Zobrazeno 1 - 10
of 166
pro vyhledávání: '"Xie, Minge"'
Autor:
Xie, Minge, Wang, Peng
Rapid advancements in data science require us to have fundamentally new frameworks to tackle prevalent but highly non-trivial "irregular" inference problems, to which the large sample central limit theorem does not apply. Typical examples are those i
Externí odkaz:
http://arxiv.org/abs/2402.15004
Stemming from the high profile publication of Nissen and Wolski (2007) and subsequent discussions with divergent views on how to handle observed zero-total-event studies, defined to be studies which observe zero events in both treatment and control a
Externí odkaz:
http://arxiv.org/abs/2310.13178
Background: Outcome measures that are count variables with excessive zeros are common in health behaviors research. There is a lack of empirical data about the relative performance of prevailing statistical models when outcomes are zero-inflated, par
Externí odkaz:
http://arxiv.org/abs/2301.12674
Approximate confidence distribution computing (ACDC) offers a new take on the rapidly developing field of likelihood-free inference from within a frequentist framework. The appeal of this computational method for statistical inference hinges upon the
Externí odkaz:
http://arxiv.org/abs/2206.01707
Autor:
Thornton, Suzanne, Xie, Minge
Bayesian, frequentist and fiducial (BFF) inferences are much more congruous than they have been perceived historically in the scientific community (cf., Reid and Cox 2015; Kass 2011; Efron 1998). Most practitioners are probably more familiar with the
Externí odkaz:
http://arxiv.org/abs/2012.04464
Fusion learning refers to synthesizing inferences from multiple sources or studies to provide more effective inference and prediction than from any individual source or study alone. Most existing methods for synthesizing inferences rely on parametric
Externí odkaz:
http://arxiv.org/abs/2011.07047
Many clinical endpoint measures, such as the number of standard drinks consumed per week or the number of days that patients stayed in the hospital, are count data with excessive zeros. However, the zero-inflated nature of such outcomes is sometimes
Externí odkaz:
http://arxiv.org/abs/2009.10265
The flexibility and wide applicability of the Fisher randomization test (FRT) makes it an attractive tool for assessment of causal effects of interventions from modern-day randomized experiments that are increasing in size and complexity. This paper
Externí odkaz:
http://arxiv.org/abs/2004.08472
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