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Research Paper#Econometrics, Network Analysis, Panel Data🔬 ResearchAnalyzed: Jan 3, 2026 08:43

QMLE for Unbalanced Dynamic Network Panel Data

Published:Dec 31, 2025 09:47
1 min read
ArXiv

Analysis

This paper addresses the challenge of estimating dynamic network panel data models when the panel is unbalanced (i.e., not all units are observed for the same time periods). This is a common issue in real-world datasets. The paper proposes a quasi-maximum likelihood estimator (QMLE) and a bias-corrected version to address this, providing theoretical guarantees (consistency, asymptotic distribution) and demonstrating its performance through simulations and an empirical application to Airbnb listings. The focus on unbalanced data and the bias correction are significant contributions.
Reference

“The paper establishes the consistency of the QMLE and derives its asymptotic distribution, and proposes a bias-corrected estimator.”

PermalinkArXiv
Research Paper#Ranking, Statistics, Quasi-Likelihood, U-statistics🔬 ResearchAnalyzed: Jan 3, 2026 16:52

Novel Quasi-Likelihood Framework for Ranking Data

Published:Dec 30, 2025 06:12
1 min read
ArXiv

Analysis

This paper introduces a new quasi-likelihood framework for analyzing ranked or weakly ordered datasets, particularly those with ties. The key contribution is a new coefficient (τ_κ) derived from a U-statistic structure, enabling consistent statistical inference (Wald and likelihood ratio tests). This addresses limitations of existing methods by handling ties without information loss and providing a unified framework applicable to various data types. The paper's strength lies in its theoretical rigor, building upon established concepts like the uncentered correlation inner-product and Edgeworth expansion, and its practical implications for analyzing ranking data.
Reference

“The paper introduces a quasi-maximum likelihood estimation (QMLE) framework, yielding consistent Wald and likelihood ratio test statistics.”

PermalinkArXiv
Research#llm📝 BlogAnalyzed: Dec 28, 2025 12:02

Building a Machine Learning Infrastructure with BigQuery ML (BQML)

Published:Dec 28, 2025 11:23
1 min read
Qiita AI

Analysis

This article discusses the challenges of setting up a machine learning infrastructure, particularly the difficulty of moving data from a data warehouse (DWH) to a learning environment. It highlights BigQuery ML (BQML) as a solution, suggesting that it allows users to perform machine learning tasks using familiar SQL, eliminating the need for complex data pipelines and Python environment setup. The article likely goes on to explain the benefits and practical applications of BQML for simplifying the machine learning workflow. The core argument is that BQML lowers the barrier to entry for machine learning by leveraging existing SQL skills and infrastructure.
Reference

“DWHから学習環境へのデータ移動（パイプライン構築）”

PermalinkQiita AI
Machine Learning#BigQuery📝 BlogAnalyzed: Dec 28, 2025 11:02

CVR Prediction Model Implementation with BQ ML

Published:Dec 28, 2025 10:16
1 min read
Qiita AI

Analysis

This article presents a hypothetical case study on implementing a CVR (Conversion Rate) prediction model using BigQuery ML (BQML) and DNN models. It's important to note that the article explicitly states that all companies, products, and numerical data are fictional and do not represent any real-world entities or services. The purpose is to share technical knowledge about BQML and DNN models in a practical context. The value lies in understanding the methodology and potential applications of these technologies, rather than relying on the specific data presented.

Key Takeaways

Reference

“本記事は、BigQuery ML (BQML) および DNNモデルの技術的知見の共有を目的として構成された架空のケーススタディです。”

PermalinkQiita AI
Research#llm📝 BlogAnalyzed: Dec 25, 2025 18:10

[BQML] Completing Missing Values with Gemini Grounding (Google Search)

Published:Dec 25, 2025 09:20
1 min read
Zenn Gemini

Analysis

This article discusses using BigQuery ML (BQML) with Gemini and Grounding with Google Search to address the common problem of missing data in data analysis. Traditionally, filling in missing data required external scripts and APIs or manual web searches. The article highlights how this new approach allows users to complete this process using only SQL, streamlining the data completion workflow. This integration simplifies data preparation and makes it more accessible to users familiar with SQL. The article promises to detail how this integration works and its benefits for data analysis and utilization, particularly in scenarios where data is incomplete or requires external validation.
Reference

“データ分析や活用において、頻繁に課題となるのが 「データの欠損」 です。”

PermalinkZenn Gemini
Research#QML🔬 ResearchAnalyzed: Jan 10, 2026 08:50

DeepQuantum: A New Software Platform for Quantum Machine Learning

Published:Dec 22, 2025 03:22
1 min read
ArXiv

Analysis

This article introduces DeepQuantum, a PyTorch-based software platform designed for quantum machine learning and photonic quantum computing. The platform's use of PyTorch could facilitate wider adoption by researchers already familiar with this popular deep learning framework.
Reference

“DeepQuantum is a PyTorch-based software platform.”

PermalinkArXiv
Research#QML🔬 ResearchAnalyzed: Jan 10, 2026 09:27

Domain-Aware Quantum Circuits Advance Quantum Machine Learning

Published:Dec 19, 2025 17:02
1 min read
ArXiv

Analysis

This research explores a novel approach to improve Quantum Machine Learning (QML) performance by incorporating domain-specific knowledge into quantum circuit design. The use of domain-aware quantum circuits may result in significant advancements in various applications.
Reference

“The article's context provides information on Domain-Aware Quantum Circuit for QML.”

PermalinkArXiv