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So $p \sim truncated~Normal$ in $[0,1]$; does this make sense? $\mu_1, \mu_2, \sigma \sim Normal$ but how do I avoid negative $\sigma$ ? …

If the lognormal distribution is truncated to $(1, \infty)$ so that the normal distribution is truncated to $(0,\infty),$ then the natural log of that "normal" distribution exists. … The distribution $\mathsf{Norm}(50, 2)$ has almost no probability below $0,$ so the truncation would have little practical effect in this example. (2) R code for the figure above: par(mfrow=c(1,3)) hist …

Basically, I would like to generate a sample of truncated lognormal distribution given mean, variance, lower bound and upper bound. … Note that the mean and variance here are the mean and variance of the truncated lognormal distribution NOT the $\mu$ and $\sigma$. …

(No, truncating accuracy at zero is not a good idea.) Model fitting relies on minimizing errors, which is often done using numerical optimizers that use first or second derivatives. … It is the (-1)-median of the time series (Gneiting, 2011, p. 752 with $\beta=-1$), which in the specific case of a lognormal distribution happens to coincide with the mode of the distribution. …

I am trying to implement Gompertz as an AFT model to get a model with exponential hazards, and since my data is atypically censored and truncated, I have given up trying to find an R package and just doing … Survival analysis, techniques for censored and truncated data, John P. Klein & Melvin L. …

legendtext = plot.legend) title(paste("For Hour", i), outer = TRUE) } # clearly a normal isn't the way to go - better would probably be a # weibull or GPD, but this serves as a good example # maybe a truncated … this seems wrong # I've seen this no matter my selectin of distribution and # (archimedean) copula # so far I've tried fitting truncated normal, weibull, truncated # lognormal, truncated logis, # gamma …

In his thesis comparing BRTs to GAms & GLMs, Abeare uses gaussian, but only after removing the zeroes (i.e. zero truncated). …

To estimate the parameters of a truncated distribution (lognormal for example), we can use the Maximum Likelihood Estimation or Method of Moments. … For the Method of Moments Estimation, one needs to write down the mathematical expression of the expected value of the truncated lognormal distribution. Is it possible to do so? …

I guess I can use the truncated lognormal distribution. I have the following questions regarding truncated lognormal distribution What are the PDF and CDF for truncated lognormal distribution. … What aspects must I consider while working with truncated distributions. If censoring the data points above the physical limit a senseful thing to do mathematically speaking. …

What are the PDF and CDF for truncated lognormal distribution. Offhand, I don't know the CDF of a truncated log-normal either. But let me take a guess. Start with a log-normal distribution. … What aspects must I consider while working with truncated distributions. …

f(\lambda;\mu,\sigma) d\lambda} $$ We can't observe zeroes so, we work with a truncated distribution to then construct a likelihood function and use the MLE frequentist estimator to obtain point estimates … The truncated PLN distribution is: $$ p_{T}(x;\mu,\sigma) = \frac{p(x;\mu,\sigma)}{p(x>0;\mu,\sigma)} = \frac{p(x;\mu,\sigma)}{1-p(0;\mu,\sigma)} $$ The conditioning is done based on the total probability …

We know that $X$ follows a lognormal distribution with parameters $\mu$ and $\sigma$. How do I estimate $\mu$ and $\sigma$ with this censored data, with arbitrary upper bounds $\{c_i\}_{i=1}^k$? … Suppose the distribution of $X$ is a truncated lognormal and that we know that the support of $X$ is $[a,b]$. …

This seems to be similar to fitting a truncated distribution but not sure. … UPDATE ** In response to Glen B's comment that this may be fitting a truncated lognormal distribution, I'd like to clarify that the exact question is why do poweRlaw and fitdistrplus differ in fitted lognorm …

I have data that is truncated on the left and censored on the right. The reason is that this is claims data, which for a claim gives the amount of the claim. … So far I use the python library Fitter, but it does not allow to fit truncated or censored distributions. Is there another library (python, R or other) that allows to do this? …

loss function: loss = $(desired\ mean - F(\mu, \sigma, x_l, x_u))^2 + (desired\ std - G(\mu, \sigma, x_l, x_u))^2$ Where the function F and G is the analytical formula of mean and standard deviation of truncated … lognormal distribution. …