Auto-covariance of a Wiener process of a function

In summary, the auto-covariance function for the process {X(t); t >= 0} is C_X (t,τ) = α*e^(-c(2t+τ))*min{e^(2ct),e^(2c(t+τ))}. The process is not stationary in the weak sense since the auto-correlation function varies with t.
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Homework Statement



Let {W(t); t >= 0} be a Wiener process. Determine the auto-covariance function for the process {X(t); t >= 0} defined by X(t) = e^(-ct) * W(e^(2ct)) for all t >= 0, where c > 0 is a constant.

Is {X(t); t >= 0} stationary in the wide sense?

Homework Equations



Is this possibly in the right direction?

The Attempt at a Solution



C_X (t,τ) = Cov(X(t), X(t+τ)) = Cov(e^(-ct) * W(e^(2ct)),e^(-c(t+τ)) * W(e^(2c(t+τ)))) = e^(-c(2t+τ))*Cov(W(e^(2ct)), W(e^(2c(t+τ)))) = α*e^(-c(2t+τ))*min{e^(2ct),e^(2c(t+τ))}.

The process is not stationary in the weak sense since the auto-correlation function (equal to the auto-covariance here?) varies with t.
 
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I have solved the problem, no need to reply to this.
 

Related to Auto-covariance of a Wiener process of a function

What is the definition of auto-covariance?

The auto-covariance of a function is a measure of the linear relationship between the function's values at different points in time. It is calculated by taking the average of the product of the function's values at two different time points, with one of the time points being shifted by a certain lag.

What is a Wiener process?

A Wiener process, also known as a Brownian motion, is a continuous stochastic process that describes the random movement of particles in a fluid or gas. It is named after mathematician Norbert Wiener and is widely used in physics, finance, and other fields.

How is auto-covariance calculated for a Wiener process of a function?

In order to calculate the auto-covariance of a Wiener process of a function, the function's values at different time points need to be multiplied together and then averaged over all possible time points. This can be represented mathematically as Cov(X(t), X(t+lag)) = E[X(t)X(t+lag)].

What is the significance of auto-covariance in a Wiener process of a function?

The auto-covariance of a Wiener process of a function is important because it provides a measure of how closely the function's values at different time points are related to each other. It can also be used to calculate other statistical properties, such as the variance and autocorrelation, which are useful in analyzing and modeling time series data.

Can auto-covariance be negative?

Yes, the auto-covariance of a function can be either positive or negative. A positive auto-covariance indicates a positive linear relationship between the function's values at different time points, while a negative auto-covariance indicates a negative linear relationship. A zero auto-covariance would indicate no linear relationship.

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