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**1 - 5**of**5**### Flexible modelling in statistics: past, present and future

"... In times where more and more data become available and where the data exhibit rather complex structures (significant departure from symmetry, heavy or light tails), flexible modelling has become an essential task for statisticians as well as researchers and practitioners from domains such as economi ..."

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In times where more and more data become available and where the data exhibit rather complex structures (significant departure from symmetry, heavy or light tails), flexible modelling has become an essential task for statisticians as well as researchers and practitioners from domains such as economics, finance or environmental sciences. This is reflected by the wealth of existing proposals for flexible distributions; well-known examples are Azzalini’s skew-normal, Tukey’s g-and-h, mixture and two-piece distribu-tions, to cite but these. My aim in the present paper is to provide an introduction to this research field, intended to be useful both for novices and professionals of the domain. After a description of the research stream itself, I will narrate the gripping his-tory of flexible modelling, starring emblematic heroes from the past such as Edgeworth and Pearson, then depict three of the most used flexible families of distributions, and finally provide an outlook on future flexible modelling research by posing challenging open questions.

### ON ASYMPTOTIC EFFICIENCY OF GOODNESS-OF-FIT TESTS FOR THE PARETO DISTRIBUTION BASED ON ITS CHARACTERIZATION

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### Mixtures of equispaced normal distributions and their use for testing symmetry in univariate data

, 2014

"... Given a random sample of observations, mixtures of normal densities are often used to estimate the unknown continuous distribution from which the data come. Here we propose the use of this semiparametric framework for testing symmetry about an unknown value. More precisely, we show how the null hypo ..."

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Given a random sample of observations, mixtures of normal densities are often used to estimate the unknown continuous distribution from which the data come. Here we propose the use of this semiparametric framework for testing symmetry about an unknown value. More precisely, we show how the null hypothesis of symmetry may be formulated in terms of normal mixture model, with weights about the centre of symmetry constrained to be equal one another. The resulting model is nested in a more general unconstrained one, with same number of mixture components and free weights. Therefore, after having maximised the constrained and unconstrained log-likelihoods by means of a suitable algorithm, such as the Expectation-Maximisation, symmetry is tested against skewness through a likelihood ratio statistic. The per-formance of the proposed mixture-based test is illustrated through a Monte Carlo simulation study, where we compare two versions of the test, based on different cri-teria to select the number of mixture components, with the traditional one based on the third standardised moment. An illustrative example is also given that focuses on real data.

### reflective symmetry about a known median direction

"... In this paper, we propose optimal tests for reflective circular symmetry about a fixed median direction. The distributions against which optimality is achieved are the so-called k-sine-skewed distributions of Umbach and Jammalamadaka (2009). We first show that sequences of k-sine-skewed models are l ..."

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In this paper, we propose optimal tests for reflective circular symmetry about a fixed median direction. The distributions against which optimality is achieved are the so-called k-sine-skewed distributions of Umbach and Jammalamadaka (2009). We first show that sequences of k-sine-skewed models are locally and asymptotically normal in the vicinity of reflective symmetry. Following the Le Cam methodology, we then construct optimal (in the maximin sense) parametric tests for reflective symmetry, which we render semi-parametric by a studentization argument. These asymptotically distribution-free tests happen to be uniformly optimal (under any reference density) and are moreover of a very simple form. They furthermore exhibit nice small sample properties, as we show through a Monte Carlo simulation study. Our new tests also allow us to re-visit the famous red wood ants data set of Jander (1957). We further show that one of the proposed parametric tests can as well serve as a test for uniformity against cardioid alternatives; this test coincides with the famous circular Rayleigh (1919) test for uniformity which is thus proved to be (also) optimal against cardioid alternatives. Moreover, our choice of k-sine-skewed alternatives, which are the circular analogues of the classical linear skew-symmetric distributions, permits us a Fisher singularity analysis à la Hallin and Ley (2012) with the result that only the prominent sineskewed von Mises distribution suffers from these inferential drawbacks. Finally, we conclude the paper by discussing the unspecified location case.