Displaying 6 results from an estimated 6 matches for "mvnk".
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mink
2018 Mar 04
2
lmrob gives NA coefficients
...,?x) where diag(?x)=1,
off-diag(?x)= ?X= 0.15 for low interdependency and ?x= 0.70 for high
interdependency. Where ?x is correlation between explanatory variables. We
chose two sample size 25 for small sample and 100 for large sample. The
specific error in equations ?i, i=1,2,?..,n, we generated by MVNk=3 (0,
??), ?? the variance covariance matrix of errors, diag(??)= 1,
off-diag(??)= ??= 0.15. To investigate the robustness of the estimators
against outliers, we chosen different percentages of outliers ( 20%, 45%).
We choose shrink parameter in (12) by minimize the new robust Cross
Validation (CVM...
2018 Mar 04
0
lmrob gives NA coefficients
...1,
> off-diag(?x)= ?X= 0.15 for low interdependency and ?x= 0.70 for high
> interdependency. Where ?x is correlation between explanatory variables. We
> chose two sample size 25 for small sample and 100 for large sample. The
> specific error in equations ?i, i=1,2,?..,n, we generated by MVNk=3 (0,
> ??), ?? the variance covariance matrix of errors, diag(??)= 1,
> off-diag(??)= ??= 0.15. To investigate the robustness of the estimators
> against outliers, we chosen different percentages of outliers ( 20%, 45%).
> We choose shrink parameter in (12) by minimize the new robust C...
2018 Mar 04
1
lmrob gives NA coefficients
...diag(?x)= ?X= 0.15 for low interdependency and ?x= 0.70 for high
>> interdependency. Where ?x is correlation between explanatory variables. We
>> chose two sample size 25 for small sample and 100 for large sample. The
>> specific error in equations ?i, i=1,2,?..,n, we generated by MVNk=3 (0,
>> ??), ?? the variance covariance matrix of errors, diag(??)= 1,
>> off-diag(??)= ??= 0.15. To investigate the robustness of the estimators
>> against outliers, we chosen different percentages of outliers ( 20%, 45%).
>> We choose shrink parameter in (12) by minimize...
2018 Mar 04
0
lmrob gives NA coefficients
...w interdependency and ?x= 0.70 for high
>>> interdependency. Where ?x is correlation between explanatory variables.
>>> We
>>> chose two sample size 25 for small sample and 100 for large sample. The
>>> specific error in equations ?i, i=1,2,?..,n, we generated by MVNk=3 (0,
>>> ??), ?? the variance covariance matrix of errors, diag(??)= 1,
>>> off-diag(??)= ??= 0.15. To investigate the robustness of the estimators
>>> against outliers, we chosen different percentages of outliers ( 20%,
>>> 45%).
>>> We choose shrink p...
2018 Mar 03
0
lmrob gives NA coefficients
> On Mar 3, 2018, at 3:04 PM, Christien Kerbert <christienkerbert at gmail.com> wrote:
>
> Dear list members,
>
> I want to perform an MM-regression. This seems an easy task using the
> function lmrob(), however, this function provides me with NA coefficients.
> My data generating process is as follows:
>
> rho <- 0.15 # low interdependency
> Sigma <-
2018 Mar 03
2
lmrob gives NA coefficients
Dear list members,
I want to perform an MM-regression. This seems an easy task using the
function lmrob(), however, this function provides me with NA coefficients.
My data generating process is as follows:
rho <- 0.15 # low interdependency
Sigma <- matrix(rho, d, d); diag(Sigma) <- 1
x.clean <- mvrnorm(n, rep(0,d), Sigma)
beta <- c(1.0, 2.0, 3.0, 4.0)
error <- rnorm(n = n,