Displaying 20 results from an estimated 2000 matches similar to: "ML fit of pareto and lognormal distributions to grouped data"
2006 Nov 28
3
ML fit of gamma distribution to grouped data
Hello,
we have a set of biological cell-size data, which are only available as
frequencies of discrete size classes, because of the high effort of
manual microscopic measurements.
The lengths are approximately gamma distributed, however the shape of
the distribution is relatively variable between different samples (maybe
it's a mixture in reality).
Is there any ML fitting (or
2001 Nov 14
0
Fitting Pareto dist in a mixture
Dear all:
First, apologies for cross-posting multiplicities and for a query that is
more
analytically related than S-language related.
The bottom-line wish is:
Could you please provide and advice, references, etc on S software
approaches for
fitting a distribution with density:
p*g(x) + (1-p)*f(x)
where g(x) is the familiar lognormal 2-parameter density
and f(x) is Pareto as defined below?
2013 Mar 18
2
Fit a mixture of lognormal and normal distributions
Hello
I am trying to find an automated way of fitting a mixture of normal and log-normal distributions to data which is clearly bimodal.
Here's a simulated example:
x.1<-rnorm(6000, 2.4, 0.6)x.2<-rlnorm(10000, 1.3,0.1)X<-c(x.1, x.2)
hist(X,100,freq=FALSE, ylim=c(0,1.5))lines(density(x.1), lty=2, lwd=2)lines(density(x.2), lty=2, lwd=2)lines(density(X), lty=4)
Currently i am using
2005 Jan 31
2
ML-Fit for truncated distributions
Hello,
maybe that my Question is a "beginner"-Question, but up to now, my research
didn't bring any useful result.
I'm trying to fit a distribution (e.g. lognormal) to a given set of data
(ML-Estimation). I KNOW about my data that there is a truncation for all
data below a well known threshold. Is there an R-solution for an
ML-estimation for this kind of data-problem? As
2007 Sep 07
1
How to obtain parameters of a mixture model of two lognormal distributions
Dear List,
I have read that a lognormal mixture model having a pdf of the form
f(x)=w1*f1(x)+(1-w1)*f2(x) fits most data sets quite well, where f1
and f2 are lognormal distributions.
Any pointers on how to create a function that would produce the 5
parameters of f(x) would be greatly appreciated.
> version
_
platform i386-pc-mingw32
arch i386
os
2005 Oct 07
1
Troubleshooting with "gpd" (Fit generalized pareto model)
Up to now, I have recognized problems with "gpd(..)", the function from
the package "evir"
I think that all these functions that estimate the parameters xi, beta for
the GPD
by given threshold mu use the function "optim(..)" ( gpd, fitgpd, ...)
"Error" example:
data1 <- rgpd(1000, xi= -1.5, mu=1000, beta=100)
so the created poinnts take place in about
2011 Nov 01
1
low sigma in lognormal fit of gamlss
Hi,
I'm playing around with gamlss and don't entirely understand the sigma
result from an attempted lognormal fit.
In the example below, I've created lognormal data with mu=10 and sigma=2.
When I try a gamlss fit, I get an estimated mu=9.947 and sigma=0.69
The mu estimate seems in the ballpark, but sigma is very low. I get similar
results on repeated trials and with Normal and
2008 Oct 07
3
Fitting weibull, exponential and lognormal distributions to left-truncated data.
Dear All,
I have two questions regarding distribution fitting.
I have several datasets, all left-truncated at x=1, that I am attempting
to fit distributions to (lognormal, weibull and exponential). I had
been using fitdistr in the MASS package as follows:
fitdistr<-(x,"weibull")
However, this does not take into consideration the truncation at x=1. I
read another posting in this
2017 Nov 13
2
[RFC] Enable Partial Inliner by default
Hi Graham,
I created a bug report with a reproducer for the failures I’ve got: https://bugs.llvm.org/show_bug.cgi?id=35288
I have also found that LTO reverts everything the partial inliner has done. Maybe the partial inliner should not be used at the first LTO phase (compilation).
I hope I’ll have a chance to look at the code size regressions this week.
Thanks,
Evgeny Astigeevich
From:
2017 Nov 10
0
[RFC] Enable Partial Inliner by default
Hi Evgeny,
I just realized that if these are compile-time errors I can help
investigate on my end. Do you have something I can use to reproduce?
