Estimating multivariate density and its derivatives for mixed measurement error data

A Parametric Bootstrap Approach for a One-Way Error Component Regression Model with Measurement Errors

“…as N(0, σ 2 µ ), N(0, σ 2 ε ) and N(0 p , Σ δ ), respectively, 0 p is the p × 1 vector of zeros and σ 2 µ ≥ 0, σ 2 ε > 0, Σ δ is known. The information of Σ δ may be obtained by replicating and observing ω ij ; the details can be found in Lin and Carroll [2], Carroll et al [3], Li et al [4], Dong et al [5] and Guo et al [6]. If the measurement errors do not exist, model (1) has been discussed by refs.…”