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The number of immigrants moving to and settling in Europe has increased over the past decade, making migration one of the most topical and pressing issues in European politics. It is without a doubt that immigration has multiple impacts, in terms of economy, society and culture, on the European Union. It is fundamental to policy-makers to correctly evaluate people's attitudes towards immigration when designing integration policies. Of critical interest is to properly discriminate between subjects who are favourable towards immigration from those who are against it. Public opinions on migration are typically coded as binary responses in surveys. However, traditional methods, such as the standard logistic regression, may suffer from computational issues and are often not able to accurately model survey information. In this paper we propose an efficient Bayesian approach for modelling binary response data based on the generalized logistic regression. We show how the proposed approach provides an increased flexibility compared to traditional methods, due to its ability to capture heavy and light tails. The power of our methodology is tested through simulation studies and is illustrated using European Social Survey data on immigration collected in different European countries in 2016–2017. [ABSTRACT FROM AUTHOR]

The paper explores the relationships between subjective and contextual covariates as determinants of discrimination perception in a large sample survey. A modelling approach is implemented to detect the perception of discrimination on the ground of sexual orientation through expressed ratings, in a cross-country perspective. Exploiting the capabilities of a mixture model, which interprets ordinal evaluations as the combined results of two latent components, the cognitive process of selection among discrete ordered alternatives is discussed in terms of attractiveness towards the item and uncertainty in the response pattern. Gender, age and political orientation turn out to be significant variables at the individual level. Furthermore, the ILGA country score and education level, as contextual variables, exert prominent effects at country level. Empirical evidence from the Special Eurobarometer 2015 on Opinions as well as a simulation experiment support the usefulness of the approach. [ABSTRACT FROM AUTHOR]

This paper introduces a new two-parameter exponentiated discrete Lindley distribution. A wide range of its structural properties are investigated. This includes the shape of the probability mass function, hazard rate function, moments, skewness, kurtosis, stress–strength reliability, mean residual lifetime, mean past lifetime, order statistics and L-moment statistics. The hazard rate function can be increasing, decreasing, decreasing–increasing–decreasing, increasing–decreasing–increasing, unimodal, bathtub, and J-shaped depending on its parameters values. Two methods are used herein to estimate the model parameters, namely, the maximum likelihood, and the proportion. A detailed simulation study is carried out to examine the bias and mean square error of maximum likelihood and proportion estimators. The flexibility of the proposed model is explained by using four distinctive data sets. It can serve as an alternative model to other lifetime distributions in the existing statistical literature for modeling positive real data in many areas. [ABSTRACT FROM AUTHOR]

In this paper, we introduce a new approach to generate flexible parametric families of distributions. These models arise on competitive and complementary risks scenario, in which the lifetime associated with a particular risk is not observable; rather, we observe only the minimum/maximum lifetime value among all risks. The latent variables have a zero-truncated Poisson distribution. For the proposed family of distribution, the extra shape parameter has an important physical interpretation in the competing and complementary risks scenario. The mathematical properties and inferential procedures are discussed. The proposed approach is applied in some existing distributions in which it is fully illustrated by an important data set. [ABSTRACT FROM AUTHOR]

To handle the multicollinearity issues in the regression analysis, a class of 'strictly concave penalty function' is described in this paper. As an example, a new penalty function called 'modified log penalty' is introduced. The penalized estimator based on strictly concave penalties enjoys the oracle property under certain regularity conditions discussed in the literature. In the multicollinearity cases where such conditions are not applicable, the behaviors of the strictly concave penalties are discussed through examples involving strongly correlated covariates. Real data examples and simulation studies are provided to show the finite-sample performance of the modified log penalty in terms of prediction error under scenarios exhibiting multicollinearity. [ABSTRACT FROM AUTHOR]

