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Simon Jäger - One of the best experts on this subject based on the ideXlab platform.
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a Permutation Test for the regression kink design
Journal of the American Statistical Association, 2018Co-Authors: Peter Ganong, Simon JägerAbstract:The regression kink (RK) design is an increasingly popular empirical method for estimating causal effects of policies, such as the effect of unemployment benefits on unemployment duration. Using si...
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A Permutation Test for the Regression Kink Design
Journal of the American Statistical Association, 2018Co-Authors: Peter Ganong, Simon JägerAbstract:The Regression Kink (RK) design is an increasingly popular empirical method for causal inference. Analogous to the Regression Discontinuity design, which evaluates discontinuous changes in the level of an outcome variable with respect to the running variable at a point at which the level of a policy changes, the RK design evaluates discontinuous changes in the slope of an outcome variable with respect to the running variable at a kink point at which the slope of a policy with respect to the running variable changes. We document empirically that RK estimates are highly sensitive to nonlinearity in the underlying relationship between the outcome and the assignment variable. As an alternative to standard inference, we propose that researchers construct a distribution of placebo estimates in regions with and without a policy kink and use this distribution to gauge statistical significance. Under the assumption that the location of the kink point is random, this Permutation Test has exact size in finite samples for Testing a sharp null hypothesis of no effect of the policy on the outcome. In simulation studies with policy kinks, we find that statistical significance based on conventional standard errors may be spurious. In contrast, our Permutation Test has exact size even in the presence of non-linearity.
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a Permutation Test and estimation alternatives for the regression kink design
2014Co-Authors: Peter Ganong, Simon JägerAbstract:The Regression Kink (RK) design is an increasingly popular empirical method, with more than 20 studies circulated using RK in the last 5 years since the initial circulation of Card, Lee, Pei and Weber (2012). We document empirically that these estimates, which typically use local linear regression, are highly sensitive to curvature in the underlying relationship between the outcome and the assignment variable. As an alternative inference procedure, motivated by randomization inference, we propose that researchers construct a distribution of placebo estimates in regions without a policy kink. We apply our procedure to three empirical RK applications – two administrative UI datasets with true policy kinks and the 1980 Census, which has no policy kinks – and we find that statistical significance based on conventional p-values may be spurious. In contrast, our Permutation Test reinforces the asymptotic inference results of a recent Regression Discontinuity study and a Difference-in-Difference study. Finally, we propose estimating RK models with a modified cubic splines framework and Test the performance of different estimators in a simulation exercise. Cubic specifications – in particular recently proposed robust estimators (Calonico, Cattaneo and Titiunik 2014) – yield short interval lengths with good coverage rates.
Monjed H. Samuh - One of the best experts on this subject based on the ideXlab platform.
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applications of conditional power function of two sample Permutation Test
Computational Statistics, 2018Co-Authors: Monjed H. Samuh, Fortunato PesarinAbstract:Permutation or randomization Test is a nonparametric Test in which the null distribution (distribution under the null hypothesis of no relationship or no effect) of the Test statistic is attained by calculating the values of the Test statistic overall Permutations (or by considering a large number of random Permutation) of the observed dataset. The power of Permutation Test evaluated based on the observed dataset is called conditional power. In this paper, the conditional power of Permutation Tests is reviewed. The use of the conditional power function for sample size estimation is investigated. Moreover, reproducibility and generalizability probabilities are defined. The use of these probabilities for sample size adjustment is shown. Finally, an illustration example is used.
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More Powerful Permutation Test Based on Multistage Ranked Set Sampling
Communications in Statistics - Simulation and Computation, 2017Co-Authors: Lubna Amro, Monjed H. SamuhAbstract:AbstractMany researches have used ranked set sampling (RSS) method instead of simple random sampling (SRS) to improve power of some nonparametric Tests. In this study, the two-sample Permutation Test within multistage ranked set sampling (MSRSS) is proposed and investigated. The power of this Test is compared with the SRS Permutation Test for some symmetric and asymmetric distributions through Monte Carlo simulations. It has been found that this Test is more powerful than the SRS Permutation Test; its power increased by set size and/or number of cycles and/or number of stages. Symmetric distributions power increased better than asymmetric distributions power.
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Ranked Set Two-Sample Permutation Test
Statistica, 2017Co-Authors: Monjed H. SamuhAbstract:In this paper, ranked set two-sample Permutation Test of comparing two-independent groups in terms of some measure of location is presented. Three Test statistics are proposed. The statistical power of these new Test statistics are evaluated numerically. The results are compared with the statistical power of the usual two-sample Permutation Test under simple random sampling and with the classical independent two-sample t -Test.
