ate University
Department of Fisheries & Wildlife
[EMAIL PROTECTED]
"Brian Leung" <[EMAIL PROTECTED]> wrote in message
[EMAIL PROTECTED]">news:[EMAIL PROTECTED]...
> Greetings,
>
> I've read that the likelihood ratio test is not valid for non-nested
> models. I
>> wrote:
>>
>> > Greetings,
>> >
>> > I've read that the likelihood ratio test is not valid for non-nested
>> > models. Is this still true if the PF (i.e., multinomial) is the same,
>> > but the link function differs.
>> >
lt;[EMAIL PROTECTED]>...
> On 19 Jan 2002 10:25:10 -0800, [EMAIL PROTECTED] (Brian Leung)
> wrote:
>
> > Greetings,
> >
> > I've read that the likelihood ratio test is not valid for non-nested
> > models. Is this still true if the PF (i.e., multinomi
On 19 Jan 2002 10:25:10 -0800, [EMAIL PROTECTED] (Brian Leung)
wrote:
> Greetings,
>
> I've read that the likelihood ratio test is not valid for non-nested
> models. Is this still true if the PF (i.e., multinomial) is the same,
> but the link function differs.
>
Yes, i
Greetings,
I've read that the likelihood ratio test is not valid for non-nested
models. Is this still true if the PF (i.e., multinomial) is the same,
but the link function differs.
Thanks,
Brian
=
Instructions for joinin
similar (simulated) data directly to Markov models using the Q-matrix and
maximum likelihood methods. The likelihood function is:
L= (1/Sqrt( | CV-Matrix |))*exp((-1/2)*(O-E).(CV-Matrix^-1).(O-E))
Where | CV-Matrix | is the determinant of the Covariance matrix, (O) is the
vector of observed
"Herman Rubin" <[EMAIL PROTECTED]> wrote in message
9vqoln$[EMAIL PROTECTED]">news:9vqoln$[EMAIL PROTECTED]...
>
> Maximum likelihood is ASYMPTOTICALLY optimal in LARGE
> samples. It may not be good for small samples; it pays
> to look at how the actual likel
In article <[EMAIL PROTECTED]>,
Jimc10 <[EMAIL PROTECTED]> wrote:
>To all who have helped me on the previous thread thank you very much. I am
>reposting this beause the question has become more focused.
>I am studying a stochastic Markov process and using a maximum likelih
To all who have helped me on the previous thread thank you very much. I am
reposting this beause the question has become more focused.
I am studying a stochastic Markov process and using a maximum likelihood
technique to fit observed data to theoretical models. As a first step I am
using a Monte
In article <[EMAIL PROTECTED]>,
Jimc10 <[EMAIL PROTECTED]> wrote:
>I am using a maximum likelihood algorythym to fit an electophysiologic data to
>a series of theoretical models. I am interesting in comparing two models wich
>differ in the number of free parameters
I am using a maximum likelihood algorythym to fit an electophysiologic data to
a series of theoretical models. I am interesting in comparing two models wich
differ in the number of free parameters, the simpler being a subhypothesis of
the more complex.
I was told (and have read) that the
Can anybody tell me the difference between the maximum likelihood
method and prediction error method. Can anyone suggest me the useful material.
Thanks
=
Instructions for joining and leaving this list and remarks about
the problem
Dear Debater,
I've got two models which are nested (or hierarchical), and values
likelihood ratio chi-square (L^2) for them with degrees of freedom :
1 model: L^2 =21.93 , df=16
2 model: L^2=22.13 , df=18
I read that it' posible to settle which model is better (because they are
hi
I'm using Matlab 6.0 to perform Maximum Likelihood estimation
for identifying parameters.
The problem is that i am dealing with time varying parameters (which
are to be identified). and along with that other terms in matrices
(A,B,C,D,K) are also time varying. I am elaborating this as fo
In article <000f01c02504$0878b840$[EMAIL PROTECTED]>,
David A. Heiser <[EMAIL PROTECTED]> wrote:
>1. I appreciate professor deLeuw recommending A. W. F. Edward's book
>"Likelihood" (expanded version). Read it from cover to cover. Excellent
>sour
Gotcha. It is the headlines that count.
1. I appreciate professor deLeuw recommending A. W. F.
Edward's book "Likelihood" (expanded version). Read it from cover to cover.
Excellent source of ideas and analysis of Fisher's contributions.
2. The issue is, do we follow th
In article <[EMAIL PROTECTED]>,
P.G.Hamer <[EMAIL PROTECTED]> wrote:
> David A. Heiser wrote:
>> I am going to reference Fisher as his views later on in life in the 1973 3rd
>> edition of "Statistical
>> Methods and Scientific Inference"
>>
David A. Heiser wrote:
>Second, one needs to read Fisher's insight into Bayes original
>work to understand Fisher's view of probability.
The only discernable connection between Bayes original work
and Fisher's view of probability is that they are both discussed
in Chapter
- Original Message -
From: P.G.Hamer <[EMAIL PROTECTED]>
To: <[EMAIL PROTECTED]>
Sent: Thursday, August 17, 2000 2:40 AM
Subject: Re: likelihood
> David A. Heiser wrote:
>
> > I am going to reference Fisher as his views later on in life in the 1973
3rd
>
- Original Message -
From: Li0N_iN_0iL <[EMAIL PROTECTED]>
To: <[EMAIL PROTECTED]>
Sent: Thursday, August 17, 2000 5:51 PM
Subject: Re: likelihood
> David A. Heiser wrote:
>
> >>>Second, one needs to read Fisher's insight into Bayes original work
David A. Heiser wrote:
>>>Second, one needs to read Fisher's insight into Bayes original work to
>>>understand Fisher's view of probability.
