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completely new sequential importance sampling estimator of the desired tail probability. Numerical experiments suggest that the … sequential importance sampling estimator can be significantly more efficient than its competitor. …
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With panel data important issues can be resolved that can not beaddressed with cross--sectional data. A major drawback is that paneldata suffer from more severe missing data problems. Adding a sampleconsisting of new units randomly drawn from the original populationas replacements for units who...
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We investigate changes in the time series characteristics of postwar U.S. inflation. In a model-based analysis the conditional mean of inflation is specified by a long memory autoregressive fractionally integrated moving average process and the conditional variance is modelled by a stochastic...
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evaluating the likelihood, two of the methods rely on Monte Carlo integration with importance sampling techniques. The third …
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importance sampling or the independence chain Metropolis-Hastings algorithm for posterior analysis. A comparative analysis is … appropriately yet quickly tuned candidate, straightforward importance sampling provides the most efficient estimator of the marginal …
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