Evaluation of Linear Trend Tests Using Resampling Techniques

Monte Carlo Evaluation of Resampling-Based Hypothesis Tests

Monte Carlo estimation of the power of tests that require resampling can be very com-putationally intensive. It is possible to reduce the size of the inner resampling loop as long as the resulting estimator of power can be corrected for bias. A simple linear extrapolation method is shown to perform well in correcting for bias and thus reduces computation time in Monte Carlo power studies.

Resampling Time Series Using Missing Values Techniques

Abst rac t . Several techniques for resampling dependent data have already been proposed. In this paper we use missing values techniques to modify the moving blocks jackknife and bootstrap. More specifically, we consider the blocks of deleted observations in the blockwise jackknife as missing data which are recovered by missing values estimates incorporating the observation dependence structure...

Extending the Use of Some Traditional Trend Tests for Repairable Systems Data by Resampling Te hniques

hazard evaluation of gas condensate stabilization and dehydration unit of parsian gas refinery using hazop procedures

شناسایی مخاطرات در واحد 400 پالایشگاه گاز پارسیان. در این پروزه با بکارگیری از تکنیک hazop به شناسا یی مخاطرات ، انحرافات ممکن و در صورت لزوم ارایه راهکارهای مناسب جهت افزایش ایمنی فرا یند پرداخته میگردد. شرایط عملیاتی مخاطره آمیز نظیر فشار و دمای بالا و وجود ترکیبات مختلف سمی و قابل انفجار در واحدهای پالایش گاز، ضرورت توجه به موارد ایمنی در این چنین واحدهایی را مشخص می سازد. مطالعه hazop یک ر...

Efficient p-value evaluation for resampling-based tests.

The resampling-based test, which often relies on permutation or bootstrap procedures, has been widely used for statistical hypothesis testing when the asymptotic distribution of the test statistic is unavailable or unreliable. It requires repeated calculations of the test statistic on a large number of simulated data sets for its significance level assessment, and thus it could become very comp...