Regression analysis for a summed missing data problem under an outcome‐dependent sampling scheme

In this paper, we consider a regression analysis for a missing data problem in which the variables of primary interest are unobserved under a general biased sampling scheme, an outcome‐dependent sampling (ODS) design. We propose a semiparametric empirical likelihood method for accessing the association between a continuous outcome response and unobservable interesting factors. Simulation study results show that ODS design can produce more efficient estimators than the simple random design of the same sample size. We demonstrate the proposed approach with a data set from an environmental study for the genetic effects on human lung function in COPD smokers. The Canadian Journal of Statistics 40: 282–303; 2012 © 2012 Statistical Society of Canada     

Hierarchically penalized quantile regression with multiple responses

We study variable selection in quantile regression with multiple responses. Instead of applying conventional penalized quantile regression to each response separately, it is desired to solve them simultaneously when the sparsity patterns of the regression coefficients for different responses are similar, which is often the case in practice. In this paper, we propose employing a hierarchical penalty that enables us to detect a common sparsity pattern shared between different responses as well as additional sparsity patterns within the selected variables. We establish the oracle property of the proposed method and demonstrate it offers better performance than existing approaches.     

Limit behaviors of the estimator of nonparametric regression model based on martingale difference errors

In the paper, we shall establish some limit theorems for the nonparametric estimator of the regression model, which include $L^p$-convergence, complete convergence, and strong convergence of the estimator. These results supplement and improve some known works.     

Notes on interval estimation of the proportion ratio under a three-treatment three-period crossover trial

When comparing two experimental treatments with a placebo, we focus our attention on interval estimation of the proportion ratio (PR) of patient responses under a three-period crossover design. We propose a random effects exponential multiplicative risk model and derive asymptotic interval estimators in closed form for the PR between treatments and placebo. Using Monte Carlo simulations, we compare the performance of these interval estimators in a variety of situations. We use the data comparing two different doses of an analgesic with placebo for the relief of primary dysmenorrhea to illustrate the use of these interval estimators and the difference in estimates of the PR and odds ratio (OR) when the underlying relief rates are not small.     

Robust adaptive model selection and estimation for partial linear varying coefficient models in rank regression

Partial linear varying coefficient models are often used in real data analysis for a good balance between flexibility and parsimony. In this paper, we propose a robust adaptive model selection method based on the rank regression, which can do simultaneous coefficient estimation and three types of selections, i.e., varying and constant effects selection, relevant variable selection. The new method has superiority in robustness and efficiency by inheriting the advantage of the rank regression approach. Furthermore, consistency in the three types of selections and oracle property in estimation are established as well. Simulation studies also confirm our method.     

A generalized drop-the-loser rule for multi-treatment clinical trials

Urn models are popular for response adaptive designs in clinical studies. Among different urn models, Ivanova's drop-the-loser rule is capable of producing superior adaptive treatment allocation schemes. Ivanova [2003. A play-the-winner-type urn model with reduced variability. Metrika 58, 1–13] obtained the asymptotic normality only for two treatments. Recently, Zhang et al. [2007. Generalized drop-the-loser urn for clinical trials with delayed responses. Statist. Sinica, in press] extended the drop-the-loser rule to tackle more general circumstances. However, their discussion is also limited to only two treatments. In this paper, the drop-the-loser rule is generalized to multi-treatment clinical trials, and delayed responses are allowed. Moreover, the rule can be used to target any desired pre-specified allocation proportion. Asymptotic properties, including strong consistency and asymptotic normality, are also established for general multi-treatment cases.     

Goodness-of-fit tests for exponentiality based on a loss-of-memory type functional equation

Three goodness-of-fit tests for exponentiality based on the functional equation characterization 1?F(2x)=[1?F(x)]² for every x?0 are proposed and shown to compare well to several popular tests against common alternative cdf's. Small-sample critical values for α=0.10,0.05 are developed for the two superior test statistics and the asymptotic null-distributions are characterized.