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ARXIV:2604.00848 · STATISTICAL INFERENCE · SUBMITTED 02 APR · 20:55 UTC · FRESHNESS STALE
ARXIV:2604.00848STATISTICAL INFERENCESUBMITTED 02 APR · 20:55 UTCFRESHNESS STALEBenjamin Smith · arXiv
Examines and replicates a debiased LASSO framework for high-dimensional regression, comparing its performance and power against the desparsified LASSO.
Opportunity summary
Pain Examines and replicates a debiased LASSO framework for high-dimensional regression, comparing its performance and power against the desparsified LASSO.
Evidence 22 refs | 3 sources | 33% coverage
Blocker Evidence unverified
Examines and replicates a debiased LASSO framework for high-dimensional regression, comparing its performance and power against the desparsified LASSO. To address this, Javanmard and Montanari (2014) propose a debiased estimator that enables valid hypothesis…
High-dimensional statistical settings ($p \gg n$) pose fundamental challenges for classical inference, largely due to bias introduced by regularized estimators such as the LASSO. To address this, Javanmard and Montanari (2014) propose a debiased…
ScienceToStartup currently rates this 3.0/10 on the public viability pass. To address this, Javanmard and Montanari (2014) propose a debiased estimator that enables valid hypothesis testing and confidence interval construction. Code availability is flagged…
Statistical Inference moved forward this cycle; last verified April 2026. Public score 3.0/10. Production flags indicate code availability.
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Examines and replicates a debiased LASSO framework for high-dimensional regression, comparing its performance and power against the desparsified LASSO.
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10.48550/arXiv.2604.00848Examines and replicates a debiased LASSO framework for high-dimensional regression, comparing its performance and power against the desparsified LASSO.
Abstract
High-dimensional statistical settings ($p \gg n$) pose fundamental challenges for classical inference, largely due to bias introduced by regularized estimators such as the LASSO. To address this, Javanmard and Montanari (2014) propose a debiased estimator that enables valid hypothesis testing and confidence interval construction. This report examines their debiased LASSO framework, which yields asymptotically normal estimators in high-dimensional settings. We present the key theoretical results underlying this approach, specifically, the construction of an optimized debiased estimator that restores asymptotic normality, which enables the computation of valid confidence intervals and $p$-values. To evaluate the claims of Javanmard and Montanari, a subset of the original simulation study and a re-examination of their real-data analysis are presented. Building on this baseline, we extend the empirical analysis to include the desparsified LASSO, a closely related method referenced but not implemented in the original study. The results demonstrate that while the debiased LASSO achieves reliable coverage and controls Type I error, the LASSO projection estimator can offer improved power in low-signal settings without compromising error rates. Our findings highlight a critical practical trade-off: while the LASSO projection estimator demonstrates superior statistical power in an idealized simulated low-signal setting, the estimation procedure employed by Javanmard and Montanari adapts more robustly to complex correlation networks, yielding superior precision and signal detection in real-world genomic data.
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Proof status
unverified22 refs; 3 sources; 33% coverage.
What was readable
Derived fallback: Estimated from adjacent evidence; not verified from source.
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Dimensions overall score 3.0
PROBLEM
Examines and replicates a debiased LASSO framework for high-dimensional regression, comparing its performance and power against the desparsified LASSO. To address this, Javanmard and Montanari (2014) propose a debiased estimator that enables valid hypothesis testing and confiden...
METHOD
High-dimensional statistical settings ($p \gg n$) pose fundamental challenges for classical inference, largely due to bias introduced by regularized estimators such as the LASSO. To address this, Javanmard and Montanari (2014) propose a debiased estimator that enables valid hypo...
RESULT
ScienceToStartup currently rates this 3.0/10 on the public viability pass. To address this, Javanmard and Montanari (2014) propose a debiased estimator that enables valid hypothesis testing and confidence interval construction. Code availability is flagged in the production reco...
WHY NOW
Statistical Inference moved forward this cycle; last verified April 2026. Public score 3.0/10. Production flags indicate code availability.
Abstract-backed public claims while anchored extraction refreshes.
Examines and replicates a debiased LASSO framework for high-dimensional regression, comparing its performance and power against the desparsified LASSO. To address this, Javanmard and Montanari (2014) propose a debiased estimator that enables valid hypothesis testing and confidence interval construction.
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
partial
High-dimensional statistical settings ($p \gg n$) pose fundamental challenges for classical inference, largely due to bias introduced by regularized estimators such as the LASSO. To address this, Javanmard and Montanari (2014) propose a debiased estimator that enables valid hypothesis testing and confidence interval construction.
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
partial
ScienceToStartup currently rates this 3.0/10 on the public viability pass. To address this, Javanmard and Montanari (2014) propose a debiased estimator that enables valid hypothesis testing and confidence interval construction. Code availability is flagged in the production record; the public repository link still needs proof alignment.
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
partial
Statistical Inference moved forward this cycle; last verified April 2026. Public score 3.0/10. Production flags indicate code availability.
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
partial
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Concepts
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Examines and replicates a debiased LASSO framework for high-dimensional regression, comparing its performance and power against the desparsified LASSO.
Segment
Statistical Inference
Adoption evidence
No public code link in the paper record yet
Commercial read
3.0/10 public viability
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2/3 checks · 67%
Build Passport
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status
missing
reason
passport_row_missing
proof status
unverified
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confidence low
next verification path
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Source missing: Build Passport payload.
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Evidence coverage
OpportunityKernel evidence_receipt
22 refs / 3 sources / 33% coverage
stale
Verify missing sources before using this as buyer proof. verified:false
Build readiness
BuildPassport EvidenceState
passport absent
stale
Run Proof Lab or inspect typed missing state. verified:false
Artifact maturity
GitHub and Hugging Face maturity payloads
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stale
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Technical feasibility
partial
Current read
Runnable path is not fully verified.
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Gaps
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Run minimal reproduction from the Build Passport prototype path.
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Evidence
22 references, 3 sources, 33% evidence coverage.
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Buyer clarity
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Defensibility signals are missing.
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Refresh defensibility bars with source receipts.
Integration burden
missing
Current read
No public implementation surface observed.
Evidence
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Write integration checklist from prototype path and target workflow.
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Classify regulatory flags before commercialization planning.
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Paper authors are not treated as operators without consent.
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People
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Regulatory need unclassified.
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ARTIFACTS
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DEFENSIBILITY
Defensibility and confidence evidence pending.
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