There's at least one package that can do zero-inflated gamma regression
(Rfast2::zigamma). I'm not sure it's ML, though.
On Thu, Jan 19, 2023 at 10:17 AM Jeff Newmiller
wrote:
> Beware of adding a constant... the magnitude of the constant used can have
> an outsized impact on the answer
Another situation for the presence of 0 is about dosage when
concentration is below the detection limit. It is not necessary to
discretize the data. We propose a method here:
Salvat-Leal I, Cortés-Gómez AA, Romero D, Girondot M (2022) New method
for imputation of unquantifiable values using
Beware of adding a constant... the magnitude of the constant used can have an
outsized impact on the answer obtained. See e.g.
https://gist.github.com/jdnewmil/99301a88de702ad2fcbaef33326b08b4
On January 19, 2023 3:49:29 AM PST, peter dalgaard wrote:
>Not necessarily homework, Bert. There's a
Not necessarily homework, Bert. There's a generic issue with MLE and rounded
data, in that gamma densities may be 0 at the boundary but small numbers are
represented as 0, making the log-likelihood -Inf.
The cleanest way out is to switch to a discretized distribution in the
likelihood, so
Is this homework? This list has a no-homework policy.
-- Bert
On Tue, Jan 10, 2023 at 8:13 AM Nyasha wrote:
>
> Please how can one go about this one? I don't know how to go about it.
>
> [[alternative HTML version deleted]]
>
> __
>
Please how can one go about this one? I don't know how to go about it.
[[alternative HTML version deleted]]
__
R-help@r-project.org mailing list -- To UNSUBSCRIBE and more, see
https://stat.ethz.ch/mailman/listinfo/r-help
PLEASE do read the
Hi R-Users,
I am trying to estimate 95%-le VaR (Value-at-Risk) of a portfolio using
Extreme Value Theory. In particular, I'll use the Frechet distribution
(heavy left tail),
I have data on percentage returns ( R_t) for T = 5000 past dates. This data
has been divided into g = 50 non-overlapping
Dear List,
This problem is more a statistic one than a R one.
Any one can recommend me some references website or online paper on
maximum likelihood estimation?I'm now working on that,while still
doubt how to prove that the estimated parameters are normal
distributed.
Thanks for your time and
Check out Casella and Berger's Statistical Inference. ISBN 978-81-315-0394-2
or http://en.wikipedia.org/wiki/Maximum_likelihood as an online reference.
--Mark J. Lamias
From: Ning Cheng wakinchaue...@gmail.com
To: r-help@r-project.org
Sent: Sunday, February 27, 2011 3:19 PM
Subject: [R] MLE
I am partial to Gary King's book:
Unifying Political Methodology: The Likelihood Theory of Statistical Inference
(University of Michigan Press, 1998)
Cheers
David Cross
d.cr...@tcu.edu
www.davidcross.us
On Feb 27, 2011, at 2:19 PM, Ning Cheng wrote:
Dear List,
This problem is more a
INTRODUCTION TO THE PROBLEM
I am trying to fit a distribution to a dataset. The distribution that I
am currently considering is the (3-parameter) Singh-Maddala (Burr)
distribution. The final model will fix the mean of the distribution to a
given value and estimate the remaining parameters
Greetings, all
I am having difficulty getting the fitdistr() function to return without
an error on my data. Specifically, what I'm trying to do is get a
parameter estimation for fracture intensity data in a well / borehole.
Lower bound is 0 (no fractures in the selected data interval), and upper
Fox, Aaron wrote:
Greetings, all
I am having difficulty getting the fitdistr() function to return without
an error on my data. Specifically, what I'm trying to do is get a
parameter estimation for fracture intensity data in a well / borehole.
Lower bound is 0 (no fractures in the selected data
Fox, Aaron Afox at golder.com writes:
Greetings, all
I am having difficulty getting the fitdistr() function to return without
an error on my data. Specifically, what I'm trying to do is get a
parameter estimation for fracture intensity data in a well / borehole.
Lower bound is 0 (no
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