Titlebook: Statistical Methods for Dynamic Treatment Regimes; Reinforcement Learni Bibhas Chakraborty,Erica E.M. Moodie Textbook 2013 Springer Science

聰明

noxious

The Data: Observational Studies and Sequentially Randomized Trials,rform valid analyses in each, along with some examples. We also discuss a basic framework of causal inference in the context of observational studies, and power and sample size issues in the context of randomized studies.

debacle

Estimation of Optimal DTRs by Directly Modeling Regimes,mental difference between the approaches considered in the current chapter and those considered in previous chapters (e.g. Q-learning and G-estimation) lies in the primary target of estimation (and inference): the methods considered presently target the parameters of the decision rule itself.

muscle-fibers

Inference and Non-regularity, illustrate the phenomenon using Q-learning and G-estimation, and propose a number of strategies to mitigate the non-regularity including thresholding and penalization of the estimators as well as non-standard implementations of the bootstrap.

CHARM

Lennart Rohlfs,Julien Weiss the company or even reshaping the forces and direction of a whole industry. What allows strategic leaders make great decisions? Is this pure chance or a skill that has been developed through many years of experience? We argue that it is strategic leadership wisdom manifesting in the leaders’ abilit

GEM

Melina Bosco,Marco Caragliano,Edoardo M. Marino,Pier Paolo Rossin we present characterization theorems for three types of problems: Finite dimensional, variational and problems in linear topological spaces. In each case we present theorems for equality — inequality constraints. The theorems in each case are: first order necessary conditions, first order sufficie

construct

eustachian-tube

Armada

社團(tuán)

Multi-Object Tracking based on Imaging Radar 3D Object Detection,vior and therefore adequate planning of the ego vehicle trajectory. One approach for detecting and tracking surrounding traffic participants is the combination of a learningbased object detector with a classical tracking algorithm. Learning based object detectors have been shown to work adequately o