similar to: Help: Maximum likelihood estimation

Hello, I'm trying to calculate the Maximum likelihood estimators for a dataset which contains censored data. I started by using the function "nlm", but isn't there a separate method for doing this for e.g. the "weibull" and the "log-normal" distribution? Thanks, Olivia [[alternative HTML version deleted]]

I'm trying to estimate the parameters for GARCH(1,1) process. Here's my code: loglikelihood <-function(theta) { h=((r[1]-theta[1])^2) p=0 for (t in 2:length(r)) { h=c(h,theta[2]+theta[3]*((r[t-1]-theta[1])^2)+theta[4]*h[t-1]) p=c(p,dnorm(r[t],theta[1],sqrt(h[t]),log=TRUE)) } -sum(p) } Then I use nlminb to minimize the function loglikelihood: nlminb(

Have a look at ?optim. I don't think it has the BHHH algorithm as an option, though. =========================================== David Barron Jesus College University of Oxford -----Original Message----- From: r-help-bounces at stat.math.ethz.ch [mailto:r-help-bounces at stat.math.ethz.ch]On Behalf Of Harold Doran Sent: 10 July 2003 15:43 To: Fohr, Marc [AM]; R-help at stat.math.ethz.ch

Hi, I have a quick question regarding estimation of a truncation regression model (truncated above at 1) using MLE in R. I will be most grateful to you if you can help me out. The model is linear and the relationship is "dhat = bhat0+Z*bhat+e", where dhat is the dependent variable >0 and upper truncated at 1; bhat0 is the intercept; Z is the independent variable and is a uniform

Dear R users >From what I understand, the joint maximum likelihood procedure for Rasch (availabe in the package MiscPsycho) in R can only be used on binary data. I was wondering if the code is currently being adapted for application to ordinal data? I'm trying to replicate results obtained from Winsteps in R. Best wishes denn -- View this message in context:

Hi, I want to use Maximum likelihood to estimate the parameters from my regression line. I have purchased the book "Applied linear statistical models" from Neter, Kutner, nachtsheim & Wasserman, and in one of the first chapters, they use maximum likelihood to estimate the parameters. Now I want to tried it for my self, but couldn't find the right function. In the book, they give

Hello! I need to perform maximum likelihood estimation on R, but I am not sure which command to use. I searched on google, and found an example using the function mlogl, but I couldn't find the package on R. Is there such function? Or how should i perform my mle? Thank you very much. -- View this message in context:

Hi, as you can see in the topic, I am trying to fit a normal mixture distribution with the approach suggested by Hamilton (1991). Since I couldn't find any existing packages including the quasi-bayesian mle, I have to write my own function. Unfortunately, I have absolutely no experience in doing this. If you're not familiar with the QB-MLE, I attached the formula as pdf. The idea

Hi, The following distribution is known as Kumaraswamy binomial Distribution. http://r.789695.n4.nabble.com/file/n4636782/kb.png For a given data I need to estimate the paramters (alpha and beta) of this distribution(Known as Kumaraswamy binomial Distribution, A Binomial Like Distribution). For that, in order to use *optim()*, I first declared the Negative Log-likelihood of this distribution as

I want to find ML estimates of a model using mle2 in bbmle package. When I insert new parameters (for new covariates) in model the log-likelihood value does not change and the estimated value is exactly the initial value that I determined. What's the problem? This is the code and the result: As you see the estimated values for b2 , b3 and b4 are the initial values of them. The

Dear R experts/ users Full Information Maximum Likelihood (FIML) estimation approach is considered robust over Seemingly Unrelated Regression (SUR) approach for analysing data of multivariate sample selection problem. The zero cases in my dependent variables are resulted from three sources: Irreverent options, not choosing due to negative utility and not used in the reported time. FIML can

Dear R Helpers This is the R code (which I have slightly changed) I got in VGAM package for estimating the parameters of FRECHET. _________________________________________________________________ y = rfrechet(n <- 100, shape=exp(exp(0))) # (A) fit3 = vglm(y ~ 1, frechet3(ilocation=0), trace=TRUE, maxit=155) # (B) coef(fit3, matrix=TRUE) # (C)

Using R, I would like to calculate algorithms to estimate coefficients á and â within the gamma function: f(costij)=((costij)^á)*exp(â*costij).  I have its logarithmic diminishing line data (Logarithmic Diminishing Line Data Table) and have installed R¢s Maximum Likelihood Estimation package; however, I am unsure which method to apply in order to calculate the algorisms (i.e., Newton-Raphson

Dear R-users Problem: Given the following system of ordinary differential euqations dM/dt = (-n)*M-h*M dS/dt = n*M-h*S+u*R dA/dt = h*S-q*A dI/dt = q*A-p*I dJ/dt = h*M-v*J dR/dt=p*I+v*J-u*R where M,S,A,I,J,R are state variables and n,h,u,q,p,v parameters. I'm able to calculate the likelihood value based on the solutions M,S,A,I,J,R of the ODE's given the data, but without an explicit

Dear R-helper, I am trying to do maximum likelihood estimation in R. I use the "optim" function. Since I have no prior information on the true values of the parameters, I just randomly select different sets of starting values to feed into the program. Each time, I get the following error message: Error in optim(theta0, lf, method = "BFGS", hessian = T, Y = Y, X = X, :

Hi, I got two questions and would really appreciate any help from here. First, is the penalized maximum likelihood estimation(Firth Type Estimation) only fit for binary response (0,1 or TRUE, FALSE)? Can it be applied to multinomial logistic regression? If yes, what's the formula for LL and U(beta_i)? Can someone point me to the right reference? Second, when I used *logistf *on a dataset with

Hi I am trying to carry out some maximum likelihood estimation and I'm not making much headway, and I'm hoping that someone will be able to point me in the right direction. I am modelling mortality statistics. One way to do this is to model the mortality rate (or, more accurately, log of the mortality rate, log_m) as (say) a constant plus a proportion of age, plus time, so: r_1 <-

Hello Following some standard textbooks on ARMA(1,1)-GARCH(1,1) (e.g. Ruey Tsay's Analysis of Financial Time Series), I try to write an R program to estimate the key parameters of an ARMA(1,1)-GARCH(1,1) model for Intel's stock returns. For some random reason, I cannot decipher what is wrong with my R program. The R package fGarch already gives me the answer, but my customized function

Hi, I would like estimate a model for function of production's Coob-Douglas using maximum likelihood. The model is log(Y)= beta[1]+beta[2]*log(L)+beta[3]*log(K). I tried estimate this model using the tools nlm ( ) and optim ( ) using the log-likelihood function below: > mloglik <- function (beta, Y, L, K) { + n <- length(Y) + sum ( (log(Y)-

Hello, I am struggling for some time now to estimate AR(1) process for commodity price time series. I did it in STATA but cannot get a result in R. The equation I want to estimate is: p(t)=a+b*p(t-1)+error Using STATA I get 0.92 for a, and 0.73 for b. Code that I use in R is: p<-matrix(data$p) # price at time t lp<-cbind(1,data$lp) # price at time t-1