Non Parametric Test for Bivariate Case
Non-Parametric Inference for Bivariate Extreme-Value Copulas
... Abstract Extreme-value copulas arise as the possible limits of copulas of com- ponent-wise maxima of independent, identically distributed samples. The use of bivariate extreme-value copulas is greatly facilitated ...
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An introduction to parametric and non-parametric models for bivariate positive insurance claim severity distributions
... particular case, the data set consists of a sample of claims that include two types of losses: property damage mainly resulting from third party liability and medical expenses that are not included in the Public ...
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A goodness-of-fit test for semi-parametric copula models of right-censored bivariate survival times
... Due to intensive computations required by a large number of simulation scenarios, the results were summarized based on 200 replications for each scenario. Type I error control Table 3.1 and Table 3.2 report the empirical ...
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A non parametric maximum test for the Behrens–Fisher problem
... t test can become liberal ...Brunner-Munzel test and the Welch t test based on ranks control the type I error rate for a wide range of ...t test was superior to the Brunner-Munzel test ...
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A Two-Sample Non-Parametric Likelihood Ratio Test
... a case-by- case ...fit case, a particular weighting function might deliver high power against certain alternatives, but only at the expense of power against other ...
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A Non-Parametric Test of the Conditional CAPM for the Mexican Economy
... the test could produce a rejection and large pricing error estimates because of a poor functional ...a non-parametric discount factor we get a weighted least squares estimator for the re- gression ...
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CLOUD: a non-parametric detection test for microbiome outliers
... to test whether disease conditions are associated or correlated with specific taxa or overall ecological community com- position ...published non-parametric statistic tests of whether a patient’s ...
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Semi-parametric bivariate polychotomous ordinal regression
... occurs whenever the association between a response and one (or more) of its relevant regressor(s) is distorted by the presence of an unobserved third variable which affects simultaneously the two. Such covariates are ...
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A non-parametric test for self-similarity and stationarity in network traffic
... In general there is not a great deal of agreement between any of the es- timators, in a number of cases with differences of up to 0.1, and there is no readily discernable pattern to the differences. Unlike the WAV ...
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Gradient test under non-parametric random effects models
... gradient test on generalised linear models with random ...gradient test using the methodology developed by Peers (1971) and Hayakawa ...the test with the local power of the tests of the likelihood ...
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Energy Consumption and Economic Growth: Parametric and Non Parametric Causality Testing for the Case of Greece
... the case with the BDS test the delinearization process takes place within a bivariate VAR ...solely non-linear in nature. The detailed results from the first step of the ...
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Parametric modeling of dependence of bivariate quantile regression residuals' signs
... the bivariate case, however the considerations below can be easily extended to higher ...a bivariate dependent variable (Y 1 , Y 2 ), from which we extract the ...this case subjects with high ...
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Multivariate Longitudinal Analysis with Bivariate Correlation Test
... ratio test based on these EM estimators to test the independence of two dimensions of the ...this test and have shown that this is an extremely sensitive ...from non identically distributed ...
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A non-parametric test for self-similarity and stationarity in network traffic; Preprint version
... In general there is not a great deal of agreement between any of the estimators, in a number of cases with differences of up to 0.1, and there is no readily discernable pattern to the differences. Unlike the WAV ...
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Exports, International Investment, and Plant Performance: Evidence from a Non-Parametric Test
... stricter test of productivity differences than just comparing mean levels of ...the test such that there must be statistically robust differences between the ...
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A Non-parametric Control Chart for Controlling Variability Based on Squared Rank Test
... that non-parametric methods can be somewhat less efficient than their parametric counterparts, provided ofcourse that one has a complete knowledge of the underlying stochastic process for which the ...
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Parametric and Bayesian non-parametric estimation of copulas.
... the parametric family of the Archimedean ...one-parameter case and gives better results than its competitors, in terms of MSE, for two-parameter ...the parametric assumption and present Bayesian ...
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Nonparametric predictive inference with parametric copulas for combining bivariate diagnostic tests.
... from bivariate Normal distributions, hence with a linear dependence between the X and Y ...is non-linear, underlying dependence ...unknown, non-linear dependence between the X and Y ...
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Nonparametric predictive inference with parametric copulas for combining bivariate diagnostic tests
... in case of small data sets, but this advantage disappeared for larger data ...either parametric copulas which reflect the dependence structure, and therefore may require detailed topic knowledge, or the use ...
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Anomaly Detection on IP Flow Using Bivariate Parametric Detection Mechanism
... Uni Source Attacks Uni Source Attacks are launched by and originating from a single source. Many modern operating systems incorporate interrupt-driven network subsystem architectures, which have been shown to lack both ...
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