Displaying 13 results from an estimated 13 matches for "biomet".
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2011 Feb 25
0
I have a Quick question about biometics
Hello,
I was searching online to find more info about Biometics
and I came across your information.
Can you tell me, are you still involved with Biometics?
If you are, how are things going for you?
Please let me know.
Sincerely,
Will Hammack
2009 Apr 11
2
who happenly read these two paper Mohsen Pourahmadi (biometrika1999, 2000)
http://biomet.oxfordjournals.org/cgi/reprint/86/3/677 biometrika1999
http://biomet.oxfordjournals.org/cgi/reprint/94/4/1006 biometrika2000
Hi All:
I just want to try some luck.
I am currenly working on my project,one part of my project is to
reanalysis the kenward cattle data by using the method in Mohse...
2016 Apr 27
0
New package: bridgedist (v 0.1.0)
R Users,
The d/p/q/r functions for the bridge distribution are now available in
bridgedist.
When a random intercept follows the bridge distribution, as detailed in
Wang and Louis (2003) <doi:10.1093/biomet/90.4.765
<http://dx.doi.org/10.1093/biomet/90.4.765>>, a marginalized
random-intercept logistic regression will still be a logistic regression
with marginal coefficients that are scalar multiples of the conditional
regression's coefficients.
Another way to state the result is that the...
2015 Jun 25
1
Estimating overdispersion when using glm for count and binomial data
...illustrate the
benefits of using it for sparse data.
I am happy to give more details if needed.
David Fletcher
Department of Mathematics and Statistics
University of Otago
Dunedin
New Zealand
D.J. Fletcher (2012) Estimating overdispersion when fitting a
generalized linear model to sparse data. Biometrika 99:230?237
(http://biomet.oxfordjournals.org/content/99/1/230.abstract?etoc)
2018 Jul 23
1
Suggestion for updating `p.adjust` with new method (BKY 2006)
...ng a new method to `p.adjust` ("Adjust P-values for Multiple
Comparisons",
https://stat.ethz.ch/R-manual/R-devel/library/stats/html/p.adjust.html).
This new method is published in Benjamini, Krieger, Yekutieli 2016 Adaptive
linear step-up procedures that control the false discovery rate
(Biometrika). https://doi.org/10.1093/biomet/93.3.491
This paper described multiple methods for adjusting p-values, where the "TST"
method (Definition 6) performed the best when test statistics are
positively correlated, per my interpretation. This method can be labeled as
"BKY", for t...
2017 Jul 26
3
How long to wait for process?
...there a way
to know if the process is going to produce something useful after all
this time or if it's hanging on some kind of problem?
[1]:
https://stats.stackexchange.com/questions/11109/how-to-deal-with-perfect-separation-in-logistic-regression#68917
[2]:
https://academic.oup.com/biomet/article-abstract/80/1/27/228364/Bias-reduction-of-maximum-likelihood-estimates
--
Men occasionally stumble
over the truth, but most of them
pick themselves up and hurry off
as if nothing had happened.
-- Winston Churchill
2017 Jul 27
2
How long to wait for process?
...l after all this time or if it's hanging on some kind
>> of problem?
>>
>>
>> [1]:
>> https://stats.stackexchange.com/questions/11109/how-to-deal-with-perfect-separation-in-logistic-regression#68917
>>
>> [2]:
>> https://academic.oup.com/biomet/article-abstract/80/1/27/228364/Bias-reduction-of-maximum-likelihood-estimates
>>
>>
>>
>
--
Men occasionally stumble
over the truth, but most of them
pick themselves up and hurry off
as if nothing had happened.
-- Winston Churchill
2017 Jul 27
0
How long to wait for process?
...ng to produce something useful after all
> this time or if it's hanging on some kind of problem?
>
>
> [1]:
> https://stats.stackexchange.com/questions/11109/how-to-deal-with-perfect-separation-in-logistic-regression#68917
>
> [2]:
> https://academic.oup.com/biomet/article-abstract/80/1/27/228364/Bias-reduction-of-maximum-likelihood-estimates
>
>
>
2017 Jul 27
0
How long to wait for process?
...produce something useful after all this time or if it's hanging on some kind of problem?
>>>
>>>
>>> [1]: https://stats.stackexchange.com/questions/11109/how-to-deal-with-perfect-separation-in-logistic-regression#68917
>>> [2]: https://academic.oup.com/biomet/article-abstract/80/1/27/228364/Bias-reduction-of-maximum-likelihood-estimates
>>>
>>>
>>
2017 Jul 27
1
How long to wait for process?
...ng useful after all this time or if it's hanging on some kind of problem?
>>>>
>>>>
>>>> [1]:https://stats.stackexchange.com/questions/11109/how-to-deal-with-perfect-separation-in-logistic-regression#68917
>>>> [2]:https://academic.oup.com/biomet/article-abstract/80/1/27/228364/Bias-reduction-of-maximum-likelihood-estimates
>>>>
>>>>
--
Men occasionally stumble
over the truth, but most of them
pick themselves up and hurry off
as if nothing had happened.
-- Winston Churchill
2015 Jun 26
0
Estimating overdispersion when using glm for count and binomial data
...most a couple of lines
of code. The reference below gives details regarding its asymptotic
properties, as well as simulation results that illustrate the benefits
of using it for sparse data.
D.J. Fletcher (2012) Estimating overdispersion when fitting a
generalized linear model to sparse data. Biometrika 99:230?237
(http://biomet.oxfordjournals.org/content/99/1/230.abstract?etoc)
2010 Jun 12
1
extended Kalman filter for survival data
If you mean this paper by Fahrmeir: http://biomet.oxfordjournals.org/cgi/content/abstract/81/2/317 I would recommend BayesX: http://www.stat.uni-muenchen.de/~bayesx/.
BayesX interfaces with R and estimates discrete (and continuous) time survival data with penalized regression methods.
If you are looking for a bona fide Bayesian survival analysis...
2009 Nov 04
1
Variable selection in NLME or LME4
Good morning
I am learning about NLME and LME4, using Pinheiro and Bates and other materials from Douglas Bates, but I have not seen anything on how to do variable selection sensibly in this type of model.
In OLS regression, I frequently use the lasso, but googling did not reveal a method for lasso with mixed models.
Most of the material I've seen on these packages is about models with very