Large-scale inference: empirical Bayes methods for estimation, testing, and prediction

By I. Dialsingh. 2012

498 citations recorded by the source corpus.

View Paper: https://www.semanticscholar.org/paper/6f7c4b5dba6e92339fae4c92ceb81d93c191b4bc

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Statistics and Probability

Citations in the corpus

Controlling false positive selections in high-dimensional regression and causal inference (2013, background): “large-scale multiple testing of many marginal parameters [6] or global tests [10].”
Variable selection using pseudo-variables (2018, methodology): “Our second illustrative example used data from leukemia study (see Efron, 2012).”
Admissible ways of merging p-values under arbitrary dependence (2020, unknown): “Hence, such a method cannot be justiﬁed unless convincing evidence of independence is supplied; however, as argued by Efron (2010, pp. 50–51), neither independence nor positive regression dependence, which is often assumed in literature, is realistic in large-scale inference.”
Empirical Bayesball Remixed: Empirical Bayes Methods for Longitudinal Data (2017, background): “As stressed in recent work of Efron (2010, 2011), empirical Bayes methods pioneered by Robbins (1951, 1956) provide a statistical framework for many contemporary “big data” applications.”
Multivariate Rotated ARCH Models (2014, unknown): “To control for this e¤ect, Efron and Morris (1972) introduced the limited translation (LT) estimator:”
A MIDAS Approach to Modeling First and Second Moment Dynamics (2016, unknown): “(7)The link between MIDAS models and time varying volatility has also been explored by Engle et al. (2013) who use a MIDAS-GARCH approach to link macroeconomic variables to the long-run component of volatility.”
Confidence intervals and hypothesis testing for high-dimensional regression (2013, methodology): “n)j!1 with n. Even in the seemingly simple case in which a correct p-value is given for each individual coordinate, the problem of aggregating them has attracted considerable amount of work, see e.g. [Efr10] for an overview. Here we limit ourself to designing a testing procedure for the family of hy”