Trend smoothness achieved by penalized least squares with the smoothing parameter chosen by optimality criteria

This work presents a study about the smoothness attained by the methods more frequently used to choose the smoothing parameter in the context of splines: Cross Validation, Generalized Cross Validation, and corrected Akaike and Bayesian Information Criteria, implemented with Penalized Least Squares. It is concluded that the amount of smoothness strongly depends on the length of the series and on the type of underlying trend, while the presence of seasonality even though statistically significant is less relevant. The intrinsic variability of the series is not statistically significant and its effect is taken into account only through the smoothing parameter.     

Diagnostics in elliptical regression models with stochastic restrictions applied to econometrics

We propose an influence diagnostic methodology for linear regression models with stochastic restrictions and errors following elliptically contoured distributions. We study how a perturbation may impact on the mixed estimation procedure of parameters in the model. Normal curvatures and slopes for assessing influence under usual schemes are derived, including perturbations of case-weight, response variable, and explanatory variable. Simulations are conducted to evaluate the performance of the proposed methodology. An example with real-world economy data is presented as an illustration.     

Reliability,Population Classification and Weighting in Multidimensional Poverty Measurement: A Monte Carlo Study

Social Indicators Research - In poverty measurement, differential weighting aims to take into account the unequal importance of the diverse dimensions and aspects of poverty and to add valuable...     

Slashed generalized exponential distribution

In this paper, we introduce an extension of the generalized exponential (GE) distribution, making it more robust against possible influential observations. The new model is defined as the quotient between a GE random variable and a beta-distributed random variable with one unknown parameter. The resulting distribution is a distribution with greater kurtosis than the GE distribution. Probability properties of the distribution such as moments and asymmetry and kurtosis are studied. Likewise, statistical properties are investigated using the method of moments and the maximum likelihood approach. Two real data analyses are reported illustrating better performance of the new model over the GE model.     

Slashed generalized Rayleigh distribution

We introduce a new family of distributions suitable for fitting positive data sets with high kurtosis which is called the Slashed Generalized Rayleigh Distribution. This distribution arises as the quotient of two independent random variables, one being a generalized Rayleigh distribution in the numerator and the other a power of the uniform distribution in the denominator. We present properties and carry out estimation of the model parameters by moment and maximum likelihood (ML) methods. Finally, we conduct a small simulation study to evaluate the performance of ML estimators and analyze real data sets to illustrate the usefulness of the new model.     

Toward a multi‐ethnic public sphere? Media consumption in highly diverse districts in Spain

This article examines media consumption in highly diverse districts in Spain as representative of recent changes in this field due to new immigration to countries in Southern Europe in times of crisis. Drawing on findings from two surveys, we observed a decrease in access to media specifically aimed at audiences of minority cultures between 2010 and 2012. It is suggested that this decline is not due to a modification in media consumption habits by minority groups, but rather the economic crisis of 2008. Furthermore, we study the consumption of minority media by natives: we observe that youth and more educated individuals consume other cultures’ minority media to a greater extent, particularly the press, radio, and Internet news sources. We suggest that elements are present that public administrations could utilize to further develop a multi‐ethnic public sphere. Finally, we explore the emergence of a new transnational dimension to the multi‐ethnic public sphere.     

Epsilon Birnbaum–Saunders distribution family: properties and inference

In this paper we introduce a new extension for the Birnbaum–Saunder distribution based on the family of the epsilon-skew-symmetric distributions studied in Arellano-Valle et al. (J Stat Plan Inference 128(2):427–443, 2005). The extension allows generating Birnbaun–Saunders type distributions able to deal with extreme or outlying observations (Dupuis and Mills, IEEE Trans Reliab 47:88–95, 1998). Basic properties such as moments and Fisher information matrix are also studied. Results of a real data application are reported illustrating good fitting properties of the proposed model.     

The multivariate alpha-power model

The main object of this paper is to propose a multivariate extension to the alpha-power model which is an alternative to the multivariate skew-normal model (Arellano-Valle and Azzalini, 2008). It also extends the power-normal model discussed in Gupta and Gupta (2008) by making it more flexible. Inference is dealt with by using the likelihood approach and a pseudo-likelihood approach based on conditional distributions which, although slightly less efficient, is simpler to implement. An application to a real data set is used to demonstrate the usefulness of the extension.     

A Robust Procedure in Nonlinear Models for Repeated Measurements

Nonlinear regression models arise when definite information is available about the form of the relationship between the response and predictor variables. Such information might involve direct knowledge of the actual form of the true model or might be represented by a set of differential equations that the model must satisfy. We develop M-procedures for estimating parameters and testing hypotheses of interest about these parameters in nonlinear regression models for repeated measurement data. Under regularity conditions, the asymptotic properties of the M-procedures are presented, including the uniform linearity, normality and consistency. The computation of the M-estimators of the model parameters is performed with iterative procedures, similar to Newton–Raphson and Fisher's scoring methods. The methodology is illustrated by using a multivariate logistic regression model with real data, along with a simulation study.