>>
>> The only discernable connection between Bayes original work and Fisher's
>> view of probability is that they are both discussed in Chapter 2 of his
>>
n;
> charset="iso-8859-1"
> Content-Transfer-Encoding: quoted-printable
>
> I did some research on likelihood and wrote a 5 page response. I sent
it =
> to the EDSTAT boxes that were involved in the discussion. If any one =
> wants a copy, send
David A. Heiser wrote:
> I am going to reference Fisher as his views later on in life in the 1973 3rd
> edition of "Statistical
> Methods and Scientific Inference"
>
> "The Mathematical Likelihood assignable to every value of the unknown
> parameter p supplies
I did some research on likelihood and wrote a 5 page response.
I sent it to the EDSTAT boxes that were involved in the discussion. If any one
wants a copy, send me a request and I will send it.
DAHeiser
No L(H|D) is not a probability and it does not
obey the laws of probability.
Your citation references a discussion about
something else, fiducial probability I would
think.
The likelihood ratio is a fundamental part of
Neyman-Pearson. It is the heart of their
fundamental theorem.
The fit of
- Original Message -
From: Li0N_iN_0iL <[EMAIL PROTECTED]>
To: <[EMAIL PROTECTED]>
Sent: Thursday, August 10, 2000 4:45 PM
Subject: Re: likelihood
> David A. Heiser wrote:
> >Second, one needs to read Fisher's insight into Bayes original work to
>
- Original Message -
From: Bob Wheeler <[EMAIL PROTECTED]>
To: <[EMAIL PROTECTED]>
Sent: Friday, August 11, 2000 9:50 AM
Subject: Re: likelihood
> I'll suppose that you don't really want to have a
> discussion about probability, but are really
> ask
The definition below is (in all likelihood) taken from
A.W.F. Edwards, Likelihood (Expanded Edition), John Hopkins
University Press, 1992 (paper, ISBN 0-8018-4445-2).
This is a delightful book. Also quite readable, although
not exactly delightful, is
J.O. Berger and R.L. Wolpert. The
I'll suppose that you don't really want to have a
discussion about probability, but are really
asking about 'likelihood.'
The definition of likelihood today, except that it
may be more abstract than Fisher indented, is the
same as that given by Fisher. In particular
In article <8mvea9$78t5j$[EMAIL PROTECTED]>,
Li0N_iN_0iL wrote:
>David A. Heiser wrote:
>>von Mises criticizes Fisher (1921) for his introduction of the term
>>"likelihood" without defining it, since in common usage, 'likelihood'
>>and 'p
I'm enjoying learning about Fisher's distinction between probability and
likelihood, but it seems to me that since language is alive, "modern"
definitions of likelihood and probability should also be discussed.
In economics, for example, in 1921, Frank Knight came up with a
David A. Heiser wrote:
>von Mises criticizes Fisher (1921) for his introduction of the term
>"likelihood" without defining it, since in common usage, 'likelihood'
>and 'probability" have the same meaning.
Fisher may have addressed this issue in the preface
David A. Heiser wrote:
> First of all Fisher is a very ponderous writer, very difficult to find the
> gold in the pile of ore.
> Second, one needs to read Fisher's insight into
> Bayes original work to understand Fisher's view of probability.
Can you supply a reference? All Fisher's writings o
- Original Message -
From: Gökhan BakIr <[EMAIL PROTECTED]>
To: <[EMAIL PROTECTED]>
Sent: Monday, August 07, 2000 1:07 PM
Subject: likelihood
>
> Hi !
> Please dont flame me for this question if its too foolish,
> but is there a difference between a like
Hi !
Please dont flame me for this question if its too foolish,
but is there a difference between a likelihood and a probability ?
thanks
gökhan
--
Gökhan BakIr
Insitute of Robotics and Mechatronics
Deutsches Forschungszentrum für Luft und Raumfahrt
82234
[EMAIL PROTECTED] wrote:
> A self-report scale was constructed to measure work ethic and included three
> conceptually derived components of work ethic. Maximum likelihood factor
> analysis was then applied with the request of 3 factors to determine if the
> conceptually derive
A self-report scale was constructed to measure work ethic and included three
conceptually derived components of work ethic. Maximum likelihood factor
analysis was then applied with the request of 3 factors to determine if the
conceptually derived components actually represent empirical factors
matrix is not important (algebra).
As the mean is a sufficient statistic, we can assume sample
size 1. This case also reduces to the null hypothesis that
the entire mean is 0.
Now the logarithm of the likelihood function is
C - .5 * \sum (X_i - \mu_i)^2.
The C is unimportant; for the unc
I turns out that the difference between the constrained and
unconstrained log likelihoods has a chi-square distribution (dergr. of
freedom =restrictions) when it is multiplied by two.
Where the two comes from is something I am not getting. (or -2, the
information I have isn't completely consisten
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