search for: 99th

...inary search when the previous binary search returned 0, and times for the new approach, which directly picks the first item/child node in the leaf/node. Current approach: Count: 5013 Range: 25.000 - 497.000; Mean: 82.767; Median: 64.000; Stddev: 49.972 Percentiles: 90th: 141.000; 95th: 182.000; 99th: 287.000 25.000 - 33.930: 211 ###### 33.930 - 45.927: 277 ######## 45.927 - 62.045: 1834 ##################################################### 62.045 - 83.699: 1203 ################################### 83.699 - 112.789: 609 ################## 112.789 - 151.872: 450 ###...

I have a 20x3 matrix as follows: > m <- replicate(3, matrix(rnorm(20),20,1)) I need to compute, say, 95th and 99th percentiles of each column such that the resulting matrix becomes 2x3 with each row representing the respective percentile. My "best effort" is to compute one column at a time as follows: > quantile(m[,1], c(0.95, 0.99)) To do the same for columns 2 and 3, I would simply change the...

...sample(500:600, 98) > sample(500:600, 98)->t > fit<-arima(LakeHuron, order=c(2,1,1), xreg=t) > fit > predict(fit, n.ahead=1, newxreg=t) Now, my problem is this: is it ok to use the same t in predict function or should my newxreg contain 99 values in order to properly predict the 99th value for LakeHuron? Another problem is that i don't receive the 99th value on the screen, as i should (running the example in ?predict > (fit <- arima(USAccDeaths, order = c(0,1,1), + seasonal = list(order=c(0,1,1)))) > predict(fit, n.ahead = 6) gives me on the scre...

...onditions. For each condition, 100 sets of 10 random numbers from N(0,1) need to be generated. Here is my question. At the begining I specify a seed number. I want to make the 100th set of the first condition and 1st set of the second conditon the same. What do I need to do ? After generating 99th set of 10 random numbers and then saving .Random.seed then using .Random.seed for generating 100th set of the first condition and 1st set of the second condtion. Is this right? .Random.seed is a vector with 626 numbers, but set.seed() only accepts a integer number. What do I need to do for savi...

Ok, this is crazy, but I have a livedvd I created with fedora-8's syslinux-3.36-7.fc8 and when I boot under qemu ( -boot d -cdrom /dev/dvd) it boots just fine, but when I boot it on the actual hardware (vaio vgn-n250e), the kernel cmdline gets truncated at about the 99th character, causing problems. The same exact physical livedvd booted on a different box does not get truncated. The stock fedora f8 livecd on the vaio does not get truncated (even when I make the cmdline ridiculously long). I'm pretty sure prior (and probably subsequent) livedvds have not h...

I have been quantreg library for a number of projects but have just hit a snag. I am using nlrq to examine an asymptotic relationship between 2 variables at the 99th percentile. It performs as expected, however when I try to extract the coefficients along with se and significance I am running into problems. The problem is that for the nlrq regression Dat.nlrq, summary(Dat.nlrq) reports a different coefficients table than summary( Dat.nlrq)$coefficients. Below...

I have a model as follows: x <- replicate(100, sum(rlnorm(rpois(1,5), 0,1))) y <- quantile(x, 0.99)) How would one go about estimating the boundaries of a 95% confidence interval for y? Any pointers would be greatly appreciated. > version _ platform i386-pc-mingw32 arch i386 os mingw32 system i386, mingw32 status major 2 minor 5.1 year 2007 month 06 day 27 svn rev 42083 language R

...;21st" "22nd" > ordinal(nn,"english") [1] "0th" "1st" "2nd" "3rd" "9th" "10th" "11th" "12th" "13th"= [10] "20th" "21st" "22nd" "99th" "100th" "101st" "102nd" > ordinal(nn,"francais") [1] "0me" "1=E8re" "2me" "3me" "9me" "10me" "11me" "12me" "13m= e" [10] "20me" "...

...42 they give a short summary, describing the steps. The third step in the most intersting: "Third, we use [...] volatility estimated and the [...] probability distributions ([...] generalized error distribution) evaluated at the parameter estimates to construct VaR forecasts at the 1st and the 99th percentile." My question is now, how do they do it? They describe their fitting steps in the steps before, but I am not getting the following point: Do they fit the distribution to the original return series, calculate the volatility (?t) and then just calculate the VaR with VaR_t=sigma_t*q...