TY - JOUR
T1 - Incomplete data in generalized linear models with continuous covariates
AU - Weisberg, Sanford
AU - Ibrahim, J
PY - 1992
Y1 - 1992
N2 - This paper proposes a method for estimating the parameters in a generalized linear model with missing covariates. The missing covariates are assumed to come from a continuous distribution, and are assumed to be missing at random. In particular, Gaussian quadrature methods are used on the E‐step of the EM algorithm, leading to an approximate EM algorithm. The parameters are then estimated using the weighted EM procedure given in Ibrahim (1990). This approximate EM procedure leads to approximate maximum likelihood estimates, whose standard errors and asymptotic properties are given. The proposed procedure is illustrated on a data set. Copyright © 1992, Wiley Blackwell. All rights reserved
AB - This paper proposes a method for estimating the parameters in a generalized linear model with missing covariates. The missing covariates are assumed to come from a continuous distribution, and are assumed to be missing at random. In particular, Gaussian quadrature methods are used on the E‐step of the EM algorithm, leading to an approximate EM algorithm. The parameters are then estimated using the weighted EM procedure given in Ibrahim (1990). This approximate EM procedure leads to approximate maximum likelihood estimates, whose standard errors and asymptotic properties are given. The proposed procedure is illustrated on a data set. Copyright © 1992, Wiley Blackwell. All rights reserved
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U2 - 10.1111/j.1467-842X.1992.tb01062.x
DO - 10.1111/j.1467-842X.1992.tb01062.x
M3 - Article
SN - 0004-9581
VL - 34
SP - 461
EP - 470
JO - Australian Journal of Statistics
JF - Australian Journal of Statistics
IS - 3
ER -