A vast area which includes generating results from computer models.

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Intraclass correlation coefficient in Bayesian statistics

I need some references about intraclass correlation coefficient in Bayesian statistics and hypothesis testing. I already take a look in A. Gelman, J.B. Carlin, H.S. Stern and D.B. Rubin, Bayesian ...
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9 views

Which methods do people use to understand queueing networks?

Queueing networks can be analyzed through analytic results (in some cases), approximation methods or simulation (discrete-event simulation, system dynamics). Analytic solutions do not exist in ...
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19 views

Using ABC package from R cran with a C++ simulator [on hold]

I developed a simulator in c++ and I would like to use the R Cran package "ABC" from Csillery et al with that simulator. There seem to be many ways to make the two programs interact, but what would ...
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7 views

Simulating qualitative interaction in survival analysis

I am trying to simulate the survival data that can fit the model: $$h(x) = ho(x) exp (a_0*Treatment + a_1*Treatment*x_1 + a_2*Treatment*x_2)$$ Whereas treatment is an binary variable (0 = control ...
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2answers
63 views

Rule of Succession for Unfair Coin

Given the first n flip results from an unfair coin, we wish to estimate the probability that the next flip is a heads. I can take 2 approaches to this: ...
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1answer
45 views

Find 'p' in geometric distribution when 'P(X)' and 'k' are known

This may be a bit more of an advanced algebra question, but here goes: I'm trying to use the equation for a geometric distribution P(X) = p(1-p)^k to find the ...
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7 views

Simulating interaction term in Cox model

I am trying to simulate the survival data (by using Weibull distribution) that can fit the Cox model below: h(t) = ho(t). exp(beta1 * X1 + beta2 * X1 * X2) X1 and X2 are binary. I haved tried using ...
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5 views

Fully identified moment matching with simulated method of moments

I am struggling with some results regarding the model fit in this paper (p. 49). The authors set up a simulated method of moment estimator, using 8 different moments to match and 8 different ...
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19 views

Monte Carlo vs simulation in GARCH (package “rugarch” in R)

What is the difference between a GARCH simulation and a GARCH Monte Carlo simulation? I look in the vingette for the "rugarch" package in R, Introduction to Rugarch. In section 6 Simulation on page ...
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16 views

How to generate new random variables after using PCA for dimension reduction?

I want to be able to generate random variables, that (more or less) match the distribution of some observed data set. The data set is high dimension and I have reduced the dimension using PCA. Only ...
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49 views

If a sample of 1000 observations were simulated for this model then how many values would be greater than x? [closed]

Say my model is $ y_t=0.5y_{t-1}+x_t+v_{1t} $ and $ x_t=0.5x_{t-1}+v_{2t} $ (0.5 was chosen arbitrarily from interval (0,1) Suppose $ v_{1t} $, $ v_{2t} $ follow IID normal dist on (0,1). With ...
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27 views

Indirect solution for maximum entropy through sampling?

Is there a way to sample from a finite set $\{A,B,C,D\}$ such that the limiting empirical proportions converges to the maximum entropy solution of their probabilities consistent with known ...
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13 views

Simulation and visualization libraries for reinforcement learning in python?

I am aware of keras, block n a few others Python libraries for nn which do RL among others. But is there a library than can make the task of visualizations easy? In terms of 3D model of ...
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2answers
32 views

Simulate 1000 samples from a bivariate normal distribution

I know the code to generate two correlated variables (r=0.5), for example with 100 numbers each: xy<-mvrnorm(100, mu=c(50,60), matrix(c(1,0.5,0.5,1),2)) But how can I simulate 1000 samples ...
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13 views

Multilevel Power of a Mixed Model

For my dissertation I have data from 57 employees that responded the same survey on 11 occasions (i.e., 11 observations per person for each variable). All variables I am interested in (and are ...
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13 views

How to aggregate many simulation runs for curve-fitting?

