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L. Brown

31 papers hereh-index 5513.6k citations262 works total

Matching runs newest-first, so older work may not be attached to this profile yet.

author position
  • sole author2
  • first author11
  • middle author16
  • last author2

Across the 31 of 31 papers where every author was matched, so the position is known.

fields
  • math.ST20
  • stat.ME6
  • stat.AP4
  • math.PR1
same name
  • L. Brown — 21 papers, h 38
  • L. Brown — 10 papers, h 5
  • L. Brown — 5 papers, h 18
  • L. Brown — 3 papers, h 57
  • L. Brown — 3 papers, h 18
  • L. Brown — 2 papers, h 11

Either other researchers who publish under this name, or the same person where the external sources have not merged their records.

identity via Semantic Scholar / OpenAlex

activity
20022022
most citedValid post-selection inference

626 citations · 1.4k across the 31 of their papers we have counts for

collaborators
Showing 2018Show all

4 papers · 1 filter

math.ST2018★ 8 cited

Model-free Study of Ordinary Least Squares Linear Regression

Arun K. Kuchibhotla, Lawrence D. Brown, Andreas Buja

Ordinary least squares (OLS) linear regression is one of the most basic statistical techniques for data analysis. In the main stream literature and the statistical education, the s…

stat.ME2018

Assumption Lean Regression

Richard A. Berk, Andreas Buja, Lawrence Brown +4

It is well known that models used in conventional regression analysis are commonly misspecified. A standard response is little more than a shrug. Data analysts invoke Box's maxim t…

stat.ME2018★ 7 cited

Valid Post-selection Inference in Assumption-lean Linear Regression

Arun Kumar Kuchibhotla, Lawrence D. Brown, Andreas Buja +2

Construction of valid statistical inference for estimators based on data-driven selection has received a lot of attention in the recent times. Berk et al. (2013) is possibly the fi…

math.ST2018

Uniform-in-Submodel Bounds for Linear Regression in a Model Free Framework

Arun Kumar Kuchibhotla, Lawrence D. Brown, Andreas Buja +2

For the last two decades, high-dimensional data and methods have proliferated throughout the literature. Yet, the classical technique of linear regression has not lost its usefulne…

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Not affiliated with arXiv. Researcher data from Semantic Scholar (ODC-BY) and OpenAlex.