Analysis of the human sex ratio by using overdispersion models

Journal of the Royal Statistical Society. Series C: Applied Statistics

Volumn 47, Issue 1, 1998, Pages 149-157

  Lindsey, J K ^a   Altham, P M E ^b  

^a HASSELT UNIVERSITY   (Belgium)

^b Limburgs Universitair Centrum ^*

Author keywords

'Multiplicative' binomial distribution; Akaike information criterion; Beta binomial distribution; Direct likelihood inference; Double binomial distribution; Finite mixture model; Non linear Poisson regression; Overdispersion

Indexed keywords

ARTICLE; DEMOGRAPHIC FACTORS; DEMOGRAPHY; DEVELOPED COUNTRY; EUROPE; FAMILY AND HOUSEHOLD; FAMILY SIZE; GERMANY; HISTORICAL SURVEY; METHODOLOGY; POPULATION; POPULATION AND POPULATION RELATED PHENOMENA; RESEARCH; SEX DIFFERENCE; SEX RATIO; THEORETICAL MODEL; WESTERN EUROPE;

DEMOGRAPHIC FACTORS; DEVELOPED COUNTRIES; EUROPE; FAMILY AND HOUSEHOLD; FAMILY CHARACTERISTICS; FAMILY SIZE; GERMANY; HISTORICAL SURVEY; MODELS, THEORETICAL; POPULATION; POPULATION CHARACTERISTICS; RESEARCH METHODOLOGY; SEX DISTRIBUTION; SEX FACTORS; SEX RATIO; WESTERN EUROPE;

DEMOGRAPHY; DEVELOPED COUNTRIES; EUROPE; FAMILY CHARACTERISTICS; GERMANY; MODELS, THEORETICAL; POPULATION; POPULATION CHARACTERISTICS; RESEARCH; SEX DISTRIBUTION; SEX FACTORS; SEX RATIO;

EID: 0031607104     PISSN: 00359254     EISSN: None     Source Type: Journal    
DOI: 10.1111/1467-9876.00103     Document Type: Article

References (22)

1. Akaike, H. (1973) Information theory and an extension of the maximum likelihood principle. In Proc. 2nd Int. Symp. Inference Theory (eds B. N. Petrov and F. Csàki), pp. 267-281. Budapest: Akadémiai Kiadó.
2. Altham, P. M. E. (1978) Two generalizations of the binomial distribution. Appl. Statist., 27, 162-167.
3. Cox, D. R. (1972) The analysis of multivariate binary data. Appl. Statist., 21, 113-120.
4. Cox, D. R and Wermuth, N. (1996) Multivariate Dependencies: Models, Analysis, and Interpretation. London: Chapman and Hall.
5. Dennis, J. E. and Schnabel, R. B. (1983) Numerical Methods for Unconstrained Optimization and Nonlinear Equations. New York: Prentice Hall.
6. Edwards, A. W. F. (1958) An analysis of Geissler's data on the human sex ration. Ann. Hum. Genet., 23, 6-15.
7. _(1972) Likelihood: an Account of the Statistical Concept of Likelihood and Its Application to Scientific Inference. Cambridge: Cambridge University Press.
8. Efron, B. (1986) Double exponential families and their use in generalized linear regression. J. Am. Statist. Ass., 81, 709-721.
9. Fisher, R. A. (1958) Statistical Methods for Research Workers, 13th edn. Edinburgh: Oliver and Boyd.
10. Geissler, A. (1889) Beiträge zur Frage des Geschlechtsverhältnisses der Geborenen. Z. Köngl. Sächs. Statist. Bur., 35, 1-24.
11. Ihaka, R. and Gentleman, R. (1996) R: a language for data analysis and graphics. J. Comput. Graph. Statist., 5, 299-314.
12. Lindsey, J. K. (1974a) Construction and comparison of statistical models. J. R. Statist. Soc. B, 36, 418-425.
13. _(1974b) Comparison of probability distributions. J. R. Statist. Soc. B, 36, 38-47.
14. _(1993) Models for Repeated Measurements. Oxford: Oxford University Press.
15. _(1995) Modelling Frequency and Count Data. Oxford: Oxford University Press.
16. _(1996) Parametric Statistical Inference. Oxford: Oxford University Press.
17. Lindsey, J. K. and Laurent, C. (1996) Estimating the proportion of lymphoblastoid cells affected by exposure to ethylene oxide through micronuclei counts. Statistician, 45, 223-229.
18. Lindsey, J. K. and Mersch, G. (1992) Fitting and comparing probability distributions with log linear models. Comput. Statist. Data Anal., 13, 373-384.
19. Pregibon, D. (1984) Review of 'Generalized Linear Models' by McCullagh and Neider. Ann. Statist., 12, 1589-1596.
20. Ross, G. J. S. (1990) Non-linear Estimation. Berlin: Springer.
21. Skellam, J. G. (1948) A probability distribution derived from the binomial distribution by regarding the probability of success as variable between sets of trials. J. R. Statist. Soc. B, 10, 257-261.
22. Sokal, R. R. and Rohlf, F. J. (1969) Biometry: the Principles and Practice of Statistics in Biological Research. San Francisco: Freeman.