similar to: Implementation of quasi-bayesian maximum likelihood estimation for normal mixtures

Ok, I try it again with plain text, with a simple R code example and just sending it to the r list and you move it to sig finance if it is necessary. I try to be as detailed as possible. I want to fit a distribution to my financial data using a volatility model to estimate the VaR. So in case of a normal distribution, this would be very easy, I assume the returns to follow a normal distribution

Hi, Could you please help me with EGARCH and GJR? Is it possible to use EGARCH and GJR in R? I have used below mentioned code for GARCH in R, but I never used EGARCH and GJR in R. Thank you in advance! daten<-read.table("H://Daten//Zeitreihen//dax_1.csv", sep=";", header=T) DAX.kurs<-daten DAX.kurs<-ts(DAX.kurs,names="DAX-Kurs")

Dear R-helpers, this mail is intended to mention a rather trange result and generate potential useful comments on it. I am not aware of another posts on this issue ( RSiteSearch("quasipoisson lmer version dispersion")). MUsing the exemple in the reference of the lmer function (in lme4 library) and turning it into a quasi-poisson or quasi-binomial analysis, we get different results,

I have a class called Duration that represents a unit of time. It has lots of time-related methods and an attribute that represents the duration in seconds. I want to store it in my db. I had a column type of NUMERIC, so if i save it, rails trys to coerce it to a float automagically and fails. I need to display it and store it as a number but treat it in the middle layer as a Duration so I can

Hi, I'm using a GLM with a quasi binomial error distribution and I would like to do a model selection method similar to step(AIC) to carry out a restricted search for the "best" model. I would like to know which of my 5 predictor variables would be included in the "best" model if I start with a 'full' model (fullbinom in this case). However, AIC can't be

Dear all, Using the quasibinomial and quasipoisson families results in no AIC being calculated. However, a quasi AIC has actually been defined by Lebreton et al (1992). In the (in my opinon, at least) very interesting book by Burnham and Anderson (1998,2002) this QAIC (and also QAICc) is covered. Maybe this is something that could be implemented in R. Take a look at page 23 in this pdf:

Quasi-Poisson regression - using parameter estimates for QAICc Hello, I am using lmer (package lme4), for a GLMM, where I am modeling overdispered data with 1 random effect and several fixed effects. I want to use QAICc for my model selection, however I have 2 concerns 1) I don't know how to properly estimate the overdispersion parameter (c_hat), which is needed to calculate QAICc. I

Hello, Has anyone written up code to estimate for example a simple logit using quasi-likelihood? I know that glm() already does this, but I'd like to do some tinkering with the variance function beyond what glm() allows. I've scanned online sources and everyone seems to use glm(). Will take a crack at it if necessary but have zero experience w/q-likelihood and not that much with

Hello, I'm looking for a dataset for Quasipoisson regression. The result must be significantly different from the classic poisson regression. You can help me? Please It is for my last university exam Thanks a lot -- View this message in context: http://r.789695.n4.nabble.com/Dataset-Quasi-Poisson-tp3533060p3533060.html Sent from the R help mailing list archive at Nabble.com.

Hello all. I'd like to report near success with two games I've tested today. I used the latest wine (20040408). 1?) Arcanum ----------- I installed with wine; when I was prompted for the 2nd CD, I couldn't umount the first; so I cancelled the installation (as far as I remember the files were not erased; if they were I must have copied them somewhere before); then I copied the files

Un texte encapsul? et encod? dans un jeu de caract?res inconnu a ?t? nettoy?... Nom : non disponible Url : https://stat.ethz.ch/pipermail/r-help/attachments/20060609/ab46b028/attachment.pl

Hi All, I'm trying to implement a quasi symmetry model for data on twin pairs. A crosstabulation of twin 1 by twin 2 (assumed symmetrical) stratified by another variable. There is a good paper on this by Phil (?) McCloud and Darroch in Biometrika (1995) which explains the method, but I've not done this before so am not clear how to code these models. Any help would be greatly

Hi all, This is a very silly question or something escapes me: Let obj a simple gam poisson model. Let >obj<-gam(....,family=poisson) >obj1<-update(obj, family=quasi(link="log", var="mu")) >From summary.glm(obj1) the dispersion parameter is estimated 1.165; In fact it is: > (predict(obj1, se.fit=T)$se.fit[1:5]/predict(obj, se.fit=T)$se.fit[1:5])^2 4

Hello, I've been looking in the on-line manuals and searching past posts but can't find an answer to this question. I'd like to define my own variance function in a GLM. The function glm(formula, family=quasi(var="var function")) lets me choose from a selection of built in variances, but I want to define my own function for the variance. Is there an S-plus

Hi, I'm tying to fit a glm using quasi with variance=mu(1-mu) (to try and get "well behaved" residuals). I get an error message: "cannot find valid starting values, please specify some". I have tried using start= and specifying coefficients from a previous fit (with different error structure) but get the same message. What would be valid starting values? I am using a

Hi all, Cameron and Trivedi in their 1998 Regression Analysis of Count Data refer to NB1 and NB2 NB1 is the negative binomial model with variance = mu + (alpha * mu^1) yielding (1+alpha)*mu NB2 sets the power to 2; hence, variance = mu + (alpha*mu^2) I think that NB2 can be requested via negbin2<-glm(hhm~sex+age,family=quasi(var="mu^2",link="log")) Is

Hi everyone, I'm trying to perform the following regressions in order to compare linear vs. sigmoidal fit of the relationship between my dependent variable (y) and one explaining parameter (x2), both including the confounding effects of a third variable (x1): quasi-pois-lin <- glm(y ~ x1 + x2, family = quasipoisson(link="identity"), data=fit) quasi-pois-sig <- gnm(y ~ x1 +

Hi list, can anybody point me to the trick how glm is computing the dispersion parameter in quasi-poisson regression, eg. glm(...,family="quasipoisson")? Thanks &regards, Sven

Hello R-helpers, I would like to fit a non-linear function to data (Discrete X axis, over-dispersed Poisson values on the Y axis). I found the functions gnlr in the gnlm package from Jim Lindsey: this can handle nonlinear regression equations for the parameters of Poisson and negative binomial distributions, among others. I also found the function nls2 in the software package

Hi I want to do simulation on quasi-binomial distribution with some covariates. Does anyone have an idea how to do that? [[alternative HTML version deleted]]