How to find the conditional CDF based on observed data in R [on hold]
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If we have two samples (generally their distribution is not known),say $Xsim N(0,1)$, $Y|Xsim N(X,X^2/2)$. Can we recover the conditional CDF of $Y|X$ based on the observed samples in R?
n=1000
x=rnorm(n)
y=rnorm(n,x,x^2/2)
r distributions conditional-probability cdf
asked Apr 19 at 4:43
J.MikeJ.Mike
1415
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put on hold as off-topic by Nick Cox, Peter Flom♦ Apr 19 at 11:26
This question appears to be off-topic. The users who voted to close gave this specific reason:
- "This question appears to be off-topic because EITHER it is not about statistics, machine learning, data analysis, data mining, or data visualization, OR it focuses on programming, debugging, or performing routine operations within a statistical computing platform. If the latter, you could try the support links we maintain." – Nick Cox, Peter Flom
If this question can be reworded to fit the rules in the help center, please edit the question.
add a comment |
$begingroup$
If we have two samples (generally their distribution is not known),say $Xsim N(0,1)$, $Y|Xsim N(X,X^2/2)$. Can we recover the conditional CDF of $Y|X$ based on the observed samples in R?
n=1000
x=rnorm(n)
y=rnorm(n,x,x^2/2)
r distributions conditional-probability cdf
asked Apr 19 at 4:43
J.MikeJ.Mike
1415
$endgroup$
put on hold as off-topic by Nick Cox, Peter Flom♦ Apr 19 at 11:26
This question appears to be off-topic. The users who voted to close gave this specific reason:
- "This question appears to be off-topic because EITHER it is not about statistics, machine learning, data analysis, data mining, or data visualization, OR it focuses on programming, debugging, or performing routine operations within a statistical computing platform. If the latter, you could try the support links we maintain." – Nick Cox, Peter Flom
If this question can be reworded to fit the rules in the help center, please edit the question.
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conditional PDF and CDF can be estimated nonparametrically. There is supposedly at least one package available for those purposes.
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– Gary Moore
Apr 19 at 5:05
$begingroup$
Thank you for your kind comment! Do you have the reference for the method of nonparametric estimation and the R package?
$endgroup$
– J.Mike
Apr 19 at 5:26
add a comment |
$begingroup$
If we have two samples (generally their distribution is not known),say $Xsim N(0,1)$, $Y|Xsim N(X,X^2/2)$. Can we recover the conditional CDF of $Y|X$ based on the observed samples in R?
n=1000
x=rnorm(n)
y=rnorm(n,x,x^2/2)
r distributions conditional-probability cdf
asked Apr 19 at 4:43
J.MikeJ.Mike
1415
$endgroup$
If we have two samples (generally their distribution is not known),say $Xsim N(0,1)$, $Y|Xsim N(X,X^2/2)$. Can we recover the conditional CDF of $Y|X$ based on the observed samples in R?
n=1000
x=rnorm(n)
y=rnorm(n,x,x^2/2)
r distributions conditional-probability cdf
r distributions conditional-probability cdf
asked Apr 19 at 4:43
J.MikeJ.Mike
1415
asked Apr 19 at 4:43
J.MikeJ.Mike
1415
asked Apr 19 at 4:43
J.MikeJ.Mike
1415
asked Apr 19 at 4:43
J.MikeJ.Mike
1415
asked Apr 19 at 4:43
J.MikeJ.Mike
1415
1415
put on hold as off-topic by Nick Cox, Peter Flom♦ Apr 19 at 11:26
This question appears to be off-topic. The users who voted to close gave this specific reason:
- "This question appears to be off-topic because EITHER it is not about statistics, machine learning, data analysis, data mining, or data visualization, OR it focuses on programming, debugging, or performing routine operations within a statistical computing platform. If the latter, you could try the support links we maintain." – Nick Cox, Peter Flom
If this question can be reworded to fit the rules in the help center, please edit the question.
put on hold as off-topic by Nick Cox, Peter Flom♦ Apr 19 at 11:26
This question appears to be off-topic. The users who voted to close gave this specific reason:
- "This question appears to be off-topic because EITHER it is not about statistics, machine learning, data analysis, data mining, or data visualization, OR it focuses on programming, debugging, or performing routine operations within a statistical computing platform. If the latter, you could try the support links we maintain." – Nick Cox, Peter Flom
If this question can be reworded to fit the rules in the help center, please edit the question.
$begingroup$
conditional PDF and CDF can be estimated nonparametrically. There is supposedly at least one package available for those purposes.
$endgroup$
– Gary Moore
Apr 19 at 5:05
$begingroup$
Thank you for your kind comment! Do you have the reference for the method of nonparametric estimation and the R package?
