Subset selection in quantile regression analysis via alternative Bayesian information criteria and heuristic optimization

Subset selection is an extensively studied problem in statistical learning. Especially it becomes popular for regression analysis. This problem has considerable attention for generalized linear models as well as other types of regression methods. Quantile regression is one of the most used types of regression method. In this article, we consider subset selection problem for quantile regression analysis with adopting some recent Bayesian information criteria. We also utilized heuristic optimization during selection process. Simulation and real data application results demonstrate the capability of the mentioned information criteria. According to results, these information criteria can determine the true models effectively in quantile regression models.     

New estimators based on order statistics in some families of scale distributions

In this study some new unbiased estimators based on order statistics are proposed for the scale parameter in some family of scale distributions. These new estimators are suitable for the cases of complete (uncensored) and symmetric doubly Type-II censored samples. Further, they can be adapted to Type II right or Type II left censored samples. In addition, unbiased standard deviation estimators of the proposed estimators are also given. Moreover, unlike BLU estimators based on order statistics, expectation and variance-covariance of relevant order statistics are not required in computing these new estimators.

Simulation studies are conducted to compare performances of the new estimators with their counterpart BLU estimators for small sample sizes. The simulation results show that most of the proposed estimators in general perform almost as good as the counterpart BLU estimators; even some of them are better than BLU in some cases. Furthermore, a real data set is used to illustrate the new estimators and the results obtained parallel with those of BLUE methods.     

Particle swarm optimization-based variable selection in Poisson regression analysis via information complexity-type criteria

Modeling of count responses is widely performed via Poisson regression models. This paper covers the problem of variable selection in Poisson regression analysis. The basic emphasis of this paper is to present the usefulness of information complexity-based criteria for Poisson regression. Particle swarm optimization (PSO) algorithm was adopted to minimize the information criteria. A real dataset example and two simulation studies were conducted for highly collinear and lowly correlated datasets. Results demonstrate the capability of information complexity-type criteria. According to the results, information complexity-type criteria can be effectively used instead of classical criteria in count data modeling via the PSO algorithm.     

Adaptive Elastic Net GMM Estimation With Many Invalid Moment Conditions: Simultaneous Model and Moment Selection

This article develops the adaptive elastic net generalized method of moments (GMM) estimator in large-dimensional models with potentially (locally) invalid moment conditions, where both the number of structural parameters and the number of moment conditions may increase with the sample size. The basic idea is to conduct the standard GMM estimation combined with two penalty terms: the adaptively weighted lasso shrinkage and the quadratic regularization. It is a one-step procedure of valid moment condition selection, nonzero structural parameter selection (i.e., model selection), and consistent estimation of the nonzero parameters. The procedure achieves the standard GMM efficiency bound as if we know the valid moment conditions ex ante, for which the quadratic regularization is important. We also study the tuning parameter choice, with which we show that selection consistency still holds without assuming Gaussianity. We apply the new estimation procedure to dynamic panel data models, where both the time and cross-section dimensions are large. The new estimator is robust to possible serial correlations in the regression error terms.     

The Effects of Perceived Islamophobia on Group Identification and Acculturation Attitudes

This study examines the effects of perceived Islamophobia on group identification and acculturation attitudes in a sample of Turkish Canadians. We tested the rejection‐identification model (RIM) and the rejection‐disidentification model (RDIM) to examine how perceived Islamophobia affects religious and national identification. The study also explores whether perceived Islamophobia, national identification, and religious identification have any impact on the acculturation attitudes of Turkish immigrants. All relationships were investigated using path analysis. The results suggest that perceived Islamophobia does not affect religious or national identification. Results also show that perceived Islamophobia positively predicts endorsement of marginalization as an acculturation strategy. National identification predicts higher levels of integration and assimilation and lower levels of separation. Those who report higher religious identification are more inclined to prefer separation.     

Transmuted two-parameter Lindley distribution

In this study, we propose a new distribution using the quadratic rank transmutation map named as transmuted two-parameter Lindley distribution (TTLD). This distribution is more flexible than the two-parameter Lindley distribution (TLD). The properties of the TTLD are examined, and estimation methods for the parameters of this distribution are discussed. The usefulness of the TTLD is demonstrated on some real data.     

Elites,Oil, and Democratization: A Survival Analysis*

Objective. This study explores the relationship between natural resource wealth, most notably oil, and democratic survival. I contend that natural resources help sustain democratic regimes by easing social conflict between the rich and the poor. As such, the availability of abundant natural resource revenues alleviates the elites' fear of redistribution of wealth, thereby stabilizing the regime. Methods. I estimate a series of survival analyses for the period between 1960 and 1999. Results. Elite support and oil wealth independently help stabilize democracies. Conclusions. The results bring the widely held views on the link between oil and democracy into question and invite further scrutiny.     

Timing the Use of Breast Cancer Risk Information in Biopsy Decision‐Making

Available clinical evidence is inconclusive on whether radiologists should use the patient risk profile information when interpreting mammograms. On the one hand, risk profile information is informative and can improve radiologists’ performance, but on the other hand, it may impair their judgment by introducing biases in mammography interpretation. Therefore, it is important to assess whether and when profile information use translates into improved outcomes. We model the use of profile information in mammography, using a decision theoretic approach and explore the value of profile information using three process design choices: mammography only, unbiased, and biased reading. We estimate the parameters of our model using clinical data and find that using profile information along with the mammography information can achieve a better performance than not using the profile information. However, the better performance is contingent on the weight assigned to the profile information as well as the extent of bias due to profile information. Translating our findings into clinical practice would require properly designed experiments aiming to quantify the effect of the timing and the use of profile information on performance while accounting for radiologist and patient characteristics. When conducting an experiment is not feasible, a uniform operational sequence for interpreting mammograms and related guidelines may be a useful starting point to improve the quality of mammography operations.