ESD(Exponential Standard Deviation) Band centered at Exponential Moving Average

Exponential Moving Average Model in Parallel Speech Recognition Training

As training data rapid growth, large-scale parallel training with multi-GPUs cluster is widely applied in the neural network model learning currently. We present a new approach that applies exponential moving average method in large-scale parallel training of neural network model. It is a non-interference strategy that the exponential moving average model is not broadcasted to distributed worke...

A Moving Average Exponential Point Process ( Ema 1 )

A construction is given for a stationary sequence of random variables {X.} which have exponential marginal distributions and are random linear combinations of order one of an i.i.d. exponential sequence {e.}. The Joint and trivariate exponential distributions of X.,, X. and X... are studied, as well as the Intensity function, point spectrum and variance time curve for the point process which ha...

comparison of estimators of standard deviation, mean moving range and median moving range to estimate process standard deviation using autocorrelated data

independence of observations is a fundamental assumption in designing an x chart for individual observations. but unfortunately, the assumption of existence of independent data is not even approximately satisfied. in fact, there is one kind of autocorrelation in the data. this paper demonstrates, through simulating an ar(1) process, that the autocorrelation in the data has deep effect on x char...

Statistics at square one. III--Standard deviation.

Moving Average Processes with Infinite Variance

The sample autocorrelation function (acf) of a stationary process has played a central statistical role in traditional time series analysis, where the assumption is made that the marginal distribution has a second moment. Now, the classical methods based on acf are not applicable in heavy tailed modeling. Using the codifference function as dependence measure for such processes be shown it be as...