The goal of precision medicine is to tailor treatment strategies on an individual patient level. Although several estimation techniques have been developed for determining optimal treatment rules, the majority of methods focus on the case of a dichotomous treatment, an example being the dynamic weighted ordinary least squares regression approach of Wallace and Moodie (2015). We propose an extension to the aforementioned framework to allow for a continuous treatment with the ultimate goal of estimating optimal dosing strategies. The proposed method is shown to be doubly robust against model misspecification whenever the implemented weights satisfy a particular balancing condition. A broad class of weight functions can be derived from the balancing condition, providing a flexible regression based estimation method in the context of adaptive treatment strategies for continuous valued treatments.

In Brazil, a significant part of the biomass is unused, contributing to environmental pollution. The tannin industry commonly extracts tannins from the bark of Acacia mearnsii or black wattle, leaving a significant residue of 70% (w w⁻¹). This study investigates the conversion of black wattle bark into a porous carbonaceous material to efficiently remove organic pollutants. Using ZnCl₂ as a chemical activation reagent, the experiments varied the impregnation time, carbonization rates, and temperatures. Additional experiments aimed to increase the specific surface area (SSA). X-ray diffraction (XRD) analysis showed the formation and removal of ZnO, which increased porosity. Scanning electron microscopy (SEM) showed an irregular morphology with pores. Fourier-transform infrared (FTIR) spectroscopy indicated characteristic bands, and electron paramagnetic resonance (EPR) detected organic free radicals. The SSAs exceeded 1000 m² g⁻¹, averaging 1360 m² g⁻¹, with a maximum of 1525 m² g⁻¹. Micropores (1.4 nm) were consistent. The structure of the material and the high SSA suggest a potential for efficient removal of aromatic impurities by π–π interactions. This approach addresses the issue of biomass waste, provides a solution for environmental remediation, and represents a transformative strategy for biomass utilization.

Summary: Multivariate count data arise naturally in practice. In analysing such data, it is critical to define a model that can accurately capture the underlying dependence structure between the counts. To this end, this paper develops a multivariate model wherein correlated Poisson margins are generated by a comonotonic shock vector. The proposed model allows for greater flexibility in the dependence structure than that of the classical construction, which relies on the convolution of vectors of common Poisson shock variables. Several probabilistic properties of the multivariate comonotonic shock Poisson model are established, and various estimation strategies are discussed in detail. The model is further studied through simulations, and its utility is highlighted using a real data application.

The positivity or experimental treatment assignment assumption is a fundamental requirement in causal analyses, invoked to ensure that identifiability holds without extrapolating beyond what the observed data can reveal. Positivity is well understood in the context of binary and categorical treatments, and has been thoroughly discussed—from how the assumption can be assessed to approaches that may be used when the assumption is suspected not to hold. Positivity extends to the context of continuous exposures, such as doses, however it has been given very little formal consideration. In this manuscript, we propose a method for assessing whether the positivity assumption is violated in a given dataset, relying on a principled concept in regression analysis. We demonstrate the diagnostic tool in various simulated settings, as well as in an application involving warfarin dosing.

Multi‐dimensional data frequently occur in many different fields, including risk management, insurance, biology, environmental sciences, and many more. In analyzing multivariate data, it is imperative that the underlying modelling assumptions adequately reflect both the marginal behaviour and the associations between components. This article focuses specifically on developing a new multivariate Poisson model appropriate for multi‐dimensional count data. The proposed formulation is based on convolutions of comonotonic shock vectors with Poisson‐distributed components and allows for flexibility in capturing different degrees of positive dependence. In this article, we will present the general model framework along with various distributional properties. Several estimation techniques will be explored and assessed both through simulations and in a real data application involving extreme rainfall events.

Multi-dimensional data frequently occur in many different fields, including risk management, insurance, biology, environmental sciences, and many more. In analyzing multivariate data, it is imperative that the underlying modelling assumptions adequately reflect both the marginal behavior as well as the associations between components. This work focuses specifically on developing a new multivariate Poisson model appropriate for multi-dimensional count data. The proposed formulation is based on convolutions of comonotonic shock vectors with Poisson distributed components and allows for flexibility in capturing different degrees of positive dependence. In this paper, the general model framework will be presented along with various distributional properties. Several estimation techniques will be explored and assessed both through simulations and in a real data application involving extreme rainfall events.