A new probability model for estimation of child mortality for fixed parity

Authors

  • Sonam Maheshwari Department of Community Medicine, SRMS-IMS, Bhojipura, Bareilly-243 202
  • Brijesh Singh Faculty of Commerce,BHU
  • Puneet Gupta Department of Economics and Statistics, Rampur

DOI:

https://doi.org/10.14419/ijh.v3i2.5033

Published:

2015-08-24

Keywords:

Child Mortality, Parameter Estimation, Kumaraswamy Distribution.

Abstract

In demography, child mortality is useful as a sensitive index of a nation’s health conditions and as guided for the structuring of public health schemes. In the present study, we proposed a probability model for the number of child loss among females for a fixed parity. The application of the model proposed in the paper is illustrated through its application to the data from Madhya Pradesh from National Family Health Survey-III (NFHS-III). Finally, we show that proposed model is better fitted than the Beta-Binomial model for the data.

References

[1] Arnold BC (1993) Pareto Distributions. Vol.5 in statistical distribution, Fairland (MD), International Co-operative Publishing house.

[2] Bandyopadhyay D, Reich BJ & Slate EH (2011), a spatial beta-binomial model for clustered count data on dental caries. Statistical Methods in Medical Research, 20(2), 85-102. http://dx.doi.org/10.1177/0962280210372453.

[3] Bhuyan KC &Degraties R (1999) on the probability model of child mortality pattern in North Eastern Libya. The Turkish Journal of Population Studies, 21, 33-37.

[4] Chatfield C &Goodhardt, GJ (1970), the beta-binomial model for consumer purchasing behaviour. Journal of the Royal Statistical Society. Series C (Applied Statistics), 19(3), 240-250. http://dx.doi.org/10.2307/2346328.

[5] Chauhan RK (1997), Graduation of infant Deaths by Age. Demography India, 26(2), 261-174.

[6] Dennis JE & Schnabel RB (1983), Numerical Methods for Unconstrained Optimization and Nonlinear Equations, Prentice-Hall, Englewood Cliffs, NJ, 1983.

[7] Ennis DM & Bi J (1998), the beta-binomial model: accounting for inter-trial variation in replicated difference and preference tests. Journal of Sensory Studies, 13(4), 389-412. http://dx.doi.org/10.1111/j.1745-459X.1998.tb00097.x.

[8] Gange SJ, Munoz A, Saez M & Alonso J (1996), Use of the beta-binomial distribution to model the effect of policy changes on appropriateness of hospital stays. Journal of the Royal Statistical Society. Series C (Applied Statistics), 45(3), 371-382. http://dx.doi.org/10.2307/2986094.

[9] Goldblatt PO (1989), Mortality by social class, 1971-85. Population Trends, No. 56, Summer.

[10] Griffiths DA (1973), Maximum likelihood estimation for the beta-binomial distribution and an application to the household distribution of the total number of cases of a disease. Biometrics, 29(4), 637-648. http://dx.doi.org/10.2307/2529131.

[11] Haseman JK &Kupper LL (1979), Analysis of dichotomous response data from certain toxicological experiments. Biometrics, 35(1), 281-293. http://dx.doi.org/10.2307/2529950.

[12] Heligman L & Pollard JH (1980) Age Pattern of Mortality, Journal of Institute of Actuaries, 117, 49-80. http://dx.doi.org/10.1017/S0020268100040257.

[13] Henningsena &Toomet O (2010), maxLik: a package for maximum likelihood estimation in R, Computational Statistics, 26 (3).

[14] Hill AG &Devid HP (1989), Measuring Child Mortality in the Third World, in N. Sources and Approaches, eds, IUSSP Proceeding of international conference, New Delhi, India.

[15] Jones MC (2009), Kumaraswamy’s distribution: A beta-type distribution with some tractability advantages. Statistical Methodology, 6(1), 70-81. http://dx.doi.org/10.1016/j.stamet.2008.04.001.

[16] Keyfitz N (1977), Introduction to the Mathematics of Population with Revisions. Reading, Mssachusetts. Addition-Wesley Publishing Company.

[17] Krishnan P (1993) Mortality modeling with order Statistics, Edmonton: Population Research Laboratory, Department of Sociology, University of Alberta, Research Discussion paper No. 95.

[18] Kumaraswamy P (1980) a generalized probability density function for double-bounded random processes. Journal of Hydrology, 46(1), 79-88.http://dx.doi.org/10.1016/0022-1694(80)90036-0.

[19] Li XH, Huang YY, Zhao XY (2011) the Kumaraswamy Binomial Distribution. Chinese Journal of Applied Probability and Statistics, 27(5), 511-521.

[20] Pathak KB, Pandey A, Mishra US (1991) On Estimating current Levels of Fertility and Child Mortality from the Data on Open Birth Interval and Survival Status of the last Child, Janasamkhya, 9, 15-24.

[21] Paul SR (1982) Analysis of proportions of affected foetuses in teratological experiments. Biometrics, 38(2), 361-370.http://dx.doi.org/10.2307/2530450.

[22] Ronald D Lee, Lawrece R, Carter (1992) Modeling and Forecasting U.S. mortality. Journal of American Statistical Association, 87, 659-675.

[23] Singh KK, Singh Brijesh P & Singh N (2011) A Probability Model for Number of Child Death for Fixed Parity. Demography India, 40(2), 55-68.

[24] Singh Brijesh P, Singh N, Roy TK & Singh G (2011) on the pattern of child loss in Madhya Pradesh. India 2011, Mortality, Health and Development (Edited by AalokRanjan), 75-88.

[25] Skellam JG (1948) a probability distribution derived from the binomial distribution by regarding the probability of success as variable between the sets of trials. Journal of the Royal Statistical Society. Series B (Methodological), 10(2), 257-261.

[26] Tripathi RC, Gupta RC, Gurland J (1994) Estimation of parameters in the beta binomial model. Annals of the Institute of Statistical Mathematics, 46(2), 17-331.http://dx.doi.org/10.1007/bf01720588.

[27] Williams DA (1975) the analysis of binary responses from toxicological experiments involving reproduction and teratogenicity. Biometrics, 31(4), 949-952.http://dx.doi.org/10.2307/2529820.

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