search for: alpha_1

Hi, I'm trying to fit the following model to data using 'nls': y = alpha_1 * beta_1 * exp(-beta_1 * x) + alpha_2 * beta_2 * exp(-beta_2 * x) and the call I've been using is: nls(y ~ alpha_1 * beta_1 * exp(-beta_1 * x) + alpha_2 * beta_2 * exp(-beta_2 * x), start=list(alpha_1=4, alpha_2=2, beta_1=3.5, beta_2=2.5), trace=TRUE, control=nls.control...

Hi, as i suppose to know in a stationary GARCH(1,1) model the sum of alpha and beta has to be smaller than 1. But if i use the garchfit() function from the package fGarch for my timeseries the sum is bigger than 1. The adf.test tells me a p-value smaller than 0.01 instead. What does this mean for me? Can i trust in the coefficients in this case? mfg user84 -- View this message in context:

Hello, Could someone give me a hint about what might be the difference between running urppTest with Z-alpha and Z-tau in type=c("Z-alpha", "Z-tau")? Is this the underlying equation: delta_y(t) = mu + tau*timetrend+(1-rho)*y(t-1) + alpha_1*delta_y(t-1) + ... + alpha_k*delta_y(t-k) + error term ? I looked at Banerjee et al. mentioned in the fSeries documentation, but that didn't help. Thanks a lot, Katrin

I feel bad even asking, but: Rgames> data(OrchardSprays) Rgames> model<-lm(decrease~.,data=OrchardSprays) Rgames> model Call: lm(formula = decrease ~ ., data = OrchardSprays) Coefficients: (Intercept) rowpos colpos treatmentB treatmentC 22.705 -2.784 -1.234 3.000 20.625 treatmentD treatmentE treatmentF treatmentG treatmentH

...001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 Residual standard error: 1.873 on 11 degrees of freedom Multiple R-Squared: 0.9403, Adjusted R-squared: 0.9241 F-statistic: 57.78 on 3 and 11 DF, p-value: 5.082e-07 In the book, the estimates are From this book, mu=33.8 alpha_1=6.017 alpha_2=0.942 beta=0.899 Is it possible to obtain this estimates and their standard errors from the model I fitted? Thanks in advance Tomas Goicoa [[alternative HTML version deleted]]

Dear Sir/Madam, I apologize for any cross-posting. I got a simple question, which I thought the R list may help me to find an answer. Suppose we have Y_1, Y_2, ., Y_n ~ Poisson (Lambda_i) and Lambda_i ~Gamma(alpha_i, beta_i). Empirical Bayes Estimator for hyper-parameters of the gamma distr, i.e. (alpha_t, beta_t) are needed. y=c(12,5,17,14) n=4 What about a Hierarchal B ayes

...0.47083 0.08461 5.56 tstnum6 0.97500 0.08461 11.52 The mean of (the columns of) the data matrix is 3.229167 3.695833 3.729167 4.066667 3.700000 4.204167 which is in exact agreement with the lmer() results when converted to the same parameterization (mu_i = mu + alpha_i, with alpha_1 = 0). (Notice the surprizing, depressing, and so far unexplained *drop* in the response over the second summer.) What I *don't* understand is the correlation structure of the estimates produced by lmer(), which is: Correlation of Fixed Effects: (Intr) tstnm2 tstnm3 tstnm4 tstnm5 tst...

Hi everybody, I'm trying to fit a Garch(1,1) process to the DAX returns. My data consists of about 2300 10day-logreturns in chronologically descending order (see attachment). But if I use the garch function I get a very high alpha_1 and a quite low beta, which doesn't make that much sense. I think I am missing something, but have no idea what it might be. I'd appreciate it a lot if someone could have a look at the output I posted at the end of this mail. Maybe there's something an experienced user might see at...

...1 attr(,"assign") [1] 0 1 1 2 3 3 attr(,"contrasts") attr(,"contrasts")$LevelA [1] "contr.treatment" attr(,"contrasts")$LevelB [1] "contr.treatment" which shows that internally lm() seems to use corner point constraints of the form \[\alpha_1=\beta_1=(\alpha\beta)_{11}= (\alpha\beta)_{12}=(\alpha\beta)_{12}=(\alpha\beta)_{31}=0\] in the model $E[Y_{jkl}]=\mu+\alpha_j+\beta_k+(\alpha\beta)_{jk}$ $j=1,2,3$, $k=1,2$, $l=1,2$, Dobson, page 102. My question is: how can I incorporate restrictions like $\alpha_1+\alpha_2+\alpha_3=0$, $\be...