Cheers,
Graham Yiu
LLVM Compiler Development
IBM Toronto Software Lab
Office: (905) 413-4077 C2-707/8200/Markham
Email: gyiu at ca.ibm.com
From: Graham Yiu/Toronto/IBM
To: Evgeny Astigeevich <Evgeny.Astigeevich at arm.com>
Cc:
2017 Oct 03
2
PGO information at LTO/thinLTO link step
On Tue, Oct 3, 2017 at 1:46 PM, Teresa Johnson via llvm-dev <
llvm-dev at lists.llvm.org> wrote:
>
>
> On Tue, Oct 3, 2017 at 1:38 PM, Graham Yiu <gyiu at ca.ibm.com> wrote:
>
>> Hi Teresa,
>>
>> Actually, enabling the new pass manager manually seems to have solved
>> this issue, so this problem is only valid for the old pass manager.
>>
>
2013 Jan 21
0
random draw from a RESTRICTED pareto distribution
Dear R user,
I am a newcomer and need help concerning 'draw a random number for a
restricted area of a prareto distribution'.
(1) For estimation of pareto distribution:
>http://stats.stackexchange.com/questions/27426/how-do-i-fit-a-set-of-data-to-a-pareto-distribution-in-r<
We calculate the pareto distribution (parameter estimation) as follows:
pareto.MLE <- function(X)
{
n
2017 Oct 03
2
PGO information at LTO/thinLTO link step
Thanks Easwaran. This is what we've observed as well, where the old PM
inliner was only looking hot/cold callee information, which have
signficantly smaller boosts/penalties compared to callsite information.
Teresa, do you know if there is some documentation/video/presentation on
how PGO information is represented in LLVM and what information is passed
via the IR? I'm finding some
2011 Jun 03
0
Pareto Chart using GUI
Hi,
I am exploring GUI's for doing Quality
Management/Assurance/Improvement activities and this is another mail
in series!
Focus of this mail is Pareto Analysis for following data (Truncated):
Date Defect code Operator Shift Machine Cost - Internal Cost -
External Cost - Total
8-Jun-2011 410 Joe 1 AAA 5 50 55
8-Jun-2011 465 Joe 1 AAA 1.5 25 26.5
8-Jun-2011 412 Joe 1 AAA 1.5 10 11.5
2005 Jan 09
2
How can I simulate Pareto distribution in R?
Hi, guys,
I need to simulate Pareto distribution. But I found 'rpareto' didn't exist in R. And it seems that Pareto distribution don't have mathematical relationships with other distributions. What can I do?
Thanks a lot.
Ni
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2010 Nov 09
2
simulation from pareto distn
Dear all,
I am trying to simulate from truncated Pareto distribution. I know there is
a package called PtProcess for Pareto distribution...but it is not for
truncated one. Can anyone please help me with this?
Thanks in advance.
Cassie
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2008 Jan 22
2
MLE for censored distributions in R
Hi just wondering if there is a package that can get the maximum likelihood
or method of moments estimator for distributions with censored data? The
distributions I'm interested in are: Exponential, pareto, beta, gamma and
lognormal.
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2017 Aug 24
1
rmutil parameters for Pareto distribution
In https://en.wikipedia.org/wiki/Pareto_distribution, it is clear what the
parameters are for the pareto distribution: *xmin *the scale parameter and
*a* the shape parameter.
I am using rmutil to generate random deviates from a pareto distribution.
It says in the documentation that the probabilty density of the pareto
distribution
The Pareto distribution has density
f(y) = s (1 + y/(m
2007 Jul 11
1
CDF for pareto distribution
Hi, I would like to use the following codes to plot the CDF for pareto
distribution. Before doing this, I have plot the emperical one.
x <- seq(1.6, 3, 0.1)
lines(x,pgpd(x, 1.544,0.4477557,), col="red")
Could anyone give me some advice whether the above codes are correct?
Many thanks.
--
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2007 Jun 13
2
Fitted Value Pareto Distribution
I would like to fit a Pareto Distribution and I am using the following codes.
I thought the fitted (fit1) should be the fitted value for the data, is it
correct? As the result of the "fitted" turns out to be a single value for
all.
fit=vglm(ycf1 ~ 1, pareto1(location=alpha), trace=TRUE, crit="c")
fitted(fit)
The result is
fitted(fit)
[,1]
[1,] 0.07752694