Statistical Theory and Related Fields ushered into its second year. Again, we would like to take this opportunity to express our sincere gratitude to all the authors and supporters of this journal.
The first article in the first issue of 2018 is a review paper by Prentice and Huang for nutritional epidemiology methods, which reviews statistical challenges as well as opportunities in obtaining reliable information on dietary approaches to chronic disease prevention. This article is followed by three discussion papers, by Freedman and Shaw, Spiegelman, and Lin, and a rejoinder by Prentice and Huang. The discussions and rejoinder add more extensive results and extensions.
The next two papers are in the area of ‘objective Bayesian analysis’. The paper by He, Wang and Cao studies the objective Bayes method in the accelerated degradation Wiener model with measurement errors, while the paper by Zhang, He, Sun, Lu and Wang considers hypothesis testing and estimation for the intraclass model using objective Bayes.
The next three articles are all in the field of life testing. Bai, Shi, Liu and Liu develop methods on statistical analysis of dependent competing risks model, which can be applied to accelerated life testing with added progressive censoring. In the paper by He, Yue and He, they develop a step-stress accelerated degradation test. Wiener process with correlation is considered. The paper by Zubair, Alzaatreh, Tahir, Mansoor and Mustafa generalises the exponential distribution for life testing to a model for skewed data, and considers its applications.
The last three articles are in different topics. The paper by Lu, Song and Liang is about statistical learning for optimal dynamic treatment regime. The authors constructed a deep advantage learning method and implemented different architectures of deep and convexified convolutional neural networks. The article by Zhang, Shao, Yu and Wang investigates the impact of sufficient dimension reduction in nonparametric estimation of causal effects, where they show how to derive efficient nonparametric estimators after dimension reduction. The last paper by Ye studies covariate adaptive design in clinical trials, with the main emphasise in obtaining robust variance estimators.
Our goal is to publish interesting and diversified results in this journal. We welcome submissions of high-quality articles from statisticians and scientists analysing various types of data.