This paper proposes k-medoids clustering method to reveal the distinct groups of 1318 variable stars in the Galaxy based on their light curves, where each light curve represents the graph of brightness of the star against time. To overcome the deficiencies of subjective traditional classification, we separate the stars more scientifically according to their geometrical configuration and show that our approach outperforms the existing classification schemes in astronomy. It results in two optimum groups of eclipsing binaries corresponding to bright, massive systems and fainter, less massive systems. [ABSTRACT FROM AUTHOR]

In reliability or survival analysis, the hazard function plays a significant part for it can display the instantaneous failure rate at any time point. In practice, the abrupt change in hazard function at an unknown time point may occur after a maintenance activity or major operation. Under these circumstances, identifying the change point and estimating the size of the change are meaningful. In this paper, we assume that the hazard function is piecewise constant with a single jump at an unknown time. We propose the single change-point model for interval-censored survival data with a cure fraction. Estimation methods for the proposed model are investigated, and large-sample properties of the estimators are established. Simulation studies are carried out to evaluate the performance of the estimating method. The liver cancer data and breast cancer data are analyzed as the applications. [ABSTRACT FROM AUTHOR]

Based on the minimal spanning tree (MST) of the observed data set, the paper introduces new notions of data depth and medians for multivariate data. The MST of a data set of size n is the MST of the complete weighted undirected graph on n vertices, where the edge weights are the pairwise distances of the data points. We study several properties of the MST-based depth functions. We consider the corresponding multidimensional medians, investigate their robustness and computational complexity. An example illustrates the use of the MST-based depth functions. [ABSTRACT FROM AUTHOR]

Corpus linguistics is the study of language as expressed in a body of texts or documents. The relative frequency of a word within a text and the dispersion of the word across the collection of texts provide information about the word's prominence and diffusion, respectively. In practice, people tend to use a relatively small number of words in a language's inventory of words and thus a large number of words in the lexicon are rarely employed. The zero-inflated beta distribution enables one to model the relative frequency of a word in a text since some texts may not even contain the word under study. In this paper, the expectation of a word's prominence and dispersion are defined under the zero-inflated beta model. Estimates of a word's prominence and dispersion are computed for words in the British National Corpus 1994 (BNC), a 100 million word collection of written and spoken language of a wide range of British English. The relationship between a word's prominence and dispersion is discussed as well as measures that are functions of both prominence and dispersion. [ABSTRACT FROM AUTHOR]

The purpose of this paper is to treat the prediction problems where a number of observations are missing to the quarter-plane past of a stationary random field. Our aim is to quantify the influence of missing values on the prediction by giving the simple bounds for the prediction error variance. These bounds allow to characterize the random fields for which the missing observations do not affect the prediction. Simulation experiments and an application to real data are presented. [ABSTRACT FROM AUTHOR]

Time lag effect exists widely in the course of economic operation. Some economic variables are affected not only by various factors in the current period but also by various factors in the past and even their own past values. As a class of dynamical models, autoregressive distributed lag (ARDL) models are frequently used to conduct dynamic regression analysis. In this paper, we are interested in the quantile regression (QR) modeling of the ARDL model in a dynamic framework. By combining the working likelihood of asymmetric Laplace distribution (ALD) with the expectation–maximization (EM) algorithm into the considered ARDL model, the iterative weighted least square estimators (IWLSE) are derived. Some Monte Carlo simulations are implemented to evaluate the performance of the proposed estimation method. A dataset of the consumption of electricity by residential customers is analyzed to illustrate the application. [ABSTRACT FROM AUTHOR]

Generalized linear mixed models have been widely used in the analysis of correlated data in a lot of research areas. The linear mixed model with normal errors has been a popular model for the analysis of repeated measures and longitudinal data. Outliers, however, can severely have an wrong influence on the linear mixed model. The aforementioned model has not fully taken those severe outliers into consideration. One of the popular robust estimation methods, M-estimator attains robustness at the expense of first-order or second-order efficiency whereas minimum Hellinger distance estimator is efficient and robust. In this paper, we propose more robust Bayesian version of parameter estimation via pseudo posterior distribution based on minimum Hellinger distance. It accommodates an appropriate nonparametric kernel density estimation for longitudinal data to require the proposed cross-validation estimator. We conduct simulation study and real data study with the orthodontic study data and the Alzheimers Disease (AD) study data. In simulation study, the proposed method shows smaller biases, mean squared errors, and standard errors than the (residual) maximum likelihood method (REML) in the presence of outliers or missing values. In real data analysis, standard errors and variance-covariance components for the proposed method in two data sets are shown to be lower than those for REML method. [ABSTRACT FROM AUTHOR]

