%0 Conference Paper %1 SchrausserRG.2.2.19532.69768 %A Schrausser, D. G. %B 41. Kongreß der Deutschen Gesellschaft für Psychologie %C Dresden, Deutschland %D 1998 %I Technische Universität Dresden %K Bootstrap Permutationtests Probability Simulation Statistics myown %R 10.13140/RG.2.2.19532.69768 %T Die Permutationsmethode: Voraussetzungsfrei testen %U http://dx.doi.org/10.13140/RG.2.2.19532.69768 %X The permutation method, a computationally intensive method for statistical inference, is presented using the example of the 2-sample problem (t-test paradigm). In the permutation test, the probability of parameter differences is defined by comparing a characteristic value (e.g.: t-value) with a distribution of characteristic values that was generated by permutation of the raw data. The superiority of the permutation method, which, in contrast to the asymptotic approximation, makes no demands on (a) distribution, (b) variances or (c) sample size due to this procedure, is demonstrated in the case of (a) extremely small or (b) extremely different sample sizes using numerical simulations and empirical examples demonstrated. The areas of application of the permutation method are discussed.

@inproceedings{SchrausserRG.2.2.19532.69768, abstract = {The permutation method, a computationally intensive method for statistical inference, is presented using the example of the 2-sample problem (t-test paradigm). In the permutation test, the probability of parameter differences is defined by comparing a characteristic value (e.g.: t-value) with a distribution of characteristic values that was generated by permutation of the raw data. The superiority of the permutation method, which, in contrast to the asymptotic approximation, makes no demands on (a) distribution, (b) variances or (c) sample size due to this procedure, is demonstrated in the case of (a) extremely small or (b) extremely different sample sizes using numerical simulations and empirical examples demonstrated. The areas of application of the permutation method are discussed.}, added-at = {2023-09-07T14:58:25.000+0200}, address = {Dresden, Deutschland}, author = {Schrausser, D. G.}, biburl = {https://www.bibsonomy.org/bibtex/20dc677064a25984ab30789d3ec965dc6/schrausser}, booktitle = {41. Kongreß der Deutschen Gesellschaft für Psychologie}, doi = {10.13140/RG.2.2.19532.69768}, interhash = {74dc7c67199068bac255f901cfe7d01f}, intrahash = {0dc677064a25984ab30789d3ec965dc6}, keywords = {Bootstrap Permutationtests Probability Simulation Statistics myown}, language = {de}, month = {09}, publisher = {Technische Universität Dresden}, timestamp = {2023-09-07T15:00:02.000+0200}, title = {Die Permutationsmethode: Voraussetzungsfrei testen}, url = {http://dx.doi.org/10.13140/RG.2.2.19532.69768}, year = 1998 }