fit.mst {QRMlib} | R Documentation |
fits multivariate Student's t distribution using variant of EM algorithm
fit.mst(data, nit=2000, tol=1e-10)
data |
matrix of data where rows are vector observations. A good choice might be data.t.5d = rmghyp(n,lambda=(-nu/2),chi=nu,psi=0,Sigma=P,mu=mu,gamma=gamma) |
nit |
number of iterations of EM-type algorithm |
tol |
tolerance of improvement for stopping iteration |
see page 75 of QRM
list containing parameter estimates, standard errors and details of fit
documentation by Scott Ulman for R-language distribution
data(DJ); Ret.DJ <- mk.returns(DJ); window1.start <- timeDate("01/01/1993",format="%m/%d/%Y"); window1.end <- timeDate("12/31/2000",format="%m/%d/%Y"); sample1 <- (seriesPositions(Ret.DJ) > window1.start & seriesPositions(Ret.DJ) < window1.end); selection1 <- c("AXP","EK","BA","C","KO","MSFT", "HWP","INTC","JPM","DIS"); DJ30daily <- Ret.DJ[sample1,selection1]; DJ30weekly <- aggregateWeeklySeries(DJ30daily, FUNC= colSums); mod.t <- fit.mst(DJ30weekly);