Maximum likelihood estimates for the parameters of a continuous time model for first conception
Bibliographic Data
| ID | 10969718 |
|---|---|
| Authors | S N Singh (0000-0002-6227-6171, Demographic Research Centre, Banaras Hindu University, Varanasi, India, corresponding author), T Bhaduri (Demographic Research Centre, Banaras Hindu University, Varanasi, India) |
| Year | 1972 |
| Volume | 9 |
| Issue | 2 |
| Pages | 249-255 |
| Publication date | 1972-05-01 |
| Peer Reviewed | Yes |
| Open Access | Yes |
| Type | ARTICLE |
| Venue | Demography (JOURNAL) |
| Journal identifiers | ISSN: 0070-3370 • E-ISSN: 1533-7790 |
| Publisher | Duke University Press (PUBLISHER • US) |
| DOI | 10.2307/2060637 |
| PMID | 4670323 |
| OpenAlex | W2027070583 |
| Language | EN |
| Citations received | 1 |
| References cited | 4 |
The duration of time between two successive births or between marriage and first birth is an indicator of the level of fertility of a couple. Potter and Parker (1964) and Singh (1961, 1967) have suggested the Type I Geometric as a distribution appropriate for representing the length of interval to first conception leading to a live birth. Potter and Parker estimated the parameters of this distribution with the help of the first two moments. Majumdar and Sheps (1970) pointed out the limitations of these moment estimates and gave a method to obtain maximum likelihood estimates, based on formulas which are too involved for solution without the help of a computer. Singh proposed a continuous probability distribution based on another set of assumptions for the above situation. He outlined a method to obtain best asymptotically normal estimates of the parameters. These estimates are obtained after several iterations starting from any set of consistent estimates. The objective of this paper is to show that it is relatively easier to obtain maximum likelihood estimates of the parameters of the continuous model, which describes the data on duration to first conception as well as does the discrete model. Simple expressions for the moment and maximum likelihood estimates with the corresponding covariance matrices are obtained. Application is made to three sets of data
Combinatorics · Covariance · Duration (music · Interval (graph theory · Maximum likelihood · Moment (physics · Set (abstract data type · Statistics · Applied Mathematics · Computer Science · demographic modeling and climate adaptation · Insurance, Mortality, Demography, Risk Management · Mathematics · Statistical Methods and Bayesian Inference
| Unique citing works | 1 |
|---|---|
| Citations per year | 0,02 |
| Citation span | 1976 - 1976 (1) |
| Citation velocity | historical |
| Highly cited | No |
| Citation types | Neutral: 1 |