惯性聚合 高效追踪和阅读你感兴趣的博客、新闻、科技资讯
阅读原文 在惯性聚合中打开

推荐订阅源

The Register - Security
The Register - Security
云风的 BLOG
云风的 BLOG
U
Unit 42
F
Fortinet All Blogs
The GitHub Blog
The GitHub Blog
cs.CV updates on arXiv.org
cs.CV updates on arXiv.org
D
Docker
Cyber Security Advisories - MS-ISAC
Cyber Security Advisories - MS-ISAC
S
Secure Thoughts
Hacker News: Ask HN
Hacker News: Ask HN
Vercel News
Vercel News
S
Security @ Cisco Blogs
GbyAI
GbyAI
Stack Overflow Blog
Stack Overflow Blog
Exploit-DB.com RSS Feed
Exploit-DB.com RSS Feed
I
Intezer
MongoDB | Blog
MongoDB | Blog
AI
AI
MyScale Blog
MyScale Blog
Engineering at Meta
Engineering at Meta
Y
Y Combinator Blog
Threat Intelligence Blog | Flashpoint
Threat Intelligence Blog | Flashpoint
P
Proofpoint News Feed
钛媒体:引领未来商业与生活新知
钛媒体:引领未来商业与生活新知
W
WeLiveSecurity
博客园 - 叶小钗
S
SegmentFault 最新的问题
N
News | PayPal Newsroom
WordPress大学
WordPress大学
freeCodeCamp Programming Tutorials: Python, JavaScript, Git & More
D
DataBreaches.Net
小众软件
小众软件
Microsoft Azure Blog
Microsoft Azure Blog
Spread Privacy
Spread Privacy
H
Help Net Security
美团技术团队
博客园 - 司徒正美
T
Threat Research - Cisco Blogs
cs.AI updates on arXiv.org
cs.AI updates on arXiv.org
K
Kaspersky official blog
Application and Cybersecurity Blog
Application and Cybersecurity Blog
K
KPMG report finds enterprise disconnect between AI and its ROI | CIO
V
Vulnerabilities – Threatpost
TaoSecurity Blog
TaoSecurity Blog
N
Netflix TechBlog - Medium
L
Lohrmann on Cybersecurity
J
Java Code Geeks
量子位
Martin Fowler
Martin Fowler
博客园_首页

