Background There is increasing interest in deploying Screening, Brief Intervention, and Referral to Treatment (SBIRT) practices in emergency departments (ED). However, the current literature is inconclusive on whether or not SBIRT practices are cost effective and cost beneficial. In order to answer this question, new analytical methods need to be developed. The objective of the following study ...

We consider the problem of claims reserving and estimating run-off triangles. We generalize the gamma cell distributions model which leads to Tweedie’s compound Poisson model. Choosing a suitable parametrization, we estimate the parameters of our model within the framework of generalized linear models (see Jørgensen-de Souza [2] and Smyth-Jørgensen [8]). We show that these methods lead to reaso...

Electrical stimulation of the skin using a needle-electrode specifically activates nociceptive nerve fibres. The detection of such stimuli by subjects depends on the activation of subsequent nociceptive mechanisms. Activation of these mechanisms depends on the temporal stimulus properties, such as the pulse-width, number of pulses, and inter-pulse interval. This different activation of nocicept...

This paper presents theory for Normalized Random Measures (NRMs), Normalized Generalized Gammas (NGGs), a particular kind of NRM, and Dependent Hierarchical NRMs which allow networks of dependent NRMs to be analysed. These have been used, for instance, for time-dependent topic modelling. In this paper, we first introduce some mathematical background of completely random measures (CRMs) and thei...

We propose Dirichlet Process mixtures of Generalized Linear Models (DP-GLM), a new class of methods for nonparametric regression. Given a data set of input-response pairs, the DP-GLM produces a global model of the joint distribution through a mixture of local generalized linear models. DP-GLMs allow both continuous and categorical inputs, and can model the same class of responses that can be mo...

We develop a dynamic Bayesian beta model for modeling and forecasting single time series of proportions. This work is related to the class of the so called dynamic generalized linear models (DGLM). We use non-conjugate priors and some forms of approximate Bayesian analysis, including Linear Bayesian estimation. Some applications to both real and simulated data are provided.

We present a novel multilabel/ranking algorithm working in partial information settings. The algorithm is based on 2nd-order descent methods, and relies on upper-confidence bounds to trade-off exploration and exploitation. We analyze this algorithm in a partial adversarial setting, where covariates can be adversarial, but multilabel probabilities are ruled by (generalized) linear models. We sho...

We study robust high-dimensional estimation of generalized linear models (GLMs); where a small number k of the n observations can be arbitrarily corrupted, and where the true parameter is high dimensional in the “p n” regime, but only has a small number s of non-zero entries. There has been some recent work connecting robustness and sparsity, in the context of linear regression with corrupted o...

We propose a Generalized Dantzig Selector (GDS) for linear models, in which any norm encoding the parameter structure can be leveraged for estimation. We investigate both computational and statistical aspects of the GDS. Based on conjugate proximal operator, a flexible inexact ADMM framework is designed for solving GDS. Thereafter, non-asymptotic high-probability bounds are established on the e...

In the last four decades, human fertility has declined across the globe, a trend that policy experts predict will continue. As a result, international migration will increasingly affect the demographic future of many nations. Joel Cohen et al. devised a generalized linear model to predict the yearly number of regional and international migrants. The model includes projected demographic variable...