similar to: How to control the skewness of a heteroscedastic variable?

When going through my earlier post I find a mistake in the example that I provided. The correct version is provided below. I also start to suspect that my problem is that although the cumulant of a sum of independent variable is the sum of the cumulants, the moments of a sum is not the sum of the moments. But that might not be the only flaw in my application. Regards, Karl-Oskar #An

Hello, I have tried to generate numbers randomly which follow normal, Student-t and skewed Student-t distributions. However, when I check those series for heteroscedastisity test (ARCH) results are showing that there is no heteroscedastisity. As we all know, returns (financial returns) usually have heteroscedastisity. My question is, is it possible somehow generate random numbers which have

Hello, All: I want to simulate future observations from fits to heteroscedastic data. A simple example is as follows: (DF3_2 <- data.frame(y=c(1:3, 10*(1:3)), gp=factor(rep(1:2, e=3)))) # I want to fit 4 models # and simulate future observations from all 4: fit11 <- lm(y~1, DF3_2) fit21 <- lm(y~gp, DF3_2) library(nlme) (fit12 <- lme(y~1, data=DF3_2,

Dear forum, which is the most suitable method to get the prediction interval of a bivariate normal distribution which is consistent with a linear model y = ax + b? I assume it is gls + predict. Am I correct? I'm rather new to R. Is there some reliable sample code for that problem? Thank you best regards -- View this message in context:

Dear ListeRs, I am trying to fit a heteroscedastic multilevel model using lmer{lme4- package). Take, for instance, the (fictive) model below. lmer(test.result ~ homework + Sex -1 + (1 | School)) Suppose that I suspect the error terms in the predicted values to differ between men and women (so, on the first level). In order to model this, I want the 'Sex'-variable to be random on

Hi, I would like to fit a model for a factorial design that allows for unequal variances in all groups. If I am not mistaken, this can be done in lm by specifying weights. A function intended to specify weights for unequal variance structures is provided in the nlme library with the varIdent function. Is it apropriate to use these weights with lm? If not, is there another possibility to do

Dear All, I'm trying to model heteroscedasticity using a multilevel model. To do so, I make use of the nlme package and the weigths-parameter. Let's say that I hypothesize that the exam score of students (normexam) is influenced by their score on a standardized LR test (standLRT). Students are of course nested in "schools". These variables are contained in the

Hi, I have a situation where I have a set of pairs of X & Y variables for each of which I have a (fairly) well-defined PDF. The PDF(x_i) 's and PDF(y_i)'s are unfortunately often rather non-Gaussian although most of the time not multi--modal. For these data (estimates of gas content in galaxies), I need to quantify a linear functional relationship and I am trying to do this as

Dear All, I am using the function gls (in the nlme package) and I would like to fit a heteroscedastic model, with different variances for each of the levels of two stratification variables. In p. 210 of Pinheiro & Bates ("Mixed effects models in S and S-Plus", 2000, Springer), the authors show the use of the "*" operator. However, that is not what I want, because it

Dear R users, I hope to explain the concepts of skewness and kurtosis by generating series of distributions with same skewness and different kurtosis or with same kurtosis and different skewness, but it seems that i cannot find the right functions. I have searched the mailing list, but no answers were found. Is it possible to do that in R? Which function could be used? Thanks a lot. --

Hello everyone, Does anyone know what would cause the skewness() function (from e1071), as well as skew() from psych, to return a value of NaN? I have a vector of positively-skewed data (https://docs.google.com/file/d/0B6-m45Jvl3ZmYzlHRVRHRURzbVk/edit?usp=sharing) which these functions return a value for like normal: > skewness( data ) # returns 1.400405 but when I instead give those

Hi, is there a test for the H0 skewness=0 (or with skewness as test statistic and normality as H0) implemented in R? Thank you, Christian *********************************************************************** Christian Hennig Fachbereich Mathematik-SPST/ZMS, Universitaet Hamburg hennig at math.uni-hamburg.de, http://www.math.uni-hamburg.de/home/hennig/

dear members, I have a heteroscedastic time series which I want to transform to make it homoscedastic by a box cox transformation. I am using Otexts by RJ hyndman and George Athanopolous as my textbook. They discuss transformation and also say the fpp3 and the fable package automatically back transforms the point forecast. they also discuss the process which I find to be

I have a question about the D'Agostino skewness test and the Anscombe-Glynn kurtosis test. agostino.test(x, alternative = c("two.sided", "less", "greater")) anscombe.test(x, alternative = c("two.sided", "less", "greater")) The option "alternative" in those two functions seems to be the null hypothesis. In the output, the

I checked the help and the mailing list archives, but I can find no mention of a routine that calculates higher moments like skewness and kurtosis. Of course, these are easy enough to write myself, but I was thinking that they MUST be in here. Am I wrong? Thanks. -Frank

# Using Moving/Rolling Windows, here we do an OLS Regression with corrections for #Heteroscedasticity and Autocorrelations (HAC) using Newey West Method. This code is a #extension of Ajay Shah?s code for moving windows simple OLS regression. # The easiest way to adjust for Autocorrelations and Heteroscedasticity in the OLS residuals is to #use the coeftest function that is included in the

I think it is simply, but I cannot find the method to figure out "skewness". Thanks! [[alternative HTML version deleted]]

Dear R users, I am conducting multiple comparisons among 12 groups (after a significant F-test) that are heteroscedastic (as judged by a significant Levene's test). It seems from the literature that the Games-Howell post-hoc test is the most appropriate for these data - but, I can't seem to locate a function on the R-site or CRAN or the help lists that implements this test in any R

Dear all, Right now, I'm doing research about outlier in statistical data (univariate and multivariate) and I want to simulate its behavior. My problem is : How to generate random data from distribution with zero skewness and some kurtosis values in R ? A. Kudus ===================== Dept. of Statistics Bandung Islamic University I n d o n e s i a ==========================

Dear R-Users and Developpers, Currently R does not include functions to compute the skewness and kurtosis. I programmed it myself in the following way, but probably *real* programmers/statisticians can do that better: mykurtosis <- function(x) { m4 <- mean((x-mean(x))^4) kurt <- m4/(sd(x)^4)-3 kurt } myskewness <- function(x) { m3 <- mean((x-mean(x))^3) skew <-