Distribution function of observations in the errors-in-variables model The Next CEO of Stack Overflow2019 Community Moderator Electionregression to model exponential distributionHow to model a Bimodal distribution of target variableCost Function for evaluating a Regression ModelLocally Weighted Regression (Loess) - Robustifying iterationsRegression - random error termReducing the dependency between variablesHow to model & predict user activity/presence time in a websiteDoes the choice of error function impact the model parametrs?Can I use Linear Regression to model a nonlinear function?How to model non-linear demand function?
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Distribution function of observations in the errors-in-variables model
The Next CEO of Stack Overflow2019 Community Moderator Electionregression to model exponential distributionHow to model a Bimodal distribution of target variableCost Function for evaluating a Regression ModelLocally Weighted Regression (Loess) - Robustifying iterationsRegression - random error termReducing the dependency between variablesHow to model & predict user activity/presence time in a websiteDoes the choice of error function impact the model parametrs?Can I use Linear Regression to model a nonlinear function?How to model non-linear demand function?
$begingroup$
Consider the linear relation $mathbfy = mathbfHmathbfx + mathbfz$. The vector $mathbfx$ is unknown, and a noisy version of the system matrix $mathbfH$, i.e., $tildemathbfH = mathbfH + mathbfE$ is available. The noise term $mathbfz$ and the rows of $mathbfE$ are i.i.d $mathcalN(mathbf0, sigma^2mathbfI)$. Given $mathbfy$ and $tildemathbfH$, how to characterize the distribution of $mathbfy$? Assume that the vectors $mathbfy$, $mathbfx$ and $mathbfz$ are $N times 1$-dimensional, while the matrix $mathbfH$ is $N times p$.
Thanks for the help!
regression
asked 2 days ago
NandaNanda
11
New contributor
Nanda is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
$endgroup$
add a comment |
$begingroup$
Consider the linear relation $mathbfy = mathbfHmathbfx + mathbfz$. The vector $mathbfx$ is unknown, and a noisy version of the system matrix $mathbfH$, i.e., $tildemathbfH = mathbfH + mathbfE$ is available. The noise term $mathbfz$ and the rows of $mathbfE$ are i.i.d $mathcalN(mathbf0, sigma^2mathbfI)$. Given $mathbfy$ and $tildemathbfH$, how to characterize the distribution of $mathbfy$? Assume that the vectors $mathbfy$, $mathbfx$ and $mathbfz$ are $N times 1$-dimensional, while the matrix $mathbfH$ is $N times p$.
Thanks for the help!
regression
asked 2 days ago
NandaNanda
11
New contributor
Nanda is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
$endgroup$
$begingroup$
This sounds like a homework question. What have you tried so far? What don't you understand?
$endgroup$
– ukemi
2 days ago
$begingroup$
Not a homework problem. I was working out the details of a proof in a research paper when I encountered this setup. I think $mathbfy$ will follow a compound distribution, however, I am still unable to obtain a complete characterization.
$endgroup$
– Nanda
2 days ago
$begingroup$
I think you should try this question in the cross-validated section of stack-Exchange at stats.stackexchange.com
$endgroup$
– Mark.F
2 days ago
add a comment |
$begingroup$
Consider the linear relation $mathbfy = mathbfHmathbfx + mathbfz$. The vector $mathbfx$ is unknown, and a noisy version of the system matrix $mathbfH$, i.e., $tildemathbfH = mathbfH + mathbfE$ is available. The noise term $mathbfz$ and the rows of $mathbfE$ are i.i.d $mathcalN(mathbf0, sigma^2mathbfI)$. Given $mathbfy$ and $tildemathbfH$, how to characterize the distribution of $mathbfy$? Assume that the vectors $mathbfy$, $mathbfx$ and $mathbfz$ are $N times 1$-dimensional, while the matrix $mathbfH$ is $N times p$.
