Weakly stationary Gaussian process?

In summary, X is defined as the square of Y(t), where Y(t) is a zero mean Gaussian process with a correlation function R_YY = exp(-λ|τ|). To check if X is weakly stationary, the mean and variance were calculated, with the mean being zero and the variance being 1. Since Y(t) is a Gaussian process, the joint distribution of Y(t1) and Y(t2) is determined by R_YY(t1-t2), which is a function of tau=t1-t2. Therefore, everything about Y(t1) and Y(t2) is at most a function of tau, including the mean and variance. To find the auto-correlation of X, it is necessary
  • #1
aliirmak
1
0
X = (Y(t))^2 where Y(t) is zero mean Gaussian process and correlation function R_YY = exp(-λ|τ|)
i want to check if X is weakly stationary.So i guess for the first part, i checked if mean is constant
σ^2=R_YY = exp(-λ|0|) = 1
E(X^2) = μ^2+ σ^2 = 1 since μ is zero and σ = 1

I wanted to check if auto-correlation is a function of τ. But I am pretty badly stuck at finding auto-correlation of X. How should i proceed?
 
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  • #2
Since Y(t) is Gaussian process, the joint distribution of Y(t1) and Y(t2) are totally determined by R_YY(t1-t2), which is a function of tau=t1-t2, therefore everything about Y(t1) and Y(t2) should at most be a function of tau (the mean and variance are constant functions of tau)
 

1. What is a weakly stationary Gaussian process?

A weakly stationary Gaussian process is a type of mathematical model that is used to describe random phenomena. It is characterized by two properties: weak stationarity and Gaussianity. Weak stationarity means that the mean and variance of the process do not change over time, while Gaussianity means that the distribution of the process follows a normal or Gaussian distribution.

2. How is a weakly stationary Gaussian process different from a strictly stationary process?

A strictly stationary process is one in which all moments of the process, including higher order moments, are invariant over time. In contrast, a weakly stationary process only requires the first two moments (mean and variance) to be invariant. This means that a weakly stationary process may exhibit some degree of correlation between observations, while a strictly stationary process does not.

3. What are some applications of weakly stationary Gaussian processes?

Weakly stationary Gaussian processes are commonly used in fields such as signal processing, time series analysis, and machine learning. They are useful for modeling and predicting random phenomena that exhibit some level of correlation between observations, such as stock prices, weather patterns, and biological data.

4. How do you test for weak stationarity in a Gaussian process?

There are several statistical tests that can be used to determine whether a process is weakly stationary. These include the Augmented Dickey-Fuller test and the Kwiatkowski-Phillips-Schmidt-Shin test. These tests look for specific patterns in the data, such as a constant mean and autocorrelation structure, to determine whether the process is weakly stationary or not.

5. Can a non-Gaussian process be weakly stationary?

Yes, a process does not have to follow a normal or Gaussian distribution to be weakly stationary. However, the Gaussianity assumption is often made for ease of mathematical analysis. In practice, it is important to consider the distribution of the data when applying weak stationarity tests and interpreting the results.

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