1,721,030 research outputs found
Comparative evaluation of external effectiveness of two University Faculties through nonparametric heterogeneity tests
No full textTesting for anomalies in intertemporal choice: a nonparametric approach
No full textWe propose a nonparametric method to test for anomalies in IC, based on combined permutation tests, which overcomes these limitations. It is robust because based on less stringent assumptions than the cited methods. A simulation study proves the good performance of the proposed method
Metodi Non Parametrici per l'Analisi di Performance con Applicazioni alla Valutazione del Sistema Universitario
No full textNonparametric directional tests in presence of confounding factors and categorical data
No full textIn modern socio-economic systems, often the aim of a performance analysis or quality evaluation is to compare different products, different manufacturing plants or service centres, different actions or distinct treatments. The question is, ‘‘Which is better?’’ This is complicated because the considered aspects are often measured through categorical data and the results can be affected by confounding factors. To solve this problem we discuss some directional permutation tests based on the nonparametric combination of dependent permutation tests (NPC) for two-sample comparisons in the presence of ordinal categorical variables and confounding factors. In particular we present a new permutation test based on the combination of a finite number of sample moments. To reduce the confounding effects we consider the joint application of stratification and the NPC method. We also show the results of Monte Carlo simulations in order to compare permutation solutions with other nonparametric tests and to evaluate the robustness of the test based on moments
Permutation tests for heterogeneity comparisons in presence of categorical variables with application to university evaluation
No full textIn social sciences researchers often meet the problem of determining if the distribution of a categorical variable is more concentrated in population X1 than in population X2. For example the effectiveness of two different PhD programs can be evaluated in terms of the heterogeneity of the set of job opportunities. The job opportunities are nominal categorical variables and populations X1 and X2 include all PhD holders for program 1 and program 2. We may define that a PhD program is better than another if it is able to offer a larger variety of job opportunities. Several other examples can be mentioned to highlight the importance of heterogeneity comparison problems in social sciences; moreover this problem occurs also very often in genetics, biology, medical studies and other sciences. The nonparametric solution of this problem has similarities to that of permutation testing for stochastic dominance on ordered categorical variables, i.e. testing under order restrictions. If ordering of probability parameters in H0 is unknown and it has to be estimated by sampling data, only approximate nonparametric solutions are possible within the permutation approach. Main properties of test solutions and some Monte Carlo simulations in order to evaluate the tests behaviour under H0 and H1, will be presented. A real problem concerned with University evaluation is also discussed
Analisi statistica degli incidenti stradali nelle province di Padova e Ferrara: caratteristiche ed evoluzione temporale del fenomeno
No full textAdvances on inferential methods for heterogeneity comparisons
No full textThe purpose of the present work consists in the study of a non-parametric
procedure for a two sample test for heterogeneity comparisons in presence of categorical
variables. In particular a comparison between the approximated permutation solution
proposed by Arboretti et al. (2009) with a new proposal based on a bootstrap
resampling method is performed. Some results of the simulation study for analyzing the
performances of the compared methods are shown. An application related to a customer
satisfaction survey is also illustrated
Statistical Inference in Behavioral Economics: a Permutation Approach
No full textSome experiments of behavioral economics concern intertemporal choice (IC). Behavioral anomalies such as magnitude effect, sign effect, delay effect, ecc. can be tested with complex experiments that require advanced testing methods. We propose a nonparametric method based on a permutation approach, which has many advantages and good properties, proved through Monte Carlo simulation studies
Some new results on univariate and multivariate permutation tests for ordinal categorical variables under restricted alternatives
No full textIn several sciences, especially when dealing with performance evaluation, complex testing problems may arise due in particular to the presence of multidimensional categorical data. In such cases the application of nonparametric methods can represent a reasonable approach. In this paper, we consider the problem of testing whether a "treatment" is stochastically larger than a "control" when univariate and multivariate ordinal categorical data are present. We propose a solution based on the nonparametric combination of dependent permutation tests (Pesarin in Multivariate permutation test with application to biostatistics. Wiley, Chichester, 2001), on variable transformation, and on tests on moments. The solution requires the transformation of categorical response variables into numeric variables and the breaking up of the original problem's hypotheses into partial sub-hypotheses regarding the moments of the transformed variables. This type of problem is considered to be almost impossible to analyze within likelihood ratio tests, especially in the multivariate case (Wang in J Am Stat Assoc 91:1676-1683, 1996). A comparative simulation study is also presented along with an application example
Some new results on permutation tests for ordinal categorical variables under restricted alternatives
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