Prove Zero Mean hypothesis
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I have a stochastic process for which I can compute a final outcome that is real valued. I know that the process has a relatively high variance and I is costly to obtain more data points. (data points are uncorrelated)
Is there a method to rank my hypothesis of the mean being zero based on a few hundred results?
I know from Central Limit Theorem that the sample mean would behave as a normal variable with it's mean equal to the original process mean and variance equal to the original variance divided by the number of sample points. But for the number of data point I have this would represent a very loose bound for my application.
parameter-estimation estimators
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MefiticoMefitico
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I have a stochastic process for which I can compute a final outcome that is real valued. I know that the process has a relatively high variance and I is costly to obtain more data points. (data points are uncorrelated)
Is there a method to rank my hypothesis of the mean being zero based on a few hundred results?
I know from Central Limit Theorem that the sample mean would behave as a normal variable with it's mean equal to the original process mean and variance equal to the original variance divided by the number of sample points. But for the number of data point I have this would represent a very loose bound for my application.
parameter-estimation estimators
asked 12 mins ago
MefiticoMefitico
1012
New contributor
Mefitico is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
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add a comment |
$begingroup$
I have a stochastic process for which I can compute a final outcome that is real valued. I know that the process has a relatively high variance and I is costly to obtain more data points. (data points are uncorrelated)
Is there a method to rank my hypothesis of the mean being zero based on a few hundred results?
I know from Central Limit Theorem that the sample mean would behave as a normal variable with it's mean equal to the original process mean and variance equal to the original variance divided by the number of sample points. But for the number of data point I have this would represent a very loose bound for my application.
parameter-estimation estimators
asked 12 mins ago
MefiticoMefitico
1012
New contributor
Mefitico is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
$endgroup$
I have a stochastic process for which I can compute a final outcome that is real valued. I know that the process has a relatively high variance and I is costly to obtain more data points. (data points are uncorrelated)
Is there a method to rank my hypothesis of the mean being zero based on a few hundred results?
I know from Central Limit Theorem that the sample mean would behave as a normal variable with it's mean equal to the original process mean and variance equal to the original variance divided by the number of sample points. But for the number of data point I have this would represent a very loose bound for my application.
parameter-estimation estimators
parameter-estimation estimators
asked 12 mins ago
MefiticoMefitico
1012
New contributor
Mefitico is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
asked 12 mins ago
MefiticoMefitico
1012
New contributor
Mefitico is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
asked 12 mins ago
MefiticoMefitico
1012
New contributor
Mefitico is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
asked 12 mins ago
MefiticoMefitico
1012
asked 12 mins ago
MefiticoMefitico
1012
1012
New contributor
Mefitico is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
New contributor
Mefitico is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
Mefitico is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
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