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pro vyhledávání: '"Abramson, Mark"'
It can be difficult to interpret a coefficient of an uncertain model. A slope coefficient of a regression model may change as covariates are added or removed from the model. In the context of high-dimensional data, there are too many model extensions
Externí odkaz:
http://arxiv.org/abs/2408.09634
For the purpose of causal inference we employ a stochastic model of the data generating process, utilizing individual propensity probabilities for the treatment, and also individual and counterfactual prognosis probabilities for the outcome. We assum
Externí odkaz:
http://arxiv.org/abs/2407.08862
In this article, we consider a continuous review (s, S) inventory system with failures of demand fulfillment (service) modeled as a Markov-modulated retrial queueing system. The inventory system features a single product that experiences Markovian in
Externí odkaz:
http://arxiv.org/abs/2109.12463
When studying the causal effect of $x$ on $y$, researchers may conduct regression and report a confidence interval for the slope coefficient $\beta_{x}$. This common confidence interval provides an assessment of uncertainty from sampling error, but i
Externí odkaz:
http://arxiv.org/abs/1908.08596
Autor:
Abramson, Mark Aaron
A new class of algorithms for solving nonlinearly constrained mixed variable optimization problems is presented. The Audet-Dennis Generalized Pattern Search (GPS) algorithm for bound constrained mixed variable optimization problems is extended to pro
Externí odkaz:
http://hdl.handle.net/1911/18502
Autor:
Abramson, Mark A.
Publikováno v:
Public Administration Review, 2007 Jan 01. 67(1), 127-134.
Externí odkaz:
https://www.jstor.org/stable/4624546
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