introduce two novel methods for computationally efficient simulation: Conditional MitISEM, a Markov chain Monte Carlo method to … simulate model parameters from the Partially Censored Posterior, and PCP-QERMit, an Importance Sampling method that is … provide more information about the left tail of the distribution of the standardized innovations. Extensive simulation and …

: Plain or crude Monte Carlo simulation (CMC) is commonly applied for estimating multiperiod tail risk measures such as …. To overcome this shortcoming, we propose a sequential importance sampling, which is a modification of CMC. In the … proposed method. The sampling distribution of innovations is chosen differently from the estimated conditional distribution of …

Recently it was shown that the estimated American call prices obtained with regression and simulation based methods can …

(UMVUE) and a Maximum Likelihood Estimator (MLE) of the two measures of reliability, namely R(t) = P(X > t) and P = P(X > Y … ) under Type II censoring scheme and sampling scheme of Bartholomew (1963). We also develop interval estimates of the … reliability measures. A comparative study of the different methods of point estimation has been conducted on the basis of …

dependence state we compute, by Monte Carlo simulation, trajectories of the Markov chain process and derive relevant information …

MSE is usually estimated using the parametric bootstrap method. Model-based simulation studies of the properties of the …

This paper proposes a new method for pricing American options that uses importance sampling to reduce estimator bias … and variance in simulation-and-regression based methods. Our suggested method uses regressions under the importance …. Our numerical results show that this method successfully reduces the bias plaguing the standard importance sampling method …