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2. Counting Methods and the EM Algorithm
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[3] Crow JF (1965) Problems of ascertainment in the analysis of family
data. Epidemiology and Genetics of Chronic Disease. Public Health
Service Publication 1163, Neel JV, Shaw MW, Schull WJ, editors,
Department of Health, Education, and Welfare, Washington, DC
[4] Crow JF (1986) Basic Concepts in Population, Quantitative, and Eco-
logical Genetics. Freeman, San Francisco
[5] Dempster AP, Laird NM, Rubin DB (1977) Maximum likelihood from
incomplete data via the EM algorithm (with discussion). J Roy Stat
Soc B 39:1–38
[6] Ferguson TS (1996) A Course in Large Sample Theory. Chapman &
Hall, London
[7] Flury B, Zopp` A (2000) Exercises in EM. Amer Statistician 54:207–
e
209
[8] Lawrence CE, Reilly AA (1990) An expectation maximization (EM)
algorithm for the identification and characterization of common sites
in unaligned bipolymer sequences. Proteins 7:41–51

[9] Little RJA, Rubin DB (1987) Statistical Analysis with Missing Data.
Wiley, New York

[10] Ott J (1977) Counting methods (EM algorithm) in human pedigree
analysis: linkage and segregation analysis. Ann Hum Genet 40:443–454

[11] Rao CR (1973) Linear Statistical Inference and Its Applications, 2nd
ed. Wiley, New York

[12] Smith CAB (1957) Counting methods in genetical statistics. Ann Hum
Genet 21:254–276

[13] Tanner MA (1993) Tools for Statistical Inference: Methods for the Ex-
ploration of Posterior Distributions and Likelihood Functions, 2nd ed.
Springer-Verlag, New York
[14] Thompson EA (1986) Pedigree Analysis in Human Genetics. Johns
Hopkins University Press, Baltimore
[15] Titterington DM, Smith AFM, Makov UE (1985) Statistical Analysis
of Finite Mixture Distributions. Wiley, New York
[16] Weeks DE, Lange K (1989) Trials, tribulations, and triumphs of the
EM algorithm in pedigree analysis. IMA J Math Appl Med Biol 6:209–
232

[17] Weir BS (1996) Genetic Data Analysis II. Sinauer, Sunderland, MA