TY - JOUR
T1 - Generalized EM estimation for semi-parametric mixture distributions with discretized non-parametric component
AU - Ma, Jun
AU - Gudlaugsdottir, Sigurbjorg
AU - Wood, Graham
PY - 2011/10
Y1 - 2011/10
N2 - We consider independent sampling from a two-component mixture distribution, where one component (called the parametric component) is from a known distributional family and the other component (called the non-parametric component) is unknown. This is a semi-parametric mixture distribution. We discretize the non-parametric component and estimate the parameters of this mixture model, namely the mixing proportion, the unknown parameters of the parametric component and the discretized non-parametric component. We define the maximum penalized likelihood (MPL) estimates of the mixture model parameters and then develop a generalized EM (GEM) iterative scheme to compute the MPL estimates. A simulation study and an example from biology are presented.
AB - We consider independent sampling from a two-component mixture distribution, where one component (called the parametric component) is from a known distributional family and the other component (called the non-parametric component) is unknown. This is a semi-parametric mixture distribution. We discretize the non-parametric component and estimate the parameters of this mixture model, namely the mixing proportion, the unknown parameters of the parametric component and the discretized non-parametric component. We define the maximum penalized likelihood (MPL) estimates of the mixture model parameters and then develop a generalized EM (GEM) iterative scheme to compute the MPL estimates. A simulation study and an example from biology are presented.
UR - http://www.scopus.com/inward/record.url?scp=80051470348&partnerID=8YFLogxK
U2 - 10.1007/s11222-010-9195-y
DO - 10.1007/s11222-010-9195-y
M3 - Article
AN - SCOPUS:80051470348
SN - 0960-3174
VL - 21
SP - 601
EP - 612
JO - Statistics and Computing
JF - Statistics and Computing
IS - 4
ER -