$endgroup$
– J.Mike
Apr 19 at 5:26
add a comment |
$begingroup$
conditional PDF and CDF can be estimated nonparametrically. There is supposedly at least one package available for those purposes.
$endgroup$
– Gary Moore
Apr 19 at 5:05
$begingroup$
Thank you for your kind comment! Do you have the reference for the method of nonparametric estimation and the R package?
$endgroup$
– J.Mike
Apr 19 at 5:26
$begingroup$
conditional PDF and CDF can be estimated nonparametrically. There is supposedly at least one package available for those purposes.
$endgroup$
– Gary Moore
Apr 19 at 5:05
$begingroup$
conditional PDF and CDF can be estimated nonparametrically. There is supposedly at least one package available for those purposes.
$endgroup$
– Gary Moore
Apr 19 at 5:05
$begingroup$
Thank you for your kind comment! Do you have the reference for the method of nonparametric estimation and the R package?
$endgroup$
– J.Mike
Apr 19 at 5:26
$begingroup$
Thank you for your kind comment! Do you have the reference for the method of nonparametric estimation and the R package?
$endgroup$
– J.Mike
Apr 19 at 5:26
add a comment |
2 Answers
2
active
oldest
votes
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You can't determine the CDF from samples, but you can easily get an empirical estimate:
set.seed(1359)
n <- 1000
x <- rnorm(n)
y <- rnorm(n, x, x^2/2)
LM <- lm(y ~ 0 + x) # no intercept because you know this, though usually you won't
coef(LM)
Gives me $hat{beta} = 1.076$, or $hat{y} = 1.076 cdot x + epsilon$ (about $1times x$, as you specified).
Similarly, you can get an empirical estimate of the standard deviation you supplied with sd(resid(LM)).
If you don't know anything about their distributions, you could try a non-parametric approach.
answered Apr 19 at 5:04
Frans RodenburgFrans Rodenburg
3,7501529
$endgroup$
$begingroup$
Thank you for your answer! Do you have the reference for the nonparametric approach?
$endgroup$
– J.Mike
Apr 19 at 5:28
add a comment |
$begingroup$
Finding the conditional distribution of a variable $Y$ conditional on another observed variable $X$ is the entire subject matter of regression analysis (construed in its wide sense to include linear and nonlinear regression models, GLMs, GLMMs, etc.). This is a huge subject and a core part of statistical education. If you would like to learn more about it, I would recommend starting with some material on linear regression analysis, and then building up from there.
answered Apr 19 at 10:13
BenBen
29.1k234130
$endgroup$
add a comment |
2 Answers
2
active
oldest
votes
2 Answers
2
active
oldest
votes
active
oldest
votes
active
oldest
votes
$begingroup$
You can't determine the CDF from samples, but you can easily get an empirical estimate:
set.seed(1359)
n <- 1000
x <- rnorm(n)
y <- rnorm(n, x, x^2/2)
LM <- lm(y ~ 0 + x) # no intercept because you know this, though usually you won't
coef(LM)
Gives me $hat{beta} = 1.076$, or $hat{y} = 1.076 cdot x + epsilon$ (about $1times x$, as you specified).
Similarly, you can get an empirical estimate of the standard deviation you supplied with sd(resid(LM)).
If you don't know anything about their distributions, you could try a non-parametric approach.
answered Apr 19 at 5:04
Frans RodenburgFrans Rodenburg
3,7501529
$endgroup$
$begingroup$
Thank you for your answer! Do you have the reference for the nonparametric approach?
$endgroup$
– J.Mike
Apr 19 at 5:28
add a comment |
$begingroup$
You can't determine the CDF from samples, but you can easily get an empirical estimate:
set.seed(1359)
n <- 1000
x <- rnorm(n)
y <- rnorm(n, x, x^2/2)
LM <- lm(y ~ 0 + x) # no intercept because you know this, though usually you won't
coef(LM)
Gives me $hat{beta} = 1.076$, or $hat{y} = 1.076 cdot x + epsilon$ (about $1times x$, as you specified).
Similarly, you can get an empirical estimate of the standard deviation you supplied with sd(resid(LM)).
If you don't know anything about their distributions, you could try a non-parametric approach.
answered Apr 19 at 5:04
Frans RodenburgFrans Rodenburg
3,7501529
$endgroup$
$begingroup$
Thank you for your answer! Do you have the reference for the nonparametric approach?