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Some Advances in Permutation Testing
2011Co-Authors: Monjed H. SamuhAbstract:The main objective of this Ph.D thesis is to provide some advances in Permutation Testing within different fields of statistics. Mainly, the thesis is divided into four parts. First, the two notions of power function of Permutation Tests (conditional and unconditional) are reviewed. The use of empirical conditional power function for sample size estimation is investigated. Then, the notions of reproducibility probability and generalizability probability are defined within the Permutation framework. It is shown that the reproducibility and generalizability probabilities are important tools for sample size adjustment. Second, Permutation Tests with ranked set sampling are investigated. The effectiveness of ranked set sampling on the power of Permutation Tests is studied. Two-sample Permutation Test is considered as a guide. The power of the two-sample Permutation Test is computed for ranked set and simple random samples. It is shown that the Test for ranked set sample is more powerful than for simple random sample. Moreover, the effectiveness of the set size and number of cycles of ranked set sample is studied. It is shown that the power increased by the set size and/or the number of cycles. In addition, two Test statistics are proposed for ranked set sample and investigated under different kind of distributions (symmetric and asymmetric). Third, Permutation Tests in linear mixed model are investigated. Some Tests for a zero random effect variance component are reviewed and a new Permutation Test is proposed. Random intercept model is considered as a guide. The proposed Permutation Test has the correct nominal level of significance and is more powerful than the usual Tests based on a mixture of chi-square distributions. Moreover, the proposed Permutation Test is the fasTest, according to computing time, approach among those resampling-based Test approaches. Finally, Permutation Tests in cluster analysis is investigated. Tests for random agreement between two sets of clusters of a dataset are discussed. The adjusted Rand index is proposed as a Test statistic. Two Testing methods are proposed. The first method is based on the chi-square distribution assuming the cluster sizes within each set of clusters are equal. The second method is based on the Permutation approach. Comparison between these proposed methods is carried out in terms of empirical level of significance.
Peter Ganong - One of the best experts on this subject based on the ideXlab platform.
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a Permutation Test for the regression kink design
Journal of the American Statistical Association, 2018Co-Authors: Peter Ganong, Simon JägerAbstract:The regression kink (RK) design is an increasingly popular empirical method for estimating causal effects of policies, such as the effect of unemployment benefits on unemployment duration. Using si...
-
A Permutation Test for the Regression Kink Design
Journal of the American Statistical Association, 2018Co-Authors: Peter Ganong, Simon JägerAbstract:The Regression Kink (RK) design is an increasingly popular empirical method for causal inference. Analogous to the Regression Discontinuity design, which evaluates discontinuous changes in the level of an outcome variable with respect to the running variable at a point at which the level of a policy changes, the RK design evaluates discontinuous changes in the slope of an outcome variable with respect to the running variable at a kink point at which the slope of a policy with respect to the running variable changes. We document empirically that RK estimates are highly sensitive to nonlinearity in the underlying relationship between the outcome and the assignment variable. As an alternative to standard inference, we propose that researchers construct a distribution of placebo estimates in regions with and without a policy kink and use this distribution to gauge statistical significance. Under the assumption that the location of the kink point is random, this Permutation Test has exact size in finite samples for Testing a sharp null hypothesis of no effect of the policy on the outcome. In simulation studies with policy kinks, we find that statistical significance based on conventional standard errors may be spurious. In contrast, our Permutation Test has exact size even in the presence of non-linearity.
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a Permutation Test and estimation alternatives for the regression kink design
2014Co-Authors: Peter Ganong, Simon JägerAbstract:The Regression Kink (RK) design is an increasingly popular empirical method, with more than 20 studies circulated using RK in the last 5 years since the initial circulation of Card, Lee, Pei and Weber (2012). We document empirically that these estimates, which typically use local linear regression, are highly sensitive to curvature in the underlying relationship between the outcome and the assignment variable. As an alternative inference procedure, motivated by randomization inference, we propose that researchers construct a distribution of placebo estimates in regions without a policy kink. We apply our procedure to three empirical RK applications – two administrative UI datasets with true policy kinks and the 1980 Census, which has no policy kinks – and we find that statistical significance based on conventional p-values may be spurious. In contrast, our Permutation Test reinforces the asymptotic inference results of a recent Regression Discontinuity study and a Difference-in-Difference study. Finally, we propose estimating RK models with a modified cubic splines framework and Test the performance of different estimators in a simulation exercise. Cubic specifications – in particular recently proposed robust estimators (Calonico, Cattaneo and Titiunik 2014) – yield short interval lengths with good coverage rates.
Tim Friede - One of the best experts on this subject based on the ideXlab platform.
