Displaying similar documents to “A simple non-parametric goodness-of-fit test for elliptical copulas”

About tests of the “simplifying” assumption for conditional copulas

Alexis Derumigny, Jean-David Fermanian (2017)

Dependence Modeling

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We discuss the so-called “simplifying assumption” of conditional copulas in a general framework. We introduce several tests of the latter assumption for non- and semiparametric copula models. Some related test procedures based on conditioning subsets instead of point-wise events are proposed. The limiting distributions of such test statistics under the null are approximated by several bootstrap schemes, most of them being new. We prove the validity of a particular semiparametric bootstrap...

Power comparison of Rao′s score test, the Wald test and the likelihood ratio test in (2xc) contingency tables

Anita Dobek, Krzysztof Moliński, Ewa Skotarczak (2015)

Biometrical Letters

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There are several statistics for testing hypotheses concerning the independence of the distributions represented by two rows in contingency tables. The most famous are Rao′s score, the Wald and the likelihood ratio tests. A comparison of the power of these tests indicates the Wald test as the most powerful.

A copula test space model how to avoid the wrong copula choice

Frederik Michiels, Ann De Schepper (2008)

Kybernetika

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We introduce and discuss the test space problem as a part of the whole copula fitting process. In particular, we explain how an efficient copula test space can be constructed by taking into account information about the existing dependence, and we present a complete overview of bivariate test spaces for all possible situations. The practical use will be illustrated by means of a numerical application based on an illustrative portfolio containing the S&P 500 Composite Index, the JP...

The behavior of locally most powerful tests

Marek Omelka (2005)

Kybernetika

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The locally most powerful (LMP) tests of the hypothesis H : θ = θ 0 against one-sided as well as two-sided alternatives are compared with several competitive tests, as the likelihood ratio tests, the Wald-type tests and the Rao score tests, for several distribution shapes and for location, shape and vector parameters. A simulation study confirms the importance of the condition of local unbiasedness of the test, and shows that the LMP test can sometimes dominate the other tests only in a very restricted...

On the small sample properties of variants of Mardia’s and Srivastava’s kurtosis-based tests for multivariate normality

Zofia Hanusz, Joanna Tarasińska, Zbigniew Osypiuk (2012)

Biometrical Letters

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The kurtosis-based tests of Mardia and Srivastava for assessing multivariate normality (MVN) are considered. The asymptotic standard normal distribution of their test statistics, under normality, is often misused for too small samples. The purpose of this paper is to suggest mean-and-variance corrected versions of the Mardia and Srivastava test statistics. Simulation studies evaluating both the true sizes and the powers of original and corrected tests against selected alternatives are...

Goodness-of-fit test for long range dependent processes

Gilles Fay, Anne Philippe (2010)

ESAIM: Probability and Statistics

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In this paper, we make use of the information measure introduced by Mokkadem (1997) for building a goodness-of-fit test for long-range dependent processes. Our test statistic is performed in the frequency domain and writes as a non linear functional of the normalized periodogram. We establish the asymptotic distribution of our statistic under the null hypothesis. Under specific alternative hypotheses, we prove that the power converges to one. The performance of our test procedure is illustrated...

Normalization of the Kolmogorov–Smirnov and Shapiro–Wilk tests of normality

Zofia Hanusz, Joanna Tarasińska (2015)

Biometrical Letters

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Two very well-known tests for normality, the Kolmogorov-Smirnov and the Shapiro- Wilk tests, are considered. Both of them may be normalized using Johnson’s (1949) SB distribution. In this paper, functions for normalizing constants, dependent on the sample size, are given. These functions eliminate the need to use non-standard statistical tables with normalizing constants, and make it easy to obtain p-values for testing normality.