$endgroup$
– J.Mike
Apr 19 at 5:28
add a comment |
$begingroup$
You can't determine the CDF from samples, but you can easily get an empirical estimate:
set.seed(1359)
n <- 1000
x <- rnorm(n)
y <- rnorm(n, x, x^2/2)
LM <- lm(y ~ 0 + x) # no intercept because you know this, though usually you won't
coef(LM)
Gives me $hat{beta} = 1.076$, or $hat{y} = 1.076 cdot x + epsilon$ (about $1times x$, as you specified).
Similarly, you can get an empirical estimate of the standard deviation you supplied with sd(resid(LM)).
If you don't know anything about their distributions, you could try a non-parametric approach.
answered Apr 19 at 5:04
Frans RodenburgFrans Rodenburg
3,7501529
$endgroup$
You can't determine the CDF from samples, but you can easily get an empirical estimate:
set.seed(1359)
n <- 1000
x <- rnorm(n)
y <- rnorm(n, x, x^2/2)
LM <- lm(y ~ 0 + x) # no intercept because you know this, though usually you won't
coef(LM)
Gives me $hat{beta} = 1.076$, or $hat{y} = 1.076 cdot x + epsilon$ (about $1times x$, as you specified).
Similarly, you can get an empirical estimate of the standard deviation you supplied with sd(resid(LM)).
If you don't know anything about their distributions, you could try a non-parametric approach.
answered Apr 19 at 5:04
Frans RodenburgFrans Rodenburg
3,7501529
answered Apr 19 at 5:04
Frans RodenburgFrans Rodenburg
3,7501529
answered Apr 19 at 5:04
Frans RodenburgFrans Rodenburg
3,7501529
answered Apr 19 at 5:04
Frans RodenburgFrans Rodenburg
3,7501529
3,7501529
$begingroup$
Thank you for your answer! Do you have the reference for the nonparametric approach?
$endgroup$
– J.Mike
Apr 19 at 5:28
add a comment |
$begingroup$
Thank you for your answer! Do you have the reference for the nonparametric approach?
$endgroup$
– J.Mike
Apr 19 at 5:28
$begingroup$
Thank you for your answer! Do you have the reference for the nonparametric approach?
$endgroup$
– J.Mike
Apr 19 at 5:28
$begingroup$
Thank you for your answer! Do you have the reference for the nonparametric approach?
$endgroup$
– J.Mike
Apr 19 at 5:28
add a comment |
$begingroup$
Finding the conditional distribution of a variable $Y$ conditional on another observed variable $X$ is the entire subject matter of regression analysis (construed in its wide sense to include linear and nonlinear regression models, GLMs, GLMMs, etc.). This is a huge subject and a core part of statistical education. If you would like to learn more about it, I would recommend starting with some material on linear regression analysis, and then building up from there.
answered Apr 19 at 10:13
BenBen
29.1k234130
$endgroup$
add a comment |
$begingroup$
Finding the conditional distribution of a variable $Y$ conditional on another observed variable $X$ is the entire subject matter of regression analysis (construed in its wide sense to include linear and nonlinear regression models, GLMs, GLMMs, etc.). This is a huge subject and a core part of statistical education. If you would like to learn more about it, I would recommend starting with some material on linear regression analysis, and then building up from there.
answered Apr 19 at 10:13
BenBen
29.1k234130
$endgroup$
add a comment |
$begingroup$
Finding the conditional distribution of a variable $Y$ conditional on another observed variable $X$ is the entire subject matter of regression analysis (construed in its wide sense to include linear and nonlinear regression models, GLMs, GLMMs, etc.). This is a huge subject and a core part of statistical education. If you would like to learn more about it, I would recommend starting with some material on linear regression analysis, and then building up from there.
answered Apr 19 at 10:13
BenBen
29.1k234130
$endgroup$
Finding the conditional distribution of a variable $Y$ conditional on another observed variable $X$ is the entire subject matter of regression analysis (construed in its wide sense to include linear and nonlinear regression models, GLMs, GLMMs, etc.). This is a huge subject and a core part of statistical education. If you would like to learn more about it, I would recommend starting with some material on linear regression analysis, and then building up from there.
answered Apr 19 at 10:13
BenBen
29.1k234130
answered Apr 19 at 10:13
BenBen
29.1k234130
answered Apr 19 at 10:13
BenBen
29.1k234130
answered Apr 19 at 10:13
BenBen
29.1k234130
29.1k234130
add a comment |
add a comment |
$begingroup$
conditional PDF and CDF can be estimated nonparametrically. There is supposedly at least one package available for those purposes.
$endgroup$
– Gary Moore
Apr 19 at 5:05
$begingroup$
Thank you for your kind comment! Do you have the reference for the method of nonparametric estimation and the R package?
$endgroup$
– J.Mike
Apr 19 at 5:26