Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: Quantiles are often used in risk evaluation of complex systems. In some situations, as in regulations regarding safety analyses of nuclear por plants, a confidence interval is required for the quantile of the simulation's output variable. In our current paper,  develop methods to construct confidence intervals for quantiles when applying Latin hypercube sampling, a variance reduction technique that extends stratification for sampling in higher dimensions. Our approaches employ the batching and sectioning methods when applying replicated Latin hypercube sampling, with a single Latin hypercube sample in each batch, and samples across batches are independent.  have established the asymptotic validity of the confidence intervals developed in this paper. Moreover,  have proven that quantile estimators from a single Latin hypercube sample and replicated Latin hypercube samples satisfy ak Bahadur representations. An advantage of sectioning over batching is that the sectioning confidence interval typically has better coverage, which  observe in numerical experiments.
['Hui Dong', 'Marvin K. Nakayama']
I am writing a research paper on a idea Constructing confidence intervals for a quantile using batching and sectioning when applying latin hypercube sampling
[{"from": "human", "value": "I am writing a research paper on a idea Constructing confidence intervals for a quantile using batching and sectioning when applying latin hypercube sampling"}, {"from": "gpt", "value": "Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: Quantiles are often used in risk evaluation of complex systems. In some situations, as in regulations regarding safety analyses of nuclear por plants, a confidence interval is required for the quantile of the simulation's output variable. In our current paper,  develop methods to construct confidence intervals for quantiles when applying Latin hypercube sampling, a variance reduction technique that extends stratification for sampling in higher dimensions. Our approaches employ the batching and sectioning methods when applying replicated Latin hypercube sampling, with a single Latin hypercube sample in each batch, and samples across batches are independent.  have established the asymptotic validity of the confidence intervals developed in this paper. Moreover,  have proven that quantile estimators from a single Latin hypercube sample and replicated Latin hypercube samples satisfy ak Bahadur representations. An advantage of sectioning over batching is that the sectioning confidence interval typically has better coverage, which  observe in numerical experiments."}]