activity
20072023
most citedEndogenous post-stratification in surveys: classifying with a sample-fitted model

57 citations · 126 across the 6 of their papers we have counts for

collaborators

6 papers

stat.ME2023

A flexible class of priors for orthonormal matrices with basis function-specific structure

Joshua S. North, Mark D. Risser, F. Jay Breidt

Statistical modeling of high-dimensional matrix-valued data motivates the use of a low-rank representation that simultaneously summarizes key characteristics of the data and enable…

math.ST2012★ 12 cited

Uniform convergence of the empirical cumulative distribution function under informative selection from a finite population

Daniel Bonnéry, F. Jay Breidt, François Coquet

Consider informative selection of a sample from a finite population. Responses are realized as independent and identically distributed (i.i.d.) random variables with a probability…

math.ST2008★ 57 cited

Endogenous post-stratification in surveys: classifying with a sample-fitted model

F. Jay Breidt, Jean D. Opsomer

Post-stratification is frequently used to improve the precision of survey estimators when categorical auxiliary information is available from sources outside the survey. In natural…

stat.ME2007★ 2 cited

Comment: Struggles with Survey Weighting and Regression Modeling

F. Jay Breidt, Jean D. Opsomer

Comment: Struggles with Survey Weighting and Regression Modeling [arXiv:0710.5005]

math.ST2007★ 22 cited

Rank-based estimation for all-pass time series models

Beth Andrews, Richard A. Davis, F. Jay Breidt

An autoregressive-moving average model in which all roots of the autoregressive polynomial are reciprocals of roots of the moving average polynomial and vice versa is called an all…

math.ST2007★ 33 cited

Pile-up probabilities for the Laplace likelihood estimator of a non-invertible first order moving average

F. Jay Breidt, Richard A. Davis, Nan-Jung Hsu +1

The first-order moving average model or MA(1) is given by , with independent and identically distributed . This is arguably the simplest time series mo…