We consider the problem of predicting measurable responses to scientific articles based primarily on their text content. Specifically, we consider papers in two fields (economics and computational linguistics) and make predictions about downloads and within-community citations. Our approach is based on generalized linear models, allowing interpretability; a novel extension that captures first-o...

Over the last 20 to 30 years, there has been a significant amount of tools and statistical methods that have been proposed for analyzing crash data. Yet, the Poisson-gamma (PG) is still the most commonly used and widely acceptable model. This paper documents the application of the Poisson-Weibull (PW) generalized linear model (GLM) for modeling motor vehicle crashes. The objectives of this stud...

Theobjective of thiswork is to propose a statisticalmethodology to handle regressiondata exhibiting long memory errors and missing values. This type of data appears very often in many areas, including hydrology and environmental sciences, among others. A generalized linear model is proposed to deal with this problem and an estimation strategy is developed that combines both classical and Bayesi...

Generalized linear models (GLMs) have been used widely for modelling the mean response both for discrete and continuous random variables with an emphasis on categorical response. Recently Yang, Mandal and Majumdar (2013) considered full factorial and fractional factorial locally D-optimal designs for binary response and two-level experimental factors. In this paper, we extend their results to a...

We devise a one-shot approach to distributed sparse regression in the high-dimensional setting. The key idea is to average " debiased " or " desparsified " lasso estimators. We show the approach converges at the same rate as the lasso as long as the dataset is not split across too many machines. We also extend the approach to generalized linear models.

In general, the distribution of residuals cannot be obtained explicitly. We give an asymptotic formula for the density of Pearson residuals in continuous generalized linear models corrected to order n−1, where n is the sample size. We define corrected Pearson residuals for these models that, to this order of approximation, have exactly the same distribution of the true Pearson residuals. Applic...

We introduce a class of exible conditional probability models and techniques for classi cation regression problems Many existing methods such as generalized linear models and support vector machines are subsumed under this class The exibility of this class of techniques comes from the use of kernel functions as in support vector machines and the generality from dual formulations of stan dard re...

Complex binary traits result from an intricate network of genetic and environmental factors. To aid their genetic dissection, several generalized linear models have been used to detect interaction between genes. However, it is recognized that these models have limited genetic interpretation. As an attempt to overcome this problem, we have previously proposed the allelic penetrance approach to m...

We consider a Bayesian analysis of Binomial response data with covariates. To describe the problem under investigation, suppose we have n independent binomial observations Y1, . . . , Yn where Yi ∼ Bin(mi, θi) and let xi be p-dimensional covariate vector associated with Yi for i = 1, . . . , n. Binomial observations can be analyzed through a generalized linear model (GLM) where we assume θi = F...

Statistical process control methods for monitoring processes with univariate ormultivariate measurements are used widely when the quality variables fit to known probabilitydistributions. Some processes, however, are better characterized by a profile or a function of qualityvariables. For each profile, it is assumed that a collection of data on the response variable along withthe values of the c...