# vinux

India

 Apr 9 revised Matrix decompositionformatting Mar 27 awarded Guru Feb 22 awarded Constituent Feb 18 answered Sample ACF and PACF of a random walk Feb 18 awarded Caucus Feb 12 comment ARMA conditional density@dynamic89, I missed to see that $\mu_t$ and σt are time dependent. Are both the function deterministic function of t? – Feb 12 comment ARMA conditional densityThat means, the series is not stationary. But still you get $y_t$ as sum of independent normal random variables (Hence $y_t$ is normally distributed). Feb 12 comment ARMA conditional density@dynamic89, If you are assuming $\epsilon_t$ are iid, by repeated substitution you can write $y_t$ as sum of $\epsilon_t$. Sum of independent normal rvs-> normal Feb 11 answered ARMA conditional density Feb 11 comment Left-censoring in time series dataYou are right.. it is in the other way. Feb 11 comment Left-censoring in time series dataTake T=1. How do you calculate $f(x_1|\varphi)$ from $f(y_1|\varphi)$? (from X you can calculate Y. Reverse case, you require some assumption.) Feb 8 answered Conditional expectation in AR(1) process Feb 8 revised Conditional expectation in AR(1) processformatting. added description of u Feb 8 comment Conditional expectation in AR(1) processAre you assuming distribution of $\epsilon_t$ identical? Also what about $m$? \$m