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a studentized Permutation Test for three arm trials in the gold standard design
Statistics in Medicine, 2017Co-Authors: Tobias Mutze, Frank Konietschke, Axel Munk, Tim FriedeAbstract:The 'gold standard' design for three-arm trials refers to trials with an active control and a placebo control in addition to the experimental treatment group. This trial design is recommended when being ethically justifiable and it allows the simultaneous comparison of experimental treatment, active control, and placebo. Parametric Testing methods have been studied plentifully over the past years. However, these methods often tend to be liberal or conservative when distributional assumptions are not met particularly with small sample sizes. In this article, we introduce a studentized Permutation Test for Testing non-inferiority and superiority of the experimental treatment compared with the active control in three-arm trials in the 'gold standard' design. The performance of the studentized Permutation Test for finite sample sizes is assessed in a Monte Carlo simulation study under various parameter constellations. Emphasis is put on whether the studentized Permutation Test meets the target significance level. For comparison purposes, commonly used Wald-type Tests, which do not make any distributional assumptions, are included in the simulation study. The simulation study shows that the presented studentized Permutation Test for assessing non-inferiority in three-arm trials in the 'gold standard' design outperforms its competitors, for instance the Test based on a quasi-Poisson model, for count data. The methods discussed in this paper are implemented in the R package ThreeArmedTrials which is available on the comprehensive R archive network (CRAN). Copyright © 2016 John Wiley & Sons, Ltd.
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a studentized Permutation Test for three arm trials in the gold standard design
arXiv: Applications, 2016Co-Authors: Tobias Mutze, Frank Konietschke, Axel Munk, Tim FriedeAbstract:The 'gold standard' design for three-arm trials refers to trials with an active control and a placebo control in addition to the experimental treatment group. This trial design is recommended when being ethically justifiable and it allows the simultaneous comparison of experimental treatment, active control, and placebo. Parametric Testing methods have been studied plentifully over the past years. However, these methods often tend to be liberal or conservative when distributional assumptions are not met particularly with small sample sizes. In this article, we introduce a studentized Permutation Test for Testing non-inferiority and superiority of the experimental treatment compared to the active control in three-arm trials in the `gold standard' design. The performance of the studentized Permutation Test for finite sample sizes is assessed in a Monte-Carlo simulation study under various parameter constellations. Emphasis is put on whether the studentized Permutation Test meets the target significance level. For comparison purposes, commonly used Wald-type Tests are included in the simulation study. The simulation study shows that the presented studentized Permutation Test for assessing non-inferiority in three-arm trials in the 'gold standard' design outperforms its competitors for count data. The methods discussed in this paper are implemented in the R package ThreeArmedTrials which is available on the comprehensive R archive network (CRAN).
Axel Munk - One of the best experts on this subject based on the ideXlab platform.
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a studentized Permutation Test for three arm trials in the gold standard design
Statistics in Medicine, 2017Co-Authors: Tobias Mutze, Frank Konietschke, Axel Munk, Tim FriedeAbstract:The 'gold standard' design for three-arm trials refers to trials with an active control and a placebo control in addition to the experimental treatment group. This trial design is recommended when being ethically justifiable and it allows the simultaneous comparison of experimental treatment, active control, and placebo. Parametric Testing methods have been studied plentifully over the past years. However, these methods often tend to be liberal or conservative when distributional assumptions are not met particularly with small sample sizes. In this article, we introduce a studentized Permutation Test for Testing non-inferiority and superiority of the experimental treatment compared with the active control in three-arm trials in the 'gold standard' design. The performance of the studentized Permutation Test for finite sample sizes is assessed in a Monte Carlo simulation study under various parameter constellations. Emphasis is put on whether the studentized Permutation Test meets the target significance level. For comparison purposes, commonly used Wald-type Tests, which do not make any distributional assumptions, are included in the simulation study. The simulation study shows that the presented studentized Permutation Test for assessing non-inferiority in three-arm trials in the 'gold standard' design outperforms its competitors, for instance the Test based on a quasi-Poisson model, for count data. The methods discussed in this paper are implemented in the R package ThreeArmedTrials which is available on the comprehensive R archive network (CRAN). Copyright © 2016 John Wiley & Sons, Ltd.
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a studentized Permutation Test for three arm trials in the gold standard design
arXiv: Applications, 2016Co-Authors: Tobias Mutze, Frank Konietschke, Axel Munk, Tim FriedeAbstract:The 'gold standard' design for three-arm trials refers to trials with an active control and a placebo control in addition to the experimental treatment group. This trial design is recommended when being ethically justifiable and it allows the simultaneous comparison of experimental treatment, active control, and placebo. Parametric Testing methods have been studied plentifully over the past years. However, these methods often tend to be liberal or conservative when distributional assumptions are not met particularly with small sample sizes. In this article, we introduce a studentized Permutation Test for Testing non-inferiority and superiority of the experimental treatment compared to the active control in three-arm trials in the `gold standard' design. The performance of the studentized Permutation Test for finite sample sizes is assessed in a Monte-Carlo simulation study under various parameter constellations. Emphasis is put on whether the studentized Permutation Test meets the target significance level. For comparison purposes, commonly used Wald-type Tests are included in the simulation study. The simulation study shows that the presented studentized Permutation Test for assessing non-inferiority in three-arm trials in the 'gold standard' design outperforms its competitors for count data. The methods discussed in this paper are implemented in the R package ThreeArmedTrials which is available on the comprehensive R archive network (CRAN).