I have results from about 200 runs of a simulation model. The results contain a stochastic response variable which I want to approximate with a curve-fitting approach. So far, I have opted for ...
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22 views

alternative to the delta method for computing the variance & CI of products of probabilities

I'm trying to find a way to calculate the confidence interval of the product of probabilities without using the delta method or Program MARK. I am using a logistic-exposure model to estimate the ...
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3answers
304 views

Simulate a Bernoulli variable with probability ${a\over b}$ using a biased coin

Can someone tell me how to simulate $\mathrm{Bernoulli}\left({a\over b}\right)$, where $a,b\in \mathbb{N}$, using a coin toss (as many times as you require) with $P(H)=p$ ? I was thinking of using ...
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4 views

Simulating thousands of regressions and obtaining p-values [migrated]

I'm looking to do some basic simulation in R to examine the nature of p-values. My goal is to see whether large sample sizes trend towards small p-values. My thought is to generate random vectors of ...
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5 views

Evaluate (user-defined) variance estimators in simulation environment?

I'd like to examine how a variance estimator that I constructed for complex surveys behaves in simulation environments, in a manner similar (and perhaps much simpler) to what Li and Levy (2009) at the ...
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1answer
31 views

Estimating normalization constant with Monte Carlo integration

Be $f(x)$ a function. Suppose that $f(x)$ integrates to a finite value $k$: $$\int_{-\infty}^{\infty}f(x)dx=k$$ The normalization constant of $f(x)$ is $1/k$. Monte Carlo integration can give an ...
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1answer
19 views

Why use Coded Variables in Experimental Design?

Full-disclosure, I'm not sure if this ENTIRELY on-topic. Often times, most texts will suggest that we code our variables. i.e. If we move from 345 to 350 to 355, we code our variables as -1, 0, and ...
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12 views

Diffrence in required sample size for estimating a trend with GEE in longitudinal Data

I am currently working on GEE's for longitudinal data and the model I am using to simulate and evaluate the data is: $y_{ijk}=\beta_1+\beta_2\cdot j+\beta_3\cdot k+\beta_4 \cdot j \cdot ...
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14 views

regarding generating synthetic data simulating the real data

We are trying to develop some predictive models. The current scenario is that we have to rely on synthetic data at first since the real data set will not be available quite soon. It is understandable ...
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16 views

Efficiently sampling from Markov Chain with low-probability transitions

I need to sample a large number of paths from a Markov Chain with known state transition matrix $T$, where some of the state transitions are low probability (~0.01%). For example, I might have a large ...
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34 views

Endogeneity for logistic regression

I would like to construct a setup where there is endogeneity between a binary independent variable and (in the latent formulation) the logistic noise. The set up has to be easy to simulate and ...
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1answer
44 views

Question about accuracy in Monte Carlo integration

Suppose that we want to estimate the integral: $$\psi=\int_{a}^{b}h(x)dx.$$ Let $\hat{\psi}$ be the Monte Carlo estimator. As far as I know, if we desire an accuracy up to the fourth decimal, we need ...
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27 views

Obtain marginal CDF from joint CDF by simulation

How can I evaluate the marginal cumulative distribution function of a set of random variables for which I do not have the CDF in closed form. I can, however, simulate from a joint distribution ...
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2answers
217 views

fat-finger distribution

Brief question: Is there a fat-finger distribution? I'm sure that if it exists, then it has a different name. I don't know how to formulate it as an analytic function. Can you help me either find ...
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1answer
69 views

monte carlo simulation using exponential distributions

I'm trying to simulate a stochastic model of deterministic exponential population growth, where $dN/dt = rN$ where $N$ is population size and $r$ is rate ($t$ time). I'm assuming there's no carrying ...
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29 views

Why don't I get intervals which don't contain parameter by simulation?

I effect 100 simulations, and with a confidence level 95% I expected to get by simulation 5 coinfidence intervals approximately that not contain the paramater. I always get 100 confidence intervals ...
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63 views

Bootstrapping in Binary Response Data with Few Clusters and Within-Cluster Correlation

Beware: This is (almost) a cross-post to a thread I started on the Statalist but that has not received much attention so far. Introduction I am learning about the problems when conducting ...
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69 views

Simulate similar data to get around data sharing rules?

