Are nonconscious processes sufficient to cause false memories of a nonstudied event? To investigate this issue, we controlled and measured conscious processing in the DRM task, in which studying associates (e.g., bed, rest, awake...) causes false memories of nonstudied associates (e.g., sleep). During the study phase, subjects studied visually masked associates at extremely rapid rates, followe...

Primary liver cancer has several well-recognized risk factors, such as HBV and HCV infection, alcohol abuse and aflatoxin. Recent studies show that nonalcoholic fatty liver disease (NAFLD), especially its aggressive form nonalcoholic steatohepatitis (NASH) is associated with an increased risk of liver cancer, mainly hepatocellular carcinoma (HCC). On the other hand, clinical and epidemiological...

First, outcome variable of “duration of comatose state” included all patients irrespective of their survival status. Failure to censor this time-to-event ‘duration of coma’ variable by mortality in this study is a major methodological flaw in evaluating treatment efficacy. Indeed, more aptly definable endpoint would be the “duration of resolution of abnormal mental status (or length of time to ...

Observational studies assessing causal or non-causal relationships between an explanatory measure and an outcome can be complicated by hosts of confounding measures. Large numbers of confounders can lead to several biases in conventional regression based estimation. Inference is more easily conducted if we reduce the number of confounders to a more manageable number. We discuss use of sufficien...

We study the asymptotic behavior of a class of methods for sufficient dimension reduction in high-dimension regressions, as the sample size and number of predictors grow in various alignments. It is demonstrated that these methods are consistent in a variety of settings, particularly in abundant regressions where most predictors contribute some information on the response, and oracle rates are ...

Although the concept of sufficient dimension reduction has been proposed for a long time, studies in the literature have largely focused on properties of estimators of dimension-reduction subspaces in the classical “small p, and large n” setting. Rather than the subspace, this paper considers directly the set of reduced predictors, which we believe are more relevant for subsequent analyses, and...

A family of dimension reduction methods was developed by Cook and Ni [Sufficient dimension reduction via inverse regression: a minimum discrepancy approach. J. Amer. Statist. Assoc. 100, 410–428.] via minimizing a quadratic objective function. Its optimal member called the inverse regression estimator (IRE) was proposed. However, its calculation involves higher order moments of the predictors. ...