GAMLSS: A distributional regression approach

A flexible regression approach using GAMLSS

Bayesian structured additive distributional regression

In this paper, we propose a generic Bayesian framework for inference in distributional regression models in which each parameter of a potentially complex response distribution and not only the mean is related to a structured additive predictor. The latter is composed additively of a variety of different functional effect types such as nonlinear effects, spatial effects, random coefficients, int...

Distributional Reinforcement Learning with Quantile Regression

In reinforcement learning an agent interacts with the environment by taking actions and observing the next state and reward. When sampled probabilistically, these state transitions, rewards, and actions can all induce randomness in the observed long-term return. Traditionally, reinforcement learning algorithms average over this randomness to estimate the value function. In this paper, we build ...

Package ‘ gamlss . dist ’

October 14, 2009 Type Package Title Distributions to be used for GAMLSS modelling. Version 3.0-1 Date 2009-14-09 Author Mikis Stasinopoulos , Bob Rigby with contributions from Calliope Akantziliotou, Gillian Heller, Raydonal Ospina and Nicoletta Motpan. Maintainer Mikis Stasinopoulos <[email protected]...

Portfolio Optimization: Distributional Approach

This paper analyses the stable distributional approach for portfolio optimisation. We consider a portfolio optimization problem under the assumption of normal (Gaussian) and stable (nonGaussian) distributed asset returns. We compare the results of portfolio allocations in normal and stable cases.