Shubhagata Roy ICFAI Business School, IFHE, Hyderabad, India
  • Prayas Sharma 2)Department of Decision sciences, Indian Institute of Management Sirmaur, Sirmaur, India 3)Department of Statistics, Babasaheb Bhimrao Ambedkar University, Lucknow, India
  • K. K. Singh Department of Statistics, BHU, Varanasi, India
  • Richa Srivastava Decision Sciences, Jaipuria Institute of Management, Lucknow, Uttar Pradesh, India

DOI:

https://doi.org/10.13052/jrss0974-8024.1613

Keywords:

Fecundity, Fecundability, Family planning, Fertility, Fertility transition, NFHS, Reproductive health

Abstract

The connection between male births and fertility can be easily linked with demographic transition and in defining the population distribution. In this context, it is necessary to understand the birth patterns in Indian societies which are governed by some or the other probability distributions. Although child birth is a biological process but it is very much influenced by a number of social, economic, cultural and psychological factors. Numerous demographers have proposed mathematical models to predict the number of male and female births during a given time period taking into consideration the various factors. Traditionally, estimating current levels and future trends of mean number of births is done using various life tables, cohort-component method, time-series analysis, micro-simulations, structural modeling, expert analysis, historical error analysis and also using an appropriate probability model and testing the model on real data. In the present study we developed a model for estimating the mean number of children ever born through the join probability distribution with its application for male births among the females of Uttar Pradesh and Bihar. The reasons of selecting these two states were their huge population and high total fertility rates. The model fits to the data of these two states, therefore it would be a good fit for the other states too, which shows the efficiency and applicability of the model. The applicability of this model has been illustrated on real data obtained from the National Family Health Survey-3 (2005–06). The various estimates of the parameters have been obtained by using the method of moments and suitability of the proposed model has been tested using the ‘goodness of fit’ criteria.

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Author Biographies

Shubhagata Roy, ICFAI Business School, IFHE, Hyderabad, India

Shubhagata Roy is an Assistant Professor in the Department of Operations and IT at IBS Hyderabad. He has more than 20 years of experience in industry and academia. His teaching areas are Business Analytics, Business Statistics, Predictive Modeling, Operations Research, Research Methodology, Operations Management and Quality Management. His research areas include Demography, Healthcare Analytics, Insurance Analytics, Ethics and Sustainability. He holds a PhD (Statistics) from Banaras Hindu University.

Prayas Sharma, 2)Department of Decision sciences, Indian Institute of Management Sirmaur, Sirmaur, India 3)Department of Statistics, Babasaheb Bhimrao Ambedkar University, Lucknow, India

Prayas Sharma is currently working as Assistant Professor in the area of Decision Sciences at Indian Institute of Management Sirmaur, Paonta Sahib, Himachal Pradesh. He has more than 10 years of academic experience, both in the domain of teaching and research. His research interest includes Survey Sampling, Estimation Procedures using Auxiliary Information and Measurement Errors, Predictive Modelling, Business Analytics and Operations Research. Dr. Sharma has published more than 40 research papers in reputed National & International journals along with one book and two chapters in book internationally published. He has more than 400 citations with H-Index 14 & I index of 15. Dr. Sharma has a keen interest in reading, writing and publishing, he is serving 7 reputed journals as editor/associate editor and more than 30 journals as reviewer and reviewed more than 150 research papers from the prestigious.

K. K. Singh, Department of Statistics, BHU, Varanasi, India

K. K. Singh is a Professor in the Department of Statistics, Institute of Science, Banaras Hindu University Varanasi. He has more than 40 years of experience in teaching various courses in the field of Statistics. He has successfully completed several projects funded by ICMR, UGC, Rockefeller Foundation, WHO etc. He has authored more than 150 research papers and four books in collaboration with renowned Statisticians in the area of Demography and Population Science. He has been in the boards of several Central and State Universities since past three decades. Dr. Singh holds a Ph.D. (Statistics) from Banaras Hindu University and Post-Doc from Carolina Population Center, University of North Carolina, USA.

Richa Srivastava, Decision Sciences, Jaipuria Institute of Management, Lucknow, Uttar Pradesh, India

Richa Srivastava is currently working as Assistant Professor in the area of Decision Sciences at Jaipuria Institute of Management, Lucknow, Uttar Pradesh. She is an academician and researcher having more than 8 years of experience in the field of Statistics. Her research interest includes Bayesian Statistics, Multivariate Analysis, Business Analytics, Biostatistics and Operations Research. Her research papers have been extensively published in reputed refereed journals. She has also delivered an invited talk in the International Workshop/Conference on Bayesian Theory and Applications (IWCBTA) and also won the second-best paper presentation award in IWCBTA, held in 2013. She is also associated with various academic bodies.

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Published

2023-06-21

How to Cite

Roy, S. ., Sharma, P. ., Singh, K. K. ., & Srivastava, R. . (2023). On a Statistical Model Useful for Demographics: Estimating the Mean Number of Children Ever Born Through the Distribution of Male Births with an Application to Data from India. Journal of Reliability and Statistical Studies, 16(01), 57–80. https://doi.org/10.13052/jrss0974-8024.1613

Issue

2023: Vol 16 Iss 1

Section

Articles