Beta regression models are a suitable choice for continuous response variables on the unity interval. Random effects add further flexibility to the models and accommodate data structures such as hierarchical, repeated measures and longitudinal, which typically induce extra variability and/or dependence. Closed expressions cannot be obtained for parameter estimation and numerical methods are required and possibly combined with sampling algorithms. We focus on likelihood inference and related algorithms for the analysis of beta mixed models motivated by two real problems with grouped data structures. The first is a study on a life quality index of industry workers with data collected according to an hierarchical sampling scheme. The second is a study with a nested and longitudinal data structure assessing the impact of hydroelectric power plants upon measures of water quality indexes up, downstream and at the reservoirs of the dammed rivers. Relevant scientific hypothesis are investigated by comparing alternative models. The analysis uses different algorithms including data-cloning, an alternative to numerical approximations which also assess identifiability. Confidence intervals based on profiled likelihoods are compared to those obtained by asymptotic quadratic approximations, showing relevant differences for parameters related to the random effects

In the analysis of count data often the equidispersion assumption is not suitable, hence the Poisson regression model is inappropriate. As a generalization of the Poisson distribution the COM-Poisson distribution can deal with under-, equi-and overdispersed count data. It is a member of the exponential family of distributions and has the Poisson and geometric distributions as special cases, as well as the Bernoulli distribution as a limiting case. In spite of the nice properties of the COM-Poisson distribution, its location parameter does not correspond to the expectation, which complicates the interpretation of regression models specified using this distribution. In this paper, we propose a straightforward reparametrization of the COM-Poisson distribution based on an approximation to the expectation of this distribution. The main advantage of our new parametrization is the straightforward interpretation of the regression coefficients in terms of the expectation of the count response variable, as usual in the context of generalized linear models. Furthermore, the estimation and inference for the new COM-Poisson regression model can be done based on the likelihood paradigm. We carried out simulation studies to verify the finite sample properties of the maximum likelihood estimators. The results from our simulation study show that the maximum likeli-hood estimators are unbiased and consistent for both regression and dispersion parameters. We observed that the empirical correlation between the regression and dispersion parameter estimators is close to zero, which suggests that these parameters are orthogonal. We illustrate the application of the proposed model through the analysis of three data sets with over-, under-and equidispersed count data. The study of distribution properties through a consideration of dispersion, zero-inflated and heavy tail indices, together with the results of data analysis show the flexibility over standard approaches. Therefore, we encourage the application of the new parametrization for the analysis of count data in the context of COM-Poisson regression models. The com-arXiv:1801.09795v1 [stat.AP] 29 Jan 2018 2 Ribeiro Jr et al. putational routines for fitting the original and new version of the COM-Poisson regression model and the analyzed data sets are available in the supplementary material.

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Music genre can be hard to describe: many factors are involved, such as style, music technique, and historical context. Some genres even have overlapping characteristics. Looking for a better understanding of how music genres are related to musical harmonic structures, we gathered data about the music chords for thousands of popular Brazilian songs. Here, 'popular' does not only refer to the genre named MPB (Brazilian Popular Music) but to nine different genres that were considered particular to the Brazilian case. The main goals of the present work are to extract and engineer harmonically related features from chords data and to use it to classify popular Brazilian music genres towards establishing a connection between harmonic relationships and Brazilian genres. We also emphasize the generalisation of the method for obtaining the data, allowing for the replication and direct extension of this work. Our final model is a combination of multiple classification trees, also known as the random forest model. We found that features extracted from harmonic elements can satisfactorily predict music genre for the Brazilian case, as well as features obtained from the Spotify API. The variables considered in this work also give an intuition about how they relate to the genres. Keywords feature engineering • MIR • random forests • chords • genre classification