Results 31 to 40 of about 166,649 (278)
PLoS Genetics, 2012 An important step in analyzing genetic association study data is deciding whether to adjust for covariates—those variables ancillary to the variants of interest. In particular, when testing for novel associations, should the statistical model also include known genetic or nongenetic covariates that are predictors of the trait (e.g., body mass index ...
John S. Witte, Joel Mefford
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Inference under Covariate-Adaptive Randomization with Multiple Treatments [PDF]
Quantitative Economics, 2018 This paper studies inference in randomized controlled trials with covariate‐adaptive randomization when there are multiple treatments. More specifically, we study in this setting inference about the average effect of one or more treatments relative to ...
Federico A. Bugni +2 more
semanticscholar +1 more source
Physical Review D, 2009 10 pages, no figure, RevTeX4 format; v2 adds footnote 1, Ref. [12], reformats the link in Ref.
Deffayet, C. +2 more
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Femtosecond covariance spectroscopy [PDF]
Proceedings of the National Academy of Sciences, 2019 Significance Here we establish femtosecond covariance spectroscopy as a technique that uses ultrashort stochastic light pulses to measure nonlinear material responses. By using pulses with spectrally uncorrelated fluctuations we can leverage on the noise and consider each repetition of the experiment as a measurement under different ...
Angela Montanaro +19 more
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Capacity of the covariance perceptron [PDF]
Journal of Physics A: Mathematical and Theoretical, 2020 Abstract The classical perceptron is a simple neural network that performs a binary classification by a linear mapping between static inputs and outputs and application of a threshold. For small inputs, neural networks in a stationary state also perform an effectively linear input–output transformation, but of an entire time series ...
Dahmen, David +2 more
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Marginal Singularity, and the Benefits of Labels in Covariate-Shift [PDF]
Annual Conference Computational Learning Theory, 2018 We present new minimax results that concisely capture the relative benefits of source and target labeled data, under covariate-shift. Namely, we show that the benefits of target labels are controlled by a transfer-exponent $\gamma$ that encodes how ...
Samory Kpotufe, Guillaume Martinet
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An introduction to the full random effects model
CPT: Pharmacometrics & Systems Pharmacology, 2022 The full random‐effects model (FREM) is a method for determining covariate effects in mixed‐effects models. Covariates are modeled as random variables, described by mean and variance.
Gunnar Yngman +4 more
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Covariate balancing propensity score
, 2014 The propensity score plays a central role in a variety of causal inference settings. In particular, matching and weighting methods based on the estimated propensity score have become increasingly common in the analysis of observational data.
K. Imai, Marc Ratkovic
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Stats, 2022 Paired outcomes are common in correlated clustered data where the main aim is to compare the distributions of the outcomes in a pair. In such clustered paired data, informative cluster sizes can occur when the number of pairs in a cluster (i.e., a ...
Sandipan Dutta
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, 2018 Propensity score matching and weighting are popular methods when estimating causal eects in observational studies. Beyond the assumption of unconfoundedness, however, these methods also require the model for the propensity score to be correctly specied ...
Christian Fong, C. Hazlett, K. Imai
semanticscholar +1 more source