For each parameter i.e. latency, starting level, gain, overshoot and maximum velocity, and each step change, i.e. + 0.5 D and −0.5 D, each correction condition was compared to the step only (SO) condition using a paired t-test.
Therefore, in this paper, a maximum likelihood estimator of a step change in the parameters of auto-correlated simple linear regression profiles is first proposed in which the auto-correlation structure between observations in each profile is assumed to follow a first-order auto-regressive, AR(1), model.
In this paper, a maximum likelihood estimator of a step change in the parameters of auto-correlated simple linear profiles was derived, in which the auto-correlation structure between observations in each profile was assumed to be a first-order auto-regressive model.
This finding reveals an important potential mechanism underlying developmental improvement in VWM performance, and one which in developmental terms may be more plausible than positing a step change in maximum VWM capacity.
Then, a maximum likelihood estimator (MLE) of a step change in the parameters of the transformed model is derived and three separate EWMA control charts are used to monitor the parameters of the profile.
We also define a maximum step transmission ratio as the maximum possible change in the reduction ratio between HS and HF.
Nedumaran et al. (2000) developed maximum likelihood estimator (MLE) approach to estimate step change point in the multivariate normal process mean.
Then, to estimate the process change point, they developed the maximum likelihood estimators for both linear trend and step change disturbances.
Niaki and Khedmati (2012) proposed a new method to derive the maximum likelihood estimator of the time of a step change in the mean vector of multivariate Poisson processes.
For Poisson processes, maximum likelihood (ML) methods were applied to estimate the true time of a step change (Samuel and Pignatiello 1998; Perry 2004) and a linear trend (Perry et al. 2006) in the Poisson rate.