Sometimes there are very nice datasets that have very strict data sharing rules. This makes it difficult to legally share the data so that others can build upon them. I'm interesting in finding out ...
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22 views

Simulation of time series from pdf function, defined for each time step, with aucorrelation

I have a model defined by $\mathbf{X}= (X_{1}, X_{2},... X_{t} ... X_{M})$, where for each $t$ (time step) $X_{t}$ follow a distribution ${D(\alpha_{t}, \beta_{t} )}$. I want to generate time series ...
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13 views

How to make educated guess about movement of people through graph?

I have data about weekly counts of people on entry points (orange circles on the picture below) and need to make educated guess about their counts at destination points (marked by green stars). I know ...
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39 views

Is this sample drawn from the normal distribution ? using information from both mean and standard deviation

After correcting for a bug in my code in a previous question, I could simulate efficiently thousands of samples from the normal distribution N(0,1). I calculated the mean and standard deviation of ...
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1answer
85 views

Sampling 100000 times from normal distribution in R : strange distribution of samples' standard deviation

I generated, in R, one hundred thousand random samples of ten values from the normal distribution with mean zero and unit standard deviation, and registered each mean and standard deviation, in hope ...
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30 views

Monte Carlo Simulations: Can I Use Real Data as Universe?

In Monte Carlo simulations, it is a commonly used procedure to generate synthetic data based on a large survey (e.g. a microcensus) first. These synthetic data is then used as universe/population for ...
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57 views

How can we simulate from a geometric mixture?

If $f_1,\ldots,f_k$ are known densities from which I can simulate, i.e., for which an algorithm is available. and if the product $$\prod_{i=1}^k f_i(x)^{\alpha_i}\qquad \alpha_1,\ldots,\alpha_k>0$$ ...
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36 views

Applying Meta Analysis Findings to predict future outcome

Can Meta Analysis results be used to predict future outcome? If yes, How? I am reviewing a Meta Analysis Report ( I haven't prepared this report). It is about treatment of hypertension in elderly ...
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60 views

How to simulate from a t copula?

This is a question related to: How to simulate from a Gaussian copula? Suppose that I have two univariate marginal distributions, say $F$ and $G$, which I can simulate from. Now, construct their ...
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33 views

Simulating data from a given multivariate covariance matrix - workarounds for a non positive definite covariance matrix?

As part of a simulation study, I would like to create multivariate data that follow a specific covariance matrix in R. In this study I would like to be able to show that my algorithm is able to find ...
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17 views

Parametric bootstrap in generating returns and hypothesis testing

I am trying to test a hypothesis of a statistic calculated from portfolio returns. To do so I estimate a model on the original returns series and want to obtain 100 bootstrapped series using ...
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24 views

Approximating the conditional expectation in simulations

I am simulating stock returns, which are governed by the following equations $r_t = \mu + \delta r_{t-1} + \varepsilon_t$ $\sigma^2_t = \omega + \alpha \varepsilon_{t-1}^2 + \beta \sigma^2_{t-1}$ ...
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2answers
58 views

Generating data from KM curves

I have some survival data from different cohorts and can fit Kaplan Meier curves and cox proportional hazard models. I want to be able to simulate data that resembles the data found in say the KM ...
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21 views

Simulating a media coverage time series starting from a set of events

I want to simulate a time series of media coverage starting from a series of "events" of different "importance". I started generating the events, by applying to each day a very skewed inverse ...
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80 views

Simulating a stochastic integral

I am trying to solve exercise 3.9.10 on p. 66 of Ubbo F. Wiersema's "Brownian Motion Calculus" (John Wiley & Sons, 2008), which asks to simulate the stochastic integral $$ \int_0^1 B(t)\ dB(t) $$ ...
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18 views

Best way to measure how well stochastic models fits a system of differential equations?

I have a system of differential equations which can be easily solved using ode45 in matlab. The equations represent a biochemical pathway. I have simulated the same biochemical pathway ...
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18 views

How to implement MCMC sampling of a non-standard distribution

Given that I know the distribution to a constant. What sampling method? MH(SGMCMC) or slice sampling? More importantly, what tools/package can I use to impelement the sampling directly(input the ...
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49 views

Checking for Missingness mechanism

This code is for simulating missing mechanism randomly that I extracted it from imputeR package : ...