:: Library Catalog

Imagen de Portada

Guardado en:

Detalles Bibliográficos
Autores principales:	Ofner, Maximilian, Hörmann, Siegfried, Kraus, David, Liebl, Dominik
Formato:	Preprint
Publicado:	2025
Materias:	Methodology
Acceso en línea:	https://arxiv.org/abs/2505.08721
Etiquetas:	Agregar Etiqueta Sin Etiquetas, Sea el primero en etiquetar este registro!

Ejemplares similares

Covariate-informed reconstruction of partially observed functional data via factor models
por: Ofner, Maximilian, et al.
Publicado: (2023)

Tests of Missing Completely At Random based on sample covariance matrices
por: Bordino, Alberto, et al.
Publicado: (2024)

Discussion of "Matrix Completion When Missing Is Not at Random and Its Applications in Causal Panel Data Models"
por: Ben-Michael, Eli, et al.
Publicado: (2026)

Random Indicator Imputation for Missing Not At Random Data
por: Jolani, Shahab, et al.
Publicