MATLAB: How to apply Maximum likelihood estimation technique to characterize the amplitude of noise in a given time series

maximum likelihood estimation

I have got the power spectral density of a time series. Now I want to know the amount of noises present in the time series using MLE. Please help me out..

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Im just guessing here, but since the least square fit of your time function (inverse fourier?) is a maximum likelihood estimator of your signal model (assuming gaussian noise), then the sum of residuals should be a measure for the amount of noise?

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MATLAB: I think no one will be able to answer this………

Your question is ill-defined:

You have not presented enough info.

1. Is the middle column the noisy signal?

2. Is the noise supposed to be

 a. additive?
 b. zero-mean?
 c. constant variance?
 d. Gaussian distributed?

If so, how was the variance measured? In particular, how many points were used to estimate each tabulated value?

MATLAB: What will be the sampling frequency of the following time series data?

I am not sure I am understanding your question very well, but it seems you have a sample every 0.00274 units of time. You have to convert 0.00274 into seconds to give x seconds. The sampling frecuency would be fs=1/x Hz

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MATLAB: I think no one will be able to answer this………

MATLAB: What will be the sampling frequency of the following time series data?

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