The position of the target word was varied from early to later word positions across the sentences and strings of words, and we found that word-monitoring response times (RTs) became increasingly faster at later word positions in both normal and AP but not random strings of words.
The models are developed based on experimental results which revealed that both normal and frictional forces were essentially random non-Gaussian processes.
iCluster + runs under both normal model (random initialization of penalty parameter for better model) and simple model (fixed penalty parameter).
Unlike in the situation with fixed n and γ→∞, for n→∞ we find fairly good tail behavior of LSE for normal F, for both fixed and random designs, even under heavy-tailed distribution for Xn.
Word monitoring response times become increasingly faster at later word positions in both normal prose and anomalous prose, but not random word order (Marslen-Wilson and Tyler, 1980).
In this study, both a normal and a non-normal distribution for the random variables involved are analyzed, focusing in the latter case on the Weibull distribution.
For joint clustering algorithms, we find that when the noise level is low, ASModel and normal random walk are competitive with each other.
Watermark is chosen as a zero mean and normal distributed random sequence.
For all explanatory variables, a fixed effect and a Normal random effect was assumed.
