Zobrazeno 1 - 10
of 182
pro vyhledávání: '"Zame, William R."'
Autor:
Zame, William R.
The continuous time model of dynamic asset trading is the central model of modern finance. Because trading cannot in fact take place at every moment of time, it would seem desirable to show that the continuous time model can be viewed as the limit of
Externí odkaz:
http://arxiv.org/abs/2207.03397
Modeling the preferences of agents over a set of alternatives is a principal concern in many areas. The dominant approach has been to find a single reward/utility function with the property that alternatives yielding higher rewards are preferred over
Externí odkaz:
http://arxiv.org/abs/2202.10153
Modeling a system's temporal behaviour in reaction to external stimuli is a fundamental problem in many areas. Pure Machine Learning (ML) approaches often fail in the small sample regime and cannot provide actionable insights beyond predictions. A pr
Externí odkaz:
http://arxiv.org/abs/2106.02875
Randomized Controlled Trials (RCTs) are the gold standard for comparing the effectiveness of a new treatment to the current one (the control). Most RCTs allocate the patients to the treatment group and the control group by uniform randomization. We s
Externí odkaz:
http://arxiv.org/abs/1810.02876
Choosing optimal (or at least better) policies is an important problem in domains from medicine to education to finance and many others. One approach to this problem is through controlled experiments/trials - but controlled experiments are expensive.
Externí odkaz:
http://arxiv.org/abs/1802.08679
Missing data is a ubiquitous problem. It is especially challenging in medical settings because many streams of measurements are collected at different - and often irregular - times. Accurate estimation of those missing measurements is critical for ma
Externí odkaz:
http://arxiv.org/abs/1711.08742
Autor:
Rosenthal, Howard, Zame, William R.
Publikováno v:
In Journal of Public Economics August 2022 212
Publikováno v:
In Journal of Economic Dynamics and Control August 2022 141
We present a new approach to ensemble learning. Our approach constructs a tree of subsets of the feature space and associates a predictor (predictive model) - determined by training one of a given family of base learners on an endogenously determined
Externí odkaz:
http://arxiv.org/abs/1706.01396
This paper proposes a novel approach for constructing effective personalized policies when the observed data lacks counter-factual information, is biased and possesses many features. The approach is applicable in a wide variety of settings from healt
Externí odkaz:
http://arxiv.org/abs/1612.08082