A note on improving quadratic inference functions using a linear shrinkage approach

作者：Han Peisong^*; Song Peter X K

来源：Statistics & Probability Letters, 2011, 81(3): 438-445.

DOI：10.1016/j.spl.2010.12.010

• Clinical trials
• Estimation efficiency
• Generalized estimating equations
• Longitudinal data
• Shrinkage

In some commonly used longitudinal clinical trials designs, the quadratic inference functions (QIF) method fails to work due to non-invertible estimation of the optimal weighting matrix. We propose a modified QIF method, in which the optimal weighting matrix is estimated by a linear shrinkage estimator, replacing the sample covariance matrix. We prove that the linear shrinkage estimator is consistent and asymptotically optimal under the expected quadratic loss, and will have more stable numerical performance than the sample covariance matrix. Simulations show that numerical improvements are acquired in light of a higher percentage of convergence, and smaller standard errors and mean square errors of parameter estimates.

• 出版日期2011-3