stat updates on arXiv.org

Can Adaptive Gradient Methods Converge under Heavy-Tailed Noise? A Case Study of AdaGrad Shallow ReLU$^s$ Networks in $L^p$-Type and Sobolev Spaces: Approximation and Path-Norm Controlled Generalization On Stability and Decomposition of Sample Quantiles under Heavy-Tailed Distributions Improved Baselines with Representation Autoencoders Symmetry-Compatible Principle for Optimizer Design: Embeddings, LM Heads, SwiGLU MLPs, and MoE Routers Feature Learning in Linear-Width Two-Layer Networks: Two vs. One Step of Gradient Descent A Two-Parameter Weibull Framework for Diagnosing Transformer Weight Distributions Calibeating for general proper losses: A Bregman divergence approach Dimension-Free Convergence of Discrete Diffusion Models: Adjoint Equations Induce the Right Space Sample-efficient inductive matrix completion with noise and inexact side-information Multi-task Linear Regression without Eigenvalue Lower Bounds: Adaptivity, Robustness, and Safety XAI and Statistical Analysis for Reliable Intrusion Detection in the UAVIDS-2025 Dataset: From Tree to Hybrid and Tabular DNN Ensembles Reasoning Models Don't Just Think Longer, They Move Differently TabPFN-3: Technical Report Reframing preprocessing selection as model-internal calibration in near-infrared spectroscopy: A large-scale benchmark of operator-adaptive PLS and Ridge models Towards a holistic understanding of Selection Bias for Causal Effect Identification Adaptive Kernel Density Estimation with Pre-training Coreset-Induced Conditional Velocity Flow Matching RISED: A Pre-Deployment Evaluation Framework for High-Stakes AI Decision-Support Systems, with Application to Healthcare ISOMORPH: A Supply Chain Digital Twin for Simulation, Dataset Generation, and Forecasting Benchmarks Yield Curves Dynamics Using Variational Autoencoders Under No-arbitrage Model-based Bootstrap of Controlled Markov Chains Online Learning-to-Defer with Varying Experts Self-Supervised Laplace Approximation for Bayesian Uncertainty Quantification Keeping Score: Efficiency Improvements in Neural Likelihood Surrogate Training via Score-Augmented Loss Functions One-Step Generative Modeling via Wasserstein Gradient Flows Exact Stiefel Optimization for Probabilistic PLS: Closed-Form Updates, Error Bounds, and Calibrated Uncertainty A Composite Activation Function for Learning Stable Binary Representations Adaptive Calibration in Non-Stationary Environments Real vs. Semi-Simulated: Rethinking Evaluation for Treatment Effect Estimation Federated Language Models Under Bandwidth Budgets: Distillation Rates and Conformal Coverage On Variance Reduction in Learning Mean Flows When Attention Beats Fourier: Multi-Scale Transformers for PDE Solving on Irregular Domains A Refined Generalization Analysis for Extreme Multi-class Supervised Contrastive Representation Learning Ensemble Distributionally Robust Bayesian Optimisation The Proxy Presumption: From Semantic Embeddings to Valid Social Measures Modulated learning for private and distributed regression with just a single sample per client device Query-efficient model evaluation using cached responses Order-Agnostic Autoregressive Modelling with Missing Data Grokking or Glitching? How Low-Precision Drives Slingshot Loss Spikes Tuning Derivatives for Causal Fairness in Machine Learning Spherical Flows for Sampling Categorical Data Bayesian Rain Field Reconstruction using Commercial Microwave Links and Diffusion Model Priors Unified Framework of Distributional Regret in Multi-Armed Bandits and Reinforcement Learning Jacobian-Velocity Bounds for Deployment Risk Under Covariate Drift Self-Attention as Transport: Limits of Symmetric Spectral Diagnostics Graph Convolutional Support Vector Regression for Robust Spatiotemporal Forecasting of Urban Air Pollution Stochastic Schrödinger Diffusion Models for Pure-State Ensemble Generation Understanding Self-Supervised Learning via Latent Distribution Matching Imbalanced Classification under Capacity Constraints On the Spectral Structure and Objective Equivalence of Orthogonal Multilabel Fisher Discriminants Partially Observed Structural Causal Models Robust and Fast Training via Per-Sample Clipping Efficient Preference Poisoning Attack on Offline RLHF Joint Energy Management and Coordinated AIGC Workload Scheduling for Distributed Data Centers: A Diffusion-Aided Reward Shaping Approach Distributional Causal Mediation via Conditional Generative Modeling A Theory of Saddle Escape in Deep Nonlinear Networks Adaptive Querying with AI Persona Priors Optimal Spatio-Temporal Decoupling for Bayesian Conformal Prediction Wasserstein Distributionally Robust Regret Optimization for Reinforcement Learning from Human Feedback Electricity price forecasting across Norway's five bidding zones in the post-crisis era Adversarial Robustness of NTK Neural Networks Identifiability and Stability of Generative Drifting with Companion-Elliptic Kernel Families A Limit Theory of Foundation Models: A Mathematical Approach to Understanding Emergent Intelligence and Scaling Laws Conditional Score-Based Modeling of Effective Langevin Dynamics Inference of Online Newton Methods with Nesterov's Accelerated Sketching ProEval: Proactive Failure Discovery and Efficient Performance Estimation for Generative AI Evaluation Score-Repellent Monte Carlo: Toward Efficient Non-Markovian Sampler with Constant Memory in General State Spaces Learning to Emulate Chaos: Adversarial Optimal Transport Regularization Geometric Layer-wise Approximation Rates for Deep Networks S2MAM: Semi-supervised Meta Additive Model for Robust Estimation and Variable Selection Beyond Coefficients: Forecast-Necessity Testing for Interpretable Causal Discovery in Nonlinear Time-Series Models Curiosity-Critic: Cumulative Prediction Error Improvement as a Tractable Intrinsic Reward for World Model Training Knowing When to Quit: A Principled Framework for Dynamic Abstention in LLM Reasoning Enhancing AI and Dynamical Subseasonal Forecasts with Probabilistic Bias Correction Zeroth-Order Optimization at the Edge of Stability Generative Augmented Inference Estimating Continuous Treatment Effects with Two-Stage Kernel Ridge Regression Rare Event Analysis via Stochastic Optimal Control Adaptive Learning via Off-Model Training and Importance Sampling for Fully Non-Markovian Optimal Stochastic Control. Complete version Beyond Fixed False Discovery Rates: Post-Hoc Conformal Selection with E-Variables Beyond Augmented-Action Surrogates for Multi-Expert Learning-to-Defer Spatio-temporal probabilistic forecast using MMAF-guided learning Conformal Policy Control The Implicit Curriculum: Learning Dynamics in RL with Verifiable Rewards Probabilistic NDVI Forecasting from Sparse Satellite Time Series and Weather Covariates Constrained Policy Optimization with Cantelli-Bounded Value-at-Risk Factorizable joint shift revisited Feature Learning Dynamics in Infinite-Depth Neural Networks Statistically-Guided Meta-Learning for Cross-Deployment Activity Recognition in Distributed Fiber-Optic Sensing DAPS++: Rethinking Diffusion Inverse Problems with Decoupled Posterior Annealing Branching Flows: Discrete, Continuous, and Manifold Flow Matching with Splits and Deletions Neural ARFIMA model for forecasting BRIC exchange rates with long memory Neural Stochastic Differential Equations on Compact State Spaces: Theory, Methods, and Application to Suicide Risk Modeling BOOST: A Data-Driven Framework for the Automated Joint Selection of Kernel and Acquisition Functions in Bayesian Optimization Random Walk Learning and the Pac-Man Attack Random Matrix Theory for Deep Learning: Beyond Eigenvalues of Linear Models Post-Training Augmentation Invariance Optimizing LLM Inference: Fluid-Guided Online Scheduling with Memory Constraints Dataset-Driven Channel Masks in Transformers for Multivariate Time Series
Bridging Item Response Theory and Factor Analysis: A Four-Parameter Mixture-Dichotomized Model with Bayesian Estimation
[Submitted on 4 Jul 2024 (v1), last revised 25 Jun 2026 (this ve · 2026-06-26 · via stat updates on arXiv.org

