TY - JOUR
T1 - Consistency of estimators of population scaled parameters using composite likelihood
AU - Wiuf, Carsten
PY - 2006/11/1
Y1 - 2006/11/1
N2 - Composite likelihood methods have become very popular for the analysis of large-scale genomic data sets because of the computational intractability of the basic coalescent process and its generalizations: It is virtually impossible to calculate the likelihood of an observed data set spanning a large chromosomal region without using approximate or heuristic methods. Composite likelihood methods are approximate methods and, in the present article, assume the likelihood is written as a product of likelihoods, one for each of a number of smaller regions that together make up the whole region from which data is collected. A very general framework for neutral coalescent models is presented and discussed. The framework comprises many of the most popular coalescent models that are currently used for analysis of genetic data. Assume data is collected from a series of consecutive regions of equal size. Then it is shown that the observed data forms a stationary, ergodic process. General conditions are given under which the maximum composite estimator of the parameters describing the model (e.g. mutation rates, demographic parameters and the recombination rate) is a consistent estimator as the number of regions tends to infinity.
AB - Composite likelihood methods have become very popular for the analysis of large-scale genomic data sets because of the computational intractability of the basic coalescent process and its generalizations: It is virtually impossible to calculate the likelihood of an observed data set spanning a large chromosomal region without using approximate or heuristic methods. Composite likelihood methods are approximate methods and, in the present article, assume the likelihood is written as a product of likelihoods, one for each of a number of smaller regions that together make up the whole region from which data is collected. A very general framework for neutral coalescent models is presented and discussed. The framework comprises many of the most popular coalescent models that are currently used for analysis of genetic data. Assume data is collected from a series of consecutive regions of equal size. Then it is shown that the observed data forms a stationary, ergodic process. General conditions are given under which the maximum composite estimator of the parameters describing the model (e.g. mutation rates, demographic parameters and the recombination rate) is a consistent estimator as the number of regions tends to infinity.
KW - Coalescent theory
KW - Composite likelihood
KW - Consistency
KW - Estimator
KW - Genomic data
UR - http://www.scopus.com/inward/record.url?scp=33750543338&partnerID=8YFLogxK
U2 - 10.1007/s00285-006-0031-0
DO - 10.1007/s00285-006-0031-0
M3 - Journal article
C2 - 16960689
AN - SCOPUS:33750543338
SN - 0303-6812
VL - 53
SP - 821
EP - 841
JO - Journal of Mathematical Biology
JF - Journal of Mathematical Biology
IS - 5
ER -