Copertina

When can weak latent factors be statistically inferred?

Fuente: arXiv

Salvato in:

Dettagli Bibliografici
Autori principali:	Fan, Jianqing, Yan, Yuling, Zheng, Yuheng
Natura:	Preprint
Pubblicazione:	2024
Soggetti:	Methodology Econometrics Statistics Theory Statistical Finance Machine Learning
Accesso online:	Acceder al recurso
Tags:	Aggiungi Tag Nessun Tag, puoi essere il primo ad aggiungerne!!

Documenti analoghi

Entrywise Inference for Missing Panel Data: A Simple and Instance-Optimal Approach
di: Yan, Yuling, et al.
Pubblicazione: (2024)

Sparse Asymptotic PCA: Identifying Sparse Latent Factors Across Time Horizon in High-Dimensional Time Series
di: Gao, Zhaoxing
Pubblicazione: (2024)

Dynamic treatment effects: high-dimensional inference under model misspecification
di: Zhang, Yuqian, et al.
Pubblicazione: (2021)

How weak are weak factors? Uniform inference for signal strength in signal plus noise models
di: Bykhovskaya, Anna, et al.
Pubblicazione: (2025)

Distance and Kernel-Based Measures for Global and Local Two-Sample Conditional Distribution Testing
di: Yan, Jian, et al.
Pubblicazione: (2022)

Fitting an Equation to Data Impartially
di: Tofallis, Chris
Pubblicazione: (2024)

Sequential Audit Sampling with Statistical Guarantees
di: Kato, Masahiro, et al.
Pubblicazione: (2026)

A Dynamic Spatiotemporal and Network ARCH Model with Common Factors
di: Doğan, Osman, et al.
Pubblicazione: (2024)

Kernel Three Pass Regression Filter
di: Jat, Rajveer, et al.
Pubblicazione: (2024)

Multi-regime Markov-switching models with time-varying transition probabilities: An application to U.S. Treasury yields
di: Modée, Samuel, et al.
Pubblicazione: (2026)

A Note on the Asymptotic Properties of the GLS Estimator in Multivariate Regression with Heteroskedastic and Autocorrelated Errors
di: Moriya, Koichiro, et al.
Pubblicazione: (2025)

Holistic Multi-Scale Inference of the Leverage Effect: Efficiency under Dependent Microstructure Noise
di: Xiong, Ziyang, et al.
Pubblicazione: (2025)

Bayesian Analysis of High Dimensional Vector Error Correction Model
di: Yang, Parley R, et al.
Pubblicazione: (2023)

Supervised Dynamic PCA: Linear Dynamic Forecasting with Many Predictors
di: Gao, Zhaoxing, et al.
Pubblicazione: (2023)

Comparing latent inequality with ordinal data
di: Kaplan, David M., et al.
Pubblicazione: (2025)

Debiased Nonparametric Regression for Statistical Inference and Distributionally Robustness
di: Kato, Masahiro
Pubblicazione: (2024)

Orthogonal Bootstrap: Efficient Simulation of Input Uncertainty
di: Liu, Kaizhao, et al.
Pubblicazione: (2024)

Demistifying Inference after Adaptive Experiments
di: Bibaut, Aurélien, et al.
Pubblicazione: (2024)

A Note on Doubly Robust Estimator in Regression Discontinuity Designs
di: Kato, Masahiro
Pubblicazione: (2024)

Smaller Confidence Intervals From IPW Estimators via Data-Dependent Coarsening
di: Kalavasis, Alkis, et al.
Pubblicazione: (2024)

Policy Learning for Optimal Dynamic Treatment Regimes with Observational Data
di: Sakaguchi, Shosei
Pubblicazione: (2024)

Method-of-Moments Inference for GLMs and Doubly Robust Functionals under Proportional Asymptotics
di: Chen, Xingyu, et al.
Pubblicazione: (2024)

Anytime-Valid Inference for Double/Debiased Machine Learning of Causal Parameters
di: Dalal, Abhinandan, et al.
Pubblicazione: (2024)

Structure-agnostic Optimality of Doubly Robust Learning for Treatment Effect Estimation
di: Jin, Jikai, et al.
Pubblicazione: (2024)

Debiased Regression for Root-N-Consistent Conditional Mean Estimation
di: Kato, Masahiro
Pubblicazione: (2024)

Automatic Doubly Robust Forests
di: Chen, Zhaomeng, et al.
Pubblicazione: (2024)

Combining Experimental and Observational Data for Identification and Estimation of Long-Term Causal Effects
di: Ghassami, AmirEmad, et al.
Pubblicazione: (2022)

Riesz Regression As Direct Density Ratio Estimation
di: Kato, Masahiro
Pubblicazione: (2025)

Semi-Supervised Treatment Effect Estimation with Unlabeled Covariates for Prediction-Powered Causal Inference
di: Kato, Masahiro
Pubblicazione: (2025)

The Role of Contextual Information in Best Arm Identification
di: Kato, Masahiro, et al.
Pubblicazione: (2021)

Double Machine Learning of Continuous Treatment Effects with General Instrumental Variables
di: Chen, Shuyuan, et al.
Pubblicazione: (2026)

Nearest Neighbor Matching as Least Squares Density Ratio Estimation and Riesz Regression
di: Kato, Masahiro
Pubblicazione: (2025)

Covariate Balancing and Riesz Regression Should Be Guided by the Neyman Orthogonal Score in Debiased Machine Learning
di: Kato, Masahiro
Pubblicazione: (2026)

General Bayesian Policy Learning
di: Kato, Masahiro
Pubblicazione: (2026)

Direct Bias-Correction Term Estimation for Average Treatment Effect Estimation
di: Kato, Masahiro
Pubblicazione: (2025)

Minimax and Bayes Optimal Best-Arm Identification
di: Kato, Masahiro
Pubblicazione: (2025)

Identification and Debiased Learning of Causal Effects with General Instrumental Variables
di: Chen, Shuyuan, et al.
Pubblicazione: (2025)

Dynamic covariate balancing: estimating treatment effects over time with potential local projections
di: Viviano, Davide, et al.
Pubblicazione: (2021)

Near-Optimal Non-Parametric Sequential Tests and Confidence Sequences with Possibly Dependent Observations
di: Bibaut, Aurelien, et al.
Pubblicazione: (2022)

Locally Optimal Fixed-Budget Best Arm Identification in Two-Armed Gaussian Bandits with Unknown Variances
di: Kato, Masahiro
Pubblicazione: (2023)