The Rayleigh–Lindley model: properties and applications

In this paper, the Rayleigh–Lindley (RL) distribution is introduced, obtained by compounding the Rayleigh and Lindley discrete distributions, where the compounding procedure follows an approach similar to the one previously studied by Adamidis and Loukas in some other contexts. The resulting distribution is a two-parameter model, which is competitive with other parsimonious models such as the gamma and Weibull distributions. We study some properties of this new model such as the moments and the mean residual life. The estimation was approached via EM algorithm. The behavior of these estimators was studied in finite samples through a simulation study. Finally, we report two real data illustrations in order to show the performance of the proposed model versus other common two-parameter models in the literature. The main conclusion is that the model proposed can be a valid alternative to other competing models well established in the literature.     

A Comparison of Significance Testing Procedures for the Intraclass Correlation from Family Data

Different procedures for testing problems concerning intraclass correlation from familial data are considered in the case of varying number of siblings per family. Under the assumption of multivariate normality, the hypotheses that the intraclass correlation is equal to a specified value are tested. To assess the performance of the tests, Monte Carlo simulations are designed to compare their powers. The Neyman's (1959) C(α) test and the test based on the modified ANOVA F statistic are shown to be consistently more powerful than other procedures.     

A Cross-Cultural Comparison of the Academic Engagement of Students

Higher education around the world is undergoing serious transformations as a result of technological, social, and economic processes. Universities from various countries are trying to secure a competitive position for themselves on the international market for educational services. Currently thousands of universities from different countries are trying to enter the race to join the race for international rankings. In their quest to reach the top of the rankings some countries seek to follow the experience of other, more successful nations by adopting their “best” practices. It is not surprising that all countries wish to secure a prosperous future for themselves and take efforts to avoid remaining on the sidelines of world development. However, a factor such as culture or national mentality may undermine the strategies and transformation processes that are being developed.

The goal of this study is to conduct a cross-cultural analysis of the academic engagement of students from Russian, Chinese, Japanese, American, and British universities and determine the role that cultural differences play in existing educational systems. The study sample consisted of 26,648 Bachelor’s students who were enrolled at universities in the United States, United Kingdom, China, Japan, and Russia during the 2016–2017 academic year. We used data from the “Student Experience in the Research University” (SERU) international research project to construct an index of student class involvement on the basis of a factor analysis. The results of our univariate analysis of variance (ANOVA) showed that students from Japanese, Chinese, American, British, and Russian universities demonstrate qualitatively different levels of academic involvement, which depends on the organization of the educational process as well as the cultural traditions of these countries. Thus, the Chinese students turned out to be the least engaged, since they were not required to actively participate in class discussions or to discuss additional questions related to the course with the instructor. Russian and American students demonstrated the highest level of engagement compared with other students from countries in the survey.

The author emphasizes the need to take into account existing cultural aspects when developing educational strategies, interacting with foreign students in the classroom, as well as conducting comparative studies.     

Disordered Gambling Prevalence: Methodological Innovations in a General Danish Population Survey

We study Danish adult gambling behavior with an emphasis on discovering patterns relevant to public health forecasting and economic welfare assessment of policy. Methodological innovations include measurement of formative in addition to reflective constructs, estimation of prospective risk for developing gambling disorder rather than risk of being falsely negatively diagnosed, analysis with attention to sample weights and correction for sample selection bias, estimation of the impact of trigger questions on prevalence estimates and sample characteristics, and distinguishing between total and marginal effects of risk-indicating factors. The most significant novelty in our design is that nobody was excluded on the basis of their response to a ‘trigger’ or ‘gateway’ question about previous gambling history. Our sample consists of 8405 adult Danes. We administered the Focal Adult Gambling Screen to all subjects and estimate prospective risk for disordered gambling. We find that 87.6% of the population is indicated for no detectable risk, 5.4% is indicated for early risk, 1.7% is indicated for intermediate risk, 2.6% is indicated for advanced risk, and 2.6% is indicated for disordered gambling. Correcting for sample weights and controlling for sample selection has a significant effect on prevalence rates. Although these estimates of the ‘at risk’ fraction of the population are significantly higher than conventionally reported, we infer a significant decrease in overall prevalence rates of detectable risk with these corrections, since gambling behavior is positively correlated with the decision to participate in gambling surveys. We also find that imposing a threshold gambling history leads to underestimation of the prevalence of gambling problems.     

Overcoming barriers to time-saving: reminders of future busyness encourage consumers to buy time

Spending money on time saving purchases improves happiness. Yet, people often fail to spend their money in this way. Because most people believe that the future will be less busy than the present, they may underweight the value of these purchases. We examine the impact of debiasing this previously unexplored barrier of consumer decisions to ‘buy time’ in a field experiment with a US-based sharing economy (N = 78,726). Prompting people to think that they will be as busy in the future as they are today increased the likelihood that customers would both open the email and click a link to purchase various services. In sum, making the future feel as busy as the present encourages individuals to buy future time.     

Consistency and robustness properties of the S-nonnegative garrote estimator

This paper concerns a robust variable selection method in multiple linear regression: the robust S-nonnegative garrote variable selection method. In this paper the consistency of the method, both in terms of estimation and in terms of variable selection, is established. Moreover, the robustness properties of the method are further investigated by providing a lower bound for the breakdown point, and by deriving the influence function. The provided expressions nicely reveal the impact that the choice of an initial estimator has on the robustness properties of the variable selection method. Illustrative examples of influence functions for the S-nonnegative garrote as well as for the original (non-robust) nonnegative garrote variable selection method are provided.     

An algorithm for the multivariate group lasso with covariance estimation

We study a group lasso estimator for the multivariate linear regression model that accounts for correlated error terms. A block coordinate descent algorithm is used to compute this estimator. We perform a simulation study with categorical data and multivariate time series data, typical settings with a natural grouping among the predictor variables. Our simulation studies show the good performance of the proposed group lasso estimator compared to alternative estimators. We illustrate the method on a time series data set of gene expressions.     

On the relationship between the sample size and the number of variables in a linear regression model

The problem of determining the number of variables to be included in the linear regression model is considered under the assumption that the dependent and independent variables have a joint normal distribution. It is shown that for a given sample size n there exists an optimal number k₀ (0 ≤ k₀ < n-2) of variables among all independent variables in the model, such that the expectation of the mean squared error corresponding to the prediction equation with k₀ variables is minimal.Application of this result to ustepwise procedures is discussed.