关于一个对人口统计有用的统计模型:通过男性出生率分布估计平均出生儿童数——应用于印度的数据

IF 0.9 Q3 STATISTICS & PROBABILITY Journal of Reliability and Statistical Studies Pub Date : 2023-06-21 DOI:10.13052/jrss0974-8024.1613
Shubhagata Roy, Prayas Sharma, K. K. Singh, R. Srivastava
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引用次数: 0

摘要

男性出生和生育率之间的联系可以很容易地与人口结构转型和人口分布的定义联系起来。在这种情况下,有必要了解印度社会的出生模式,这些模式受一些或其他概率分布的支配。虽然孩子的出生是一个生物学过程,但它在很大程度上受到许多社会、经济、文化和心理因素的影响。许多人口统计学家提出了数学模型,在考虑各种因素的情况下,预测给定时间段内的男性和女性出生人数。传统上,估计平均出生人数的当前水平和未来趋势是使用各种生命表、队列组成方法、时间序列分析、微观模拟、结构建模、专家分析、历史误差分析来完成的,还使用适当的概率模型并在实际数据上测试该模型。在本研究中,我们开发了一个模型,通过联合概率分布来估计平均出生儿童数,并将其应用于北方邦和比哈尔邦女性中的男性出生。选择这两个州的原因是人口众多,总生育率高。该模型适合这两个状态的数据,因此它也很适合其他状态,这表明了该模型的有效性和适用性。该模型的适用性已在第三次全国家庭健康调查(2005-2006)中获得的真实数据中得到说明。通过使用矩量法获得了参数的各种估计,并使用“拟合优度”标准测试了所提出模型的适用性。
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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
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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