The corresponding "expected parameter change" statistic might suggest increasing or decreasing y5's fixed measurement error variance, where increases or decreases should be thought of as moving up or down among latents like η3A, η3B, and η3C.
Each observation (i.e. measurement on each side) is modelled as a sample from a normal distribution with mean equal to the unknown trait value and variance equal to the degree of measurement error (σME): where i = 1..N and j = 1..K with N = number of individuals and K = number of within subject repeats.
Because the DNA methylation signature is thought to be highly correlated to the leukocyte distribution, this fits into the framework of measurement error models where using a noisy surrogate marker to test the association with a disease results in biased estimates, unless validation data can be obtained to calibrate the model and correct the bias.
Heteroscedasticity refers to a situation where measurement errors are dependent on the size of the various readings.
For example, Sun et al. described an experiment where data on the velocity of halo stars in the Milky Way are collected, and where the measurement errors are assumed to be uniformly distributed [7].
They also suggested new approaches appropriate in settings where biomarkers reflect only indirectly the important processes on the causal path to clinical disease and where biomarker measurement errors are of concern.
However, in practice, the observed data in the low-rank matrix recovery problem may be contaminated with noise, namely (b = mathrm{A}X + e), where e contains measurement errors dominated by certain normal distribution.
Where the absolute measurement errors of the electrical power ΔPmax, outside tube diameter ΔDo and active length of a tube ΔL are 10 W, 0.02 mm, and 0.2 mm, respectively.
Effects of nonshared environment, which included measurement errors, were moderate across tasks, except word reading, where they were negligible.
