A model specification test for semiparametric nonignorable missing data modeling.

The instrumental variable approaches have been demonstrated effective for semiparametrically modeling the propensity function in analyzing data that may be missing not at random. A model specification test is considered for a class of parsimonious semiparametric propensity models. The test is constructed based on assessing an over-identification so as to detect possible incompatibility in the moment conditions when the model and/or instrumental variables are misspecified.

Testing the missing at random assumption in generalized linear models in the presence of instrumental variables.

Practical problems with missing data are common, and many methods have been developed concerning the validity and/or efficiency of statistical procedures. On a central focus, there have been longstanding interests on the mechanism governing data missingness, and correctly deciding the appropriate mechanism is crucially relevant for conducting proper practical investigations. In this paper, we present a new hypothesis testing approach for deciding between the conventional notions of missing at random and missing not at random in generalized linear models in the presence of instrumental variables.

Information criteria for latent factor models: a study on factor pervasiveness and adaptivity.

We study the information criteria extensively under general conditions for high-dimensional latent factor models. Upon carefully analyzing the estimation errors of the principal component analysis method, we establish theoretical results on the estimation accuracy of the latent factor scores, incorporating the impact from possibly weak factor pervasiveness; our analysis does not require the same factor strength of all the leading factors. To estimate the number of the latent factors, we propose a new penalty specification with a two-fold consideration: i) being adaptive to the strength of the factor pervasiveness, and ii) favoring more parsimonious models.

A cost-effective chart review sampling design to account for phenotyping error in electronic health records (EHR) data.

Objectives: Electronic health records (EHR) are commonly used for the identification of novel risk factors for disease, often referred to as an association study. A major challenge to EHR-based association studies is phenotyping error in EHR-derived outcomes. A manual chart review of phenotypes is necessary for unbiased evaluation of risk factor associations.

Empirical Frequency Band Analysis of Nonstationary Time Series.

The time-varying power spectrum of a time series process is a bivariate function that quantifies the magnitude of oscillations at different frequencies and times. To obtain low-dimensional, parsimonious measures from this functional parameter, applied researchers consider collapsed measures of power within local bands that partition the frequency space. Frequency bands commonly used in the scientific literature were historically derived, but they are not guaranteed to be optimal or justified for adequately summarizing information from a given time series process under current study.

LOCAL INDEPENDENCE FEATURE SCREENING FOR NONPARAMETRIC AND SEMIPARAMETRIC MODELS BY MARGINAL EMPIRICAL LIKELIHOOD.

We consider an independence feature screening technique for identifying explanatory variables that locally contribute to the response variable in high-dimensional regression analysis. Without requiring a specific parametric form of the underlying data model, our approach accommodates a wide spectrum of nonparametric and semiparametric model families. To detect the local contributions of explanatory variables, our approach constructs empirical likelihood locally in conjunction with marginal nonparametric regressions.

Resveratrol preconditioning protects hepatocytes against hepatic ischemia reperfusion injury via Toll-like receptor 4/nuclear factor-κB signaling pathway in vitro and in vivo.

The purpose of this study was to investigate the protective effect of resveratrol against hepatic ischemia reperfusion injury (HIRI) and explore the potential underlying mechanism. Resveratrol-pretreated BRL-3A (rat liver) cells and rats underwent hypoxia/reoxygenation and hepatic ischemia/reperfusion, respectively. BRL-3A cell damage was evaluated, and the mRNA and protein expression of related signal molecules was assessed in cell model.

Everolimus in de novo liver transplant recipients: a systematic review.

Background: Everolimus has no nephrotoxicity and is used to treat patients with post-liver transplant chronic renal insufficiency. The present systematic review was to evaluate the efficacy and safety of everolimus in de novo liver transplant patients.

Data Sources: Randomized controlled trials comparing everolimus for de novo liver transplant in PubMed, the Cochrane Library, and ScienceDirect published up to March 31, 2014 were searched by two independent reviewers.

MARGINAL EMPIRICAL LIKELIHOOD AND SURE INDEPENDENCE FEATURE SCREENING.

We study a marginal empirical likelihood approach in scenarios when the number of variables grows exponentially with the sample size. The marginal empirical likelihood ratios as functions of the parameters of interest are systematically examined, and we find that the marginal empirical likelihood ratio evaluated at zero can be used to differentiate whether an explanatory variable is contributing to a response variable or not. Based on this finding, we propose a unified feature screening procedure for linear models and the generalized linear models.