In this issue, we are happy to publish the following nine original papers and one invited paper. The first paper is "A generalized procedure for estimating the multinomial proportions in randomized response sampling using scrambling variables" by Housila P. Singh and Swarangi M. Gorey (2019). The authors have suggested a generalized randomized response procedure, for estimating the multinomial proportions of potentially sensitive attributes in survey sampling, using higher order moments of

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... bling variables at the estimation stage to produce unbiased estimators. This study derived expressions for variance and covariance of the generalized estimator with its development and showed that the developed estimator is more efficient than the previous estimators. The second paper is "Bayesian analysis of happiness with individual heterogeneity" by Lei Shi and Hikaru Hasegawa (2019). This study applies a new Bayesian univariate ordered probit model to happiness data on Australia, Canada, and the United States, with immigration status and religion status reflecting individual heterogeneity in the threshold model. The empirical results show that the models including individual heterogeneity perform better than those without individual heterogeneity do. Furthermore, the effects of heterogeneity vary between the three countries. Having a religious affiliation affects the thresholds in the United States, but shows no evident effects in Canada or Australia. In addition, parents' immigration status can affect the thresholds in Australia, but shows no effects in the United States or Canada. The third paper is "Logistic regression and Ising networks: prediction and estimation when violating lasso assumptions" by Lourens J. Waldorp, Maarten Marsman and Gunter Maris (2019). Recently, the Ising model was applied in psychology to model cooccurrences of mental disorders. It has been shown that the connections between the variables (nodes) in the Ising network can be estimated with a series of logistic regressions. This study demonstrates how well such a model predicts new observations and how well parameters of the Ising model can be estimated using logistic regressions from simulation experiments.