View PDF HTML (experimental)

Abstract:Item Response Theory (IRT) and Factor Analysis (FA) are two major frameworks for modeling multi-item measurements of latent traits. While the relationship between two-parameter IRT models and dichotomized FA models is well established, FA formulations for IRT models with additional parameters are less common. We focus on the four-parameter factor-analytic (4P FA) model that extends the traditional dichotomized single-factor FA model through a hierarchical mixture formulation accounting for guessing and inattention effects. We analytically establish the equivalence of the 4P FA and 4P IRT models, extending the FA--IRT correspondence beyond the two-parameter case. A Bayesian estimation procedure is developed for model estimation, to estimate the four item parameters, the respondents' latent scores, and the scores adjusted for guessing and inattention effects. The proposed algorithm is implemented in \texttt{R} and \texttt{Python}. A simulation study compares estimation under the FA and IRT formulations of the 4P model and evaluates the practical implications of the FA parametrization. Empirical examples based on an admission test and an anxiety inventory demonstrate the correspondence between the 4P FA and 4P IRT models and illustrate the application of the proposed methodology.

Submission history

From: Ján Pavlech [view email]
[v1] Thu, 4 Jul 2024 17:20:49 UTC (93 KB)
[v2] Wed, 2 Jul 2025 08:54:04 UTC (95 KB)
[v3] Thu, 25 Jun 2026 15:18:35 UTC (192 KB)