B-Spline Modeling of Inertial Measurements for Evaluating Stroke Rehabilitation Effectiveness.

Patients who experience upper-limb paralysis after stroke require continual rehabilitation. Rehabilitation must be evaluated for appropriate treatment adjustment; such evaluation can be performed using inertial measurement units (IMUs) instead of standard scales or subjective evaluations. However, IMUs produce large quantities of discretized data, and using these data directly is challenging.

Distribution-free model selection for longitudinal zero-inflated count data with missing responses and covariates.

In many medical and social science studies, count responses with excess zeros are very common and often the primary outcome of interest. Such count responses are usually generated under some clustered correlation structures due to longitudinal observations of subjects. To model such longitudinal count data with excess zeros, the zero-inflated binomial (ZIB) models for bounded outcomes, and the zero-inflated negative binomial (ZINB) and zero-inflated poisson (ZIP) models for unbounded outcomes all are popular methods.

A novel accessory protein ArCel5 from cellulose-gelatinizing fungus Arthrobotrys sp. CX1.

Improved understanding of cellulose swelling mechanism is beneficial for increasing the hydrolysis efficiency of cellulosic substrates. Here, we report a family 5 glycoside hydrolase ArCel5 isolated from the cellulose-gelatinizing fungus Arthrobotrys sp. CX1.

On summary ROC curve for dichotomous diagnostic studies: an application to meta-analysis of COVID-19.

In a systematic review of a diagnostic performance, summarizing performance metrics is crucial. There are various summary models in the literature, and hence model selection becomes inevitable. However, most existing large-sample-based model selection approaches may not fit in a meta-analysis of diagnostic studies, typically having a rather small sample size.

Model selection based on resampling approaches for cluster longitudinal data with missingness in outcomes.

In medical and health studies, longitudinal and cluster longitudinal data are often collected, where the response variable of interest is observed repeatedly over time and along with a set of covariates. Model selection becomes an active research topic but has not been explored largely due to the complex correlation structure of the data set. To address this important issue, in this paper, we concentrate on model selection of cluster longitudinal data especially when data are subject to missingness.

Estimating the Probability of Rare Events Occurring Using a Local Model Averaging.

In statistical applications, logistic regression is a popular method for analyzing binary data accompanied by explanatory variables. But when one of the two outcomes is rare, the estimation of model parameters has been shown to be severely biased and hence estimating the probability of rare events occurring based on a logistic regression model would be inaccurate. In this article, we focus on estimating the probability of rare events occurring based on logistic regression models.