TY - JOUR
T1 - On local estimating equations in additive multiparameter models
AU - Claeskens, G.A.M.
AU - Aerts, M.
PY - 1999
Y1 - 1999
N2 - Estimating all parameters in a multiparameter response model as smooth functions of an explanatory variable is very similar to estimating the different components of an additive model for the response mean. It is shown that, in a general estimating framework, local polynomial backfitting estimators in an additive one-parameter model do not work optimally. For a multiparameter model, however, a backfitting algorithm can be defined that leads to local polynomial estimators that do have optimal properties.
AB - Estimating all parameters in a multiparameter response model as smooth functions of an explanatory variable is very similar to estimating the different components of an additive model for the response mean. It is shown that, in a general estimating framework, local polynomial backfitting estimators in an additive one-parameter model do not work optimally. For a multiparameter model, however, a backfitting algorithm can be defined that leads to local polynomial estimators that do have optimal properties.
U2 - 10.1016/S0167-7152(00)00042-0
DO - 10.1016/S0167-7152(00)00042-0
M3 - Article
VL - 49
SP - 139
EP - 148
JO - Statistics and Probability Letters
JF - Statistics and Probability Letters
SN - 0167-7152
IS - 2
ER -