The Experts below are selected from a list of 282 Experts worldwide ranked by ideXlab platform
Stephen J. Finch - One of the best experts on this subject based on the ideXlab platform.
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Randomized rejection procedure for the two-sample Kolmogorov–Smirnov statistic
Computational Statistics & Data Analysis, 2004Co-Authors: Raymond N. Greenwell, Stephen J. FinchAbstract:Abstract The two-sample Kolmogorov–Smirnov test is unable to achieve an arbitrary probability of Type I error because it can only take on a limited number of discrete values. We offer a randomized procedure that achieves any specified value of α. We derive formulas for approximating the achievable p-values immediately above and below the desired value of α. For the value of the statistic corresponding to the p-value greater than α, our procedure rejects the null hypothesis randomly with probability sufficient to achieve the specified α. Such a procedure is particularly appropriate for simulation studies. Our procedure leads to a different Continuity Correction than the one proposed by Kim (J. Amer. Statist. Assoc. 64 (1969) 1625), using the criterion that the Continuity Correction should cause the randomized procedure to reject the null hypothesis with probability α.
Raymond N. Greenwell - One of the best experts on this subject based on the ideXlab platform.
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Randomized rejection procedure for the two-sample Kolmogorov–Smirnov statistic
Computational Statistics & Data Analysis, 2004Co-Authors: Raymond N. Greenwell, Stephen J. FinchAbstract:Abstract The two-sample Kolmogorov–Smirnov test is unable to achieve an arbitrary probability of Type I error because it can only take on a limited number of discrete values. We offer a randomized procedure that achieves any specified value of α. We derive formulas for approximating the achievable p-values immediately above and below the desired value of α. For the value of the statistic corresponding to the p-value greater than α, our procedure rejects the null hypothesis randomly with probability sufficient to achieve the specified α. Such a procedure is particularly appropriate for simulation studies. Our procedure leads to a different Continuity Correction than the one proposed by Kim (J. Amer. Statist. Assoc. 64 (1969) 1625), using the criterion that the Continuity Correction should cause the randomized procedure to reject the null hypothesis with probability α.
Alan S. Rigby - One of the best experts on this subject based on the ideXlab platform.
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Statistical methods in epidemiology. VII. An overview of the χ 2 test for 2×2 contingency table analysis.
Disability and rehabilitation, 2001Co-Authors: Alan S. RigbyAbstract:Purpose: The odds ratio is an appropriate method of analysis for data in 2 2 2 contingency tables. However, other methods of analysis exist. One such method is based on the h 2 test of goodness-of-fit. Key players in the development of statistical theory include Pearson, Fisher and Yates. Method: Data are presented in the form of 2 2 2 contingency tables and a method of analysis based on the h 2 test is introduced. There are many variations of the basic test statistic, one of which is the h 2 test with Yates' Continuity Correction. The usefulness (or not) of Yates' Continuity Correction is discussed. Problems of interpretation when the method is applied to k 2 m tables are highlighted. Results: Some properties of the h 2 the test are illustrated by taking examples from the author's teaching experiences. Conclusion: Journal editors should be encouraged to give both observed and expected cell frequencies so that better information comes out of the h 2 test statistic.
Mario Steinberg - One of the best experts on this subject based on the ideXlab platform.
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A matched asymptotic expansions approach to Continuity Corrections for discretely sampled options. Part 1: barrier options.
Applied Mathematical Finance, 2007Co-Authors: Sam Howison, Mario SteinbergAbstract:We discuss the ‘Continuity Correction’ that should be applied to relate the prices of discretely sampled barrier options and their continuouslysampled equivalents. Using a matched asymptotic expansions approach we show that the Correction of Broadie, Glasserman & Kou (Mathematical Finance 7, 325 (1997)) can be applied in a very wide variety of cases. We calculate the Correction to higher order in terms of the expansion parameter (the scaled time between resets) and we show how to apply the Correction in jump-diusion and local volatility models.
Paul C. Lambert - One of the best experts on this subject based on the ideXlab platform.
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What to add to nothing? Use and avoidance of Continuity Corrections in meta‐analysis of sparse data
Statistics in medicine, 2004Co-Authors: Michael J. Sweeting, Alex J. Sutton, Paul C. LambertAbstract:Objectives: To compare the performance of different meta-analysis methods for pooling odds ratios when applied to sparse event data with emphasis on the use of Continuity Corrections. Background: Meta-analysis of side effects from RCTs or risk factors for rare diseases in epidemiological studies frequently requires the synthesis of data with sparse event rates. Combining such data can be problematic when zero events exist in one or both arms of a study as Continuity Corrections are often needed, but, these can influence results and conclusions. Methods: A simulation study was undertaken comparing several meta-analysis methods for combining odds ratios (using various classical and Bayesian methods of estimation) on sparse event data. Where required, the routine use of a constant and two alternative Continuity Corrections; one based on a function of the reciprocal of the opposite group arm size; and the other an empirical estimate of the pooled effect size from the remaining studies in the meta-analysis, were also compared. A number of meta-analysis scenarios were simulated and replicated 1000 times, varying the ratio of the study arm sizes. Results: Mantel–Haenszel summary estimates using the alternative Continuity Correction factors gave the least biased results for all group size imbalances. Logistic regression was virtually unbiased for all scenarios and gave good coverage properties. The Peto method provided unbiased results for balanced treatment groups but bias increased with the ratio of the study arm sizes. The Bayesian fixed effect model provided good coverage for all group size imbalances. The two alternative Continuity Corrections outperformed the constant Correction factor in nearly all situations. The inverse variance method performed consistently badly, irrespective of the Continuity Correction used. Conclusions: Many routinely used summary methods provide widely ranging estimates when applied to sparse data with high imbalance between the size of the studies' arms. A sensitivity analysis using several methods and Continuity Correction factors is advocated for routine practice. Copyright 2004 John Wiley & Sons, Ltd.