Thanks for the help!
regression
asked 2 days ago
NandaNanda
11
New contributor
Nanda is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
$endgroup$
Consider the linear relation $mathbfy = mathbfHmathbfx + mathbfz$. The vector $mathbfx$ is unknown, and a noisy version of the system matrix $mathbfH$, i.e., $tildemathbfH = mathbfH + mathbfE$ is available. The noise term $mathbfz$ and the rows of $mathbfE$ are i.i.d $mathcalN(mathbf0, sigma^2mathbfI)$. Given $mathbfy$ and $tildemathbfH$, how to characterize the distribution of $mathbfy$? Assume that the vectors $mathbfy$, $mathbfx$ and $mathbfz$ are $N times 1$-dimensional, while the matrix $mathbfH$ is $N times p$.
Thanks for the help!
regression
regression
asked 2 days ago
NandaNanda
11
New contributor
Nanda is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
asked 2 days ago
NandaNanda
11
New contributor
Nanda is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
asked 2 days ago
NandaNanda
11
New contributor
Nanda is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
asked 2 days ago
NandaNanda
11
asked 2 days ago
NandaNanda
11
11
New contributor
Nanda is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
New contributor
Nanda is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
Nanda is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
$begingroup$
This sounds like a homework question. What have you tried so far? What don't you understand?
$endgroup$
– ukemi
2 days ago
$begingroup$
Not a homework problem. I was working out the details of a proof in a research paper when I encountered this setup. I think $mathbfy$ will follow a compound distribution, however, I am still unable to obtain a complete characterization.
$endgroup$
– Nanda
2 days ago
$begingroup$
I think you should try this question in the cross-validated section of stack-Exchange at stats.stackexchange.com
$endgroup$
– Mark.F
2 days ago
add a comment |
$begingroup$
This sounds like a homework question. What have you tried so far? What don't you understand?
$endgroup$
– ukemi
2 days ago
$begingroup$
Not a homework problem. I was working out the details of a proof in a research paper when I encountered this setup. I think $mathbfy$ will follow a compound distribution, however, I am still unable to obtain a complete characterization.
$endgroup$
– Nanda
2 days ago
$begingroup$
I think you should try this question in the cross-validated section of stack-Exchange at stats.stackexchange.com
$endgroup$
– Mark.F
2 days ago
$begingroup$
This sounds like a homework question. What have you tried so far? What don't you understand?
$endgroup$
– ukemi
2 days ago
$begingroup$
This sounds like a homework question. What have you tried so far? What don't you understand?
$endgroup$
– ukemi
2 days ago
$begingroup$
Not a homework problem. I was working out the details of a proof in a research paper when I encountered this setup. I think $mathbfy$ will follow a compound distribution, however, I am still unable to obtain a complete characterization.
$endgroup$
– Nanda
2 days ago
$begingroup$
Not a homework problem. I was working out the details of a proof in a research paper when I encountered this setup. I think $mathbfy$ will follow a compound distribution, however, I am still unable to obtain a complete characterization.
$endgroup$
– Nanda
2 days ago
$begingroup$
I think you should try this question in the cross-validated section of stack-Exchange at stats.stackexchange.com
$endgroup$
– Mark.F
2 days ago
$begingroup$
I think you should try this question in the cross-validated section of stack-Exchange at stats.stackexchange.com
$endgroup$
– Mark.F
2 days ago
add a comment |
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Nanda is a new contributor. Be nice, and check out our Code of Conduct.
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$begingroup$
This sounds like a homework question. What have you tried so far? What don't you understand?
$endgroup$
– ukemi
2 days ago
$begingroup$
Not a homework problem. I was working out the details of a proof in a research paper when I encountered this setup. I think $mathbfy$ will follow a compound distribution, however, I am still unable to obtain a complete characterization.
$endgroup$
– Nanda
2 days ago
$begingroup$
I think you should try this question in the cross-validated section of stack-Exchange at stats.stackexchange.com
$endgroup$
– Mark.F
2 days ago