On-device research index

arXiv research

A locally-built, LLM-digested index of recent arXiv papers in quant finance, geometry/topology, and statistical ML — keyword search served straight from SQLite on this machine.

168,657 papers · 148 categories

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3 results for “double-smoothing”

Distributed High-Dimensional Quantile Regression: Estimation Efficiency and Support Recoverystat.ML

Paper tackles distributed quantile regression with improved efficiency and support recovery.

problem Challenges in distributed estimation and support recovery for high-dimensional linear quantile regression.

method Transformed quantile regression into least-squares optimization, applied double-smoothing approach, developed efficient algorithm.

result Achieved near-oracle convergence rate and high support recovery accuracy.

distributed quantile regression high-dimensional linear regression support recovery double-smoothing efficient algorithm

2024-05-13abs ↗pdf ↗

Communication-Constrained Distributed Quantile Regression with Optimal Statistical Guaranteesstat.ME

Optimal inference in distributed quantile regression without stringent scaling conditions.

problem Challenges in achieving optimal inference in distributed quantile regression due to the non-smooth nature of the QR loss function.

method Double-smoothing approach applied to local and global objective functions, with a trade-off between communication cost and statistical error.

result Established a finite-sample theoretical framework for distributed QR estimators, showing a trade-off between communication cost and statistical error.

distributed quantile regression optimal inference double-smoothing communication cost statistical error

2021-10-25abs ↗pdf ↗

Operator Analysis of MACDq-fin.MF

The paper analyzes MACD using operator theory.

problem Understanding the mathematical foundation of MACD.

method Developed a functional-analytic framework interpreting MACD as a phase-corrected, smoothed derivative operator.

result MACD is structurally equivalent to a band-pass filter and can be expressed as a finite difference of delayed and doubly averaged signals.

MACD Moving Average Convergence Divergence Functional Analysis Operator Theory Signal Detection

2025-09-05abs ↗pdf ↗