Generalized Poisson Hurdle Model for Count Data and Its Application in Ear Disease.

For count data, though a zero-inflated model can work perfectly well with an excess of zeroes and the generalized Poisson model can tackle over- or under-dispersion, most models cannot simultaneously deal with both zero-inflated or zero-deflated data and over- or under-dispersion. Ear diseases are important in healthcare, and falls into this kind of count data. This paper introduces a generalized Poisson Hurdle model that work with count data of both too many/few zeroes and a sample variance not equal to the mean.

Pst DC3000 infection alleviates subsequent freezing and heat injury to host plants via a salicylic acid-dependent pathway in Arabidopsis.

Abiotic stresses greatly affect the immunity of plants. However, it is unknown whether pathogen infection affects abiotic stress tolerance of host plants. Here, the effect of defense response on cold and heat tolerance of host plants was investigated in Pst DC3000-infected Arabidopsis plants, and it was found that the pathogen-induced defense response could alleviate the injury caused by subsequent cold and heat stress (38°C).

Double-smoothing for Varying Coefficient Models.

Moderation analyses are widely used in biomedical and psychosocial research to investigate differential treatment effects, with moderators frequently identified through testing the significance of the interaction between the predictor and the potential moderator under strong parametric assumptions. Without imposing any parametric forms on how the moderators may affect the relationship between predictors and responses, varying coefficient models address this fundamental problem of strong parametric assumptions with current practice of moderation analysis and provide a much broader class of models for complex moderation relationships. Local polynomial, especially local linear, methods are commonly used in estimating the varying coefficient models.

A baseline-free procedure for transformation models under interval censorship.

An important property of Cox regression model is that the estimation of regression parameters using the partial likelihood procedure does not depend on its baseline survival function. We call such a procedure baseline-free. Using marginal likelihood, we show that an baseline-free procedure can be derived for a class of general transformation models under interval censoring framework.