TY - JOUR
T1 - Local influence assessment in the growth curve model with unstructured covariance
AU - Pan, Jian-Xin
AU - Fang, Kai-Tai
AU - von Rosen, Dietrich
N1 - Funding Information:
where X and Z are known design matrices of ranks m < p and r < n, respectively, and B is an unknown regression coefficient matrix. The columns of the error matrix E are independent p-variate normal with mean 0 and unknown covariance matrix I; > 0, i.e, Y ~ Np, n(XBZ, X, ln). Usually, p is the number of time points observed on each of * Corresponding author. E-mail: [email protected]. J Supported partially by the Hong Kong UPGC Grant and the Swedish Natural Research Council.
PY - 1997/8/15
Y1 - 1997/8/15
N2 - In this paper, a local influence approach is employed to assess adequacy of the growth curve model with an unstructured covariance, based on likelihood displacement. The Hessian matrix of the model is investigated in detail under an abstract perturbation scheme. For illustration, covariance-weighted perturbation is discussed and used to analyze two real-life biological data sets, which show that the criteria presented in this article are useful in practice.
AB - In this paper, a local influence approach is employed to assess adequacy of the growth curve model with an unstructured covariance, based on likelihood displacement. The Hessian matrix of the model is investigated in detail under an abstract perturbation scheme. For illustration, covariance-weighted perturbation is discussed and used to analyze two real-life biological data sets, which show that the criteria presented in this article are useful in practice.
KW - Curvature
KW - Growth curve model
KW - Hessian matrix
KW - Likelihood displacement
KW - Local influence
KW - Perturbation
KW - Statistical diagnostic
UR - http://www.scopus.com/inward/record.url?scp=0031571477&partnerID=8YFLogxK
U2 - 10.1016/s0378-3758(96)00194-2
DO - 10.1016/s0378-3758(96)00194-2
M3 - Journal article
AN - SCOPUS:0031571477
SN - 0378-3758
VL - 62
SP - 263
EP - 278
JO - Journal of Statistical Planning and Inference
JF - Journal of Statistical Planning and Inference
IS - 2
ER -