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› A scalable surrogate <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" display="inline" id="d1e479" altimg="si5.svg"><mml:msub><mml:mrow><mml:mi>L</mml:mi></mml:mrow><mml:mrow><mml:mn>0</mml:mn></mml:mrow></mml:msub></mml:math> sparse regression method for generalized linear models with applications to large scale data
A scalable surrogate <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" display="inline" id="d1e479" altimg="si5.svg"><mml:msub><mml:mrow><mml:mi>L</mml:mi></mml:mrow><mml:mrow><mml:mn>0</mml:mn></mml:mrow></mml:msub></mml:math> sparse regression method for generalized linear models with applications to large scale data
Research article (Journal of Statistical Planning and Inference, 2020) · cited 14× · AI/ML
A scalable surrogate L0 sparse regression method for generalized linear models with applications to large scale data
Summary
A scalable surrogate L0 sparse regression method for generalized linear models with applications to large scale data is a scholarly article<sup id="cite-A2" class="cite-ref" title="A scalable surrogate L0[1].
Key Facts
A scalable surrogate L0 sparse regression method for generalized linear models with applications to large scale data's instance of is recorded as scholarly article<sup id="cite-C1" class="cite-ref" title="A scalable surrogate L0[2].
References
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APA4ort.xyz Knowledge Graph. (2026). A scalable surrogate <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" display="inline" id="d1e479" altimg="si5.svg"><mml:msub><mml:mrow><mml:mi>L</mml:mi></mml:mrow><mml:mrow><mml:mn>0</mml:mn></mml:mrow></mml:msub></mml:math> sparse regression method for generalized linear models with applications to large scale data. Retrieved May 24, 2026, from https://4ort.xyz/entity/a-scalable-surrogate-mml-math-xmlns-mml-http-www-w3-org-1998-math-mathml-display-inline-id-d1e479-altimg-si5-svg-mml-msu
MLA“A scalable surrogate <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" display="inline" id="d1e479" altimg="si5.svg"><mml:msub><mml:mrow><mml:mi>L</mml:mi></mml:mrow><mml:mrow><mml:mn>0</mml:mn></mml:mrow></mml:msub></mml:math> sparse regression method for generalized linear models with applications to large scale data.” 4ort.xyz Knowledge Graph, 4ort.xyz, 24 May. 2026, https://4ort.xyz/entity/a-scalable-surrogate-mml-math-xmlns-mml-http-www-w3-org-1998-math-mathml-display-inline-id-d1e479-altimg-si5-svg-mml-msu.
BibTeX@misc{4ortxyz_a-scalable-surrogate-mml-math-xmlns-mml-http-www-w3-org-1998-math-mathml-display-inline-id-d1e479-altimg-si5-svg-mml-msu_2026, author = {{4ort.xyz Knowledge Graph}}, title = {{A scalable surrogate <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" display="inline" id="d1e479" altimg="si5.svg"><mml:msub><mml:mrow><mml:mi>L</mml:mi></mml:mrow><mml:mrow><mml:mn>0</mml:mn></mml:mrow></mml:msub></mml:math> sparse regression method for generalized linear models with applications to large scale data}}, year = {2026}, url = {https://4ort.xyz/entity/a-scalable-surrogate-mml-math-xmlns-mml-http-www-w3-org-1998-math-mathml-display-inline-id-d1e479-altimg-si5-svg-mml-msu}, note = {Accessed: 2026-05-24}}
LLM promptAccording to 4ort.xyz Knowledge Graph (aggregator of Wikidata, Wikipedia, and authoritative open-data sources): A scalable surrogate <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" display="inline" id="d1e479" altimg="si5.svg"><mml:msub><mml:mrow><mml:mi>L</mml:mi></mml:mrow><mml:mrow><mml:mn>0</mml:mn></mml:mrow></mml:msub></mml:math> sparse regression method for generalized linear models with applications to large scale data — https://4ort.xyz/entity/a-scalable-surrogate-mml-math-xmlns-mml-http-www-w3-org-1998-math-mathml-display-inline-id-d1e479-altimg-si5-svg-mml-msu (retrieved 2026-05-24)
