Hansheng Wang

TL;DR: In this paper, the authors present a survey of sample sizes for single-arm and multiple-arm clinical trials, focusing on the following issues: Confounding and interaction: 1-Sided Test Versus Two-Sides Test Crossover Design Versus Parallel Design Subgroup/Interim Analyses Data Transformation Practical Issues COMPARING MEANS One-Sample Design Two-Sample Parallel Design 2-Sample Crossover design Multiple-Sample One-Way ANOVA Multiple-sample Williams Design Practical issues LARGE SAMPLE TESTS for PROPORTIONS

TL;DR: This work shows that the commonly used the generalised crossvalidation cannot select the tuning parameter satisfactorily, with a nonignorable overfitting effect in the resulting model, and proposes a bic tuning parameter selector, which is shown to be able to identify the true model consistently.

TL;DR: The least absolute deviation (LAD) regression is a useful method for robust regression, and the least absolute shrinkage and selection operator (lasso) is a popular choice for shrinkage estimation and variable selection, which are combined to produce LAD-lasso.

TL;DR: In this article, the authors further enlarge the scope of applicability of the traditional Bayesian information criterion type criteria to the situation with a diverging number of parameters for both unpenalized and penalized estimators.

TL;DR: If the adaptive LASSO penalty and a Bayes information criterion–type tuning parameter selector are used and the resulting LSA estimator can be as efficient as the oracle, the standard asymptotic theory can be established and the LARS algorithm can be applied.