References
- Barkalov, N. B. 1988. Interpolation of demographic data using rational spline functions. Demograficheskie issledovaniia (Kiev, Ukraine) 44.
- Bermúdez, S., and R. Blanquero. 2016. Optimization models for degrouping population data. Population studies 70 (2):259–72. doi: https://doi.org/10.1080/00324728.2016.1158853.
- Chandola, T., D. A. Coleman, and R. W. Hiorns. 2002. Distinctive features of age-specific fertility profiles in the English-speaking world: Common patterns in Australia, Canada, New Zealand and the United States, 1970–98. Population Studies 56 (2):181–200. doi:https://doi.org/10.1080/00324720215929.
- Chandola, T., D. A. Coleman, and R. W. Hiorns. 1999. Recent European fertility patterns: Fitting curves to ‘distorted' distributions. Population Studies 53 (3):317–29. doi:https://doi.org/10.1080/00324720308089.
- Gelfand, A. E., and S. K. Ghosh. 1998. Model choice: A minimum posterior predictive loss approach. Biometrika 85 (1):1–11.
- Grigoriev, Pavel, et al. 2018. New methods for estimating detailed fertility schedules from abridged data. No. WP-2018-001. Max Planck Institute for Demographic Research, Rostock, Germany.
- Hadwiger, H. 1940. Eine analytische reprodutions-funktion fur biologische Gesamtheiten. Skandinavisk Aktuarietidskrift 1940 (3–4):101–13.
- Hoem, J. M., D. Madsen, J. L. Nielsen, E. Ohlsen, H. O. Hansen, and B. Rennermalm. 1981. Experiments in modelling recent Danish fertility curves. Demography 18 (2):231–44.
- Laud, P. W., and J. G. Ibrahim. 1995. Predictive model selection. Journal of the Royal Statistical Society. Series B (Methodological) 57 (1):247–62. doi:https://doi.org/10.1111/j.2517-6161.1995.tb02028.x.
- Mazzuco, S., and B. Scarpa. 2015. Fitting age-specific fertility rates by a flexible generalized skew normal probability density function. Journal of the Royal Statistical Society. Series A (Statistics in Society) 187–203.
- McNEIL, D. R., T. J. Trussell, and J. C. Turner. 1977. Spline interpolation of demographic data. Demography 14 (2):245–52.
- Mishra, R. K., and S. K. Upadhyay. 2019. Parametric Bayes Analyses to Study the Age-Specific Fertility Patterns. American Journal of Mathematical and Management Sciences 38 (2):151–73.
- Mitra, S., and A. Romaniuk. 1973. Pearsonian type I curve and Its fertility potentials. Demography 10 (3):351–65.
- Nanjo, Zenji. 1987. Demographic data and spline interpolation. Jinkogaku Kenkyu 10: 43–53.
- Peristera, P., and A. Kostaki. 2007. Modeling fertility in modern populations. Demographic Research 16 (6):141–94.
- Robert, C., and G. Casella. 2013. Monte Carlo statistical methods. Springer Science & Business Media.
- Romaniuk, A. 1973. A three parameter model for birth projections. Population Studies 27 (3):467–78. doi:https://doi.org/10.1080/00324728.1973.10405494.
- Schmertmann, C. P. 2014. Calibrated spline estimation of detailed fertility schedules from abridged data. Revista Brasileira de Estudos de População 31 (2):291–307.
- Schwarz, G. 1978. Estimating the dimension of a model. The Annals of Statistics 6 (2):461–64.
- Smith, A. F. M., and G. O. Roberts. 1993. Bayesian computation via the Gibbs sampler and related Markov chain Monte Carlo methods. Journal of the Royal Statistical Society. Series B (Methodological) 55 (1):3–23.
- Smith, L., R. J. Hyndman, and S. N. Wood. 2004. Spline interpolation for demographic variables: The monotonicity problem. Journal of Population Research 21 (1):95–98.
- Upadhyay, S. K., A. Gupta, and D. K. Dey. 2012. Bayesian modelling of bathtub-shaped hazard rate using various Weibull extensions and related issues of model selection. Sankhya, B 74 (1):15–43.
- Upadhyay, S. K., and A. F. M. Smith. 1994. Modelling complexity in reliability, and the role of simulation in Bayesian computation. International Journal of Continuing Engineering Education and Life Long Learning 4 (1–2):93–104.
- Zenji, Nanjo. 1986. Practical Use of Interpolatory Cubic and Rational Spline Functions for Fertility Data. NUPRI research paper series 34: 13.