Carlo Integration Method Research Articles

Bayesian optimal designs for cox regression model with random and nonrandom intercept based on type I censored data

In this article, we aim to develop A and D-optimal designs for censored data via Bayesian strategy. In this regard, based on some different sorts of prior distributions for the unknown parameters, the appropriate designs are calculated. In the sequel, we define the random intercept model for the Cox regression model assuming the random intercept distributed normally. It is observed that the likelihood function of the observations cannot be acquired in a closed form. Thus, we obtain the quasi-information matrix based on the quasi-likelihood function. We then, use this matrix to obtain the A and D-optimal designs. It is seen that the Fisher information matrix depends on the unknown parameters as a result the locally optimal designs will be inefficient when the true guess about the parameters is failed. To overcome this problem, we propose the Bayesian strategy to obtain more robust optimal designs. Some numerical techniques such as Monte Carlo integration and numerical optimization methods are implemented to calculate the designs. Further, the generalized equivalence theorem is adapted to confirm the optimality of the proposed designs. All of the numerical results are done with R software. The results of this article will help to remove the weakness of the locally optimal designs that have been addressed in Schmidt and Schwabe when the parameters are misspecfied.

Spreading sequences for asynchronous mc-cdma revisited: accurate bit error rate analysis

The bit error rate (BER) of an asynchronous multicarrier code-division multiple-access system in an additive white Gaussian noise channel is evaluated using Monte Carlo integration and moment-generating function methods. The BER performance for different families of spreading sequences is investigated. Numerical results show that the approach of a recently published paper (see Popovic, B.M., IEEE Trans. Common., vol.47, p.918-26, 1999) cannot give an accurate BER because the interference from other subcarriers has been omitted. Some new findings about the performance of different sequences are also presented.

Quasi-monte carlo methods for numerical integration of multivariate Haar series

Quasi-monte carlo methods for numerical integration of multivariate Haar series

Review of the Simulation of Transient Experiments on Amorphous Silicon Hydride

ABSTRACTThe transient characteristics of amorphous silicon hydride (a-Si:H) devices are of great importance as a means of studying the fundamental electrical and photo-electrical processes in the material. To assist in the interpretation of these experiments, several groups have performed simulations to understand better the meaning of transient waveforms. Simulations begin with hypotheses of how the processes of interest affect each other and affect the measured quantity, and then calculate an expected experimental result. Transient experiments on a-Si:H have been simulated by a variety of techniques, including the Monte Carlo, Laplace transform, and time integration methods. Of these methods, time integration is the most powerful but also the most demanding on computer capabilities. Fortunately, recent advances in computer technology have brought the simulation of transient experiments on a-Si:H by time integration within the capabilities of advanced personal computers.