In this paper, we propose a regression depth versus regression depth plot, hereafter RR-plot, for regression observations based on the halfspace regression depth. Areas of application of this tool include: the visualization of hypothesis tests about regression coefficients, and of the comparison between regression observations from different models, etc. Some characterization theorems are also provided to address the rationale of this RR-plot. [ABSTRACT FROM AUTHOR]

In this paper, we propose a new method for testing heteroskedasticity in two-way fixed effects panel data models under two important scenarios where the cross-sectional dimension is large and the temporal dimension is either large or fixed. Specifically, we will develop test statistics for both cases under the conditional moment framework, and derive their asymptotic distributions under both the null and alternative hypotheses. The proposed tests are distribution free and can easily be implemented using the simple auxiliary regressions. Simulation studies and two real data analyses demonstrate that our proposed tests perform well in practice, and may have the potential for wide application in econometric models with panel data. [ABSTRACT FROM AUTHOR]

In medical and epidemiological studies, it is often interest to study time-to-event distributions under competing risks that involve two or more failure types. Nonparametric analysis of competing risks is typically focused on the cumulative incidence function or nonparametric quantile function. However, the existing estimators may be very unstable due to their unsmoothness. In this paper, we propose a kernel nonparametric quantile estimator for right-censored competing risks data, which is a smoothed version of Peng and Fine's nonparametric quantile estimator. We establish the Bahadur representation of the proposed estimator. The convergence rate of the remainder term for the proposed estimator is substantially faster than Peng and Fine's quantile estimator. The pointwise confidence intervals and simultaneous confidence bands of the quantile functions are also derived. Simulation studies illustrate the good performance of the proposed estimator. The methodology is demonstrated with two applications of the Supreme Court Judge data and AIDSSI data. [ABSTRACT FROM AUTHOR]

This paper considers the quantile regression model with both individual fixed effect and time period effect for general spatial panel data. Fixed effects quantile regression estimators based on instrumental variable method will be proposed. Asymptotic properties of the proposed estimators will be developed. Simulations are conducted to study the performance of the proposed method. We will illustrate our methodologies using a cigarettes demand data set. [ABSTRACT FROM AUTHOR]

We, the Editor-in-Chief and Publisher of Journal of Applied Statistics have retracted the following article, which was due to appear in a special issue: Muhammad Ijaz, Wali Khan Mashwani, Atilla Göktaş & Yuksel Akay Unvan (2021): A novel alpha power transformed exponential distribution with real-life applications, Journal of Applied Statistics. DOI: . The Editor-in-Chief and the Publisher are cognisant of clear evidence that the findings presented are unreliable. The probability distribution is only valid if α > 1 and numerous mathematical properties in Section 2 have been shown to be incorrect. This has then impacted at least two figures in the article. We are further cognisant that the article contained a number of similarities to previously published papers where some of the findings had been published without proper cross-referencing including: Gupta, R.D. and Kundu, D. (2001), Exponentiated Exponential Family: An Alternative to Gamma and Weibull Distributions. Biom. J., 43: 117–130. . We have been informed in our decision-making by our corrections and editorial policies and the Committee on Publication Ethics (COPE) guidelines on retractions. The retracted article will remain online to maintain the scholarly record, but it will be digitally watermarked on each page as 'Retracted'. The Editor-in-Chief and the Publisher would like to thank the anonymous reader/s for their comments which alerted JAS to these major errors in the first instance. [ABSTRACT FROM AUTHOR]