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pro vyhledávání: '"Armstrong, Timothy P."'
Empirical research typically involves a robustness-efficiency tradeoff. A researcher seeking to estimate a scalar parameter can invoke strong assumptions to motivate a restricted estimator that is precise but may be heavily biased, or they can relax
Externí odkaz:
http://arxiv.org/abs/2305.14265
We consider estimation and inference for a regression coefficient in panels with interactive fixed effects (i.e., with a factor structure). We demonstrate that existing estimators and confidence intervals (CIs) can be heavily biased and size-distorte
Externí odkaz:
http://arxiv.org/abs/2210.06639
Autor:
Armstrong, Timothy B.
Multiple testing adjustments, such as the Benjamini and Hochberg (1995) step-up procedure for controlling the false discovery rate (FDR), are typically applied to families of tests that control significance level in the classical sense: for each indi
Externí odkaz:
http://arxiv.org/abs/2209.13686
Autor:
Armstrong, Timothy B.
We consider an experimental design setting in which units are assigned to treatment after being sampled sequentially from an infinite population. We derive asymptotic efficiency bounds that apply to data from any experiment that assigns treatment as
Externí odkaz:
http://arxiv.org/abs/2205.02726
Autor:
Wozniak, Justin M., Armstrong, Timothy G., Maheshwari, Ketan C., Katz, Daniel S., Wilde, Michael, Foster, Ian T.
Scripting languages such as Python and R have been widely adopted as tools for the productive development of scientific software because of the power and expressiveness of the languages and available libraries. However, deploying scripted application
Externí odkaz:
http://arxiv.org/abs/2107.02841
We consider inference on a scalar regression coefficient under a constraint on the magnitude of the control coefficients. A class of estimators based on a regularized propensity score regression is shown to exactly solve a tradeoff between worst-case
Externí odkaz:
http://arxiv.org/abs/2012.14823
Publikováno v:
Econometrica, Volume 90, Issue 6, November 2021, pages 2567-2602
We construct robust empirical Bayes confidence intervals (EBCIs) in a normal means problem. The intervals are centered at the usual linear empirical Bayes estimator, but use a critical value accounting for shrinkage. Parametric EBCIs that assume a no
Externí odkaz:
http://arxiv.org/abs/2004.03448
Autor:
Wozniak, Justin M., Sharma, Hemant, Armstrong, Timothy G., Wilde, Michael, Almer, Jonathan D., Foster, Ian
New techniques in X-ray scattering science experiments produce large data sets that can require millions of high-performance processing hours per week of computation for analysis. In such applications, data is typically moved from X-ray detectors to
Externí odkaz:
http://arxiv.org/abs/2002.06258
Autor:
Armstrong, Timothy B.
We derive bounds on the scope for a confidence band to adapt to the unknown regularity of a nonparametric function that is observed with noise, such as a regression function or density, under the self-similarity condition proposed by Gine and Nickl (
Externí odkaz:
http://arxiv.org/abs/1810.09762
Autor:
Armstrong, Timothy B., Kolesár, Michal
Publikováno v:
Quantitative Economics, Volume 12, Issue 1, January 2021, pages 77-108
We consider inference in models defined by approximate moment conditions. We show that near-optimal confidence intervals (CIs) can be formed by taking a generalized method of moments (GMM) estimator, and adding and subtracting the standard error time
Externí odkaz:
http://arxiv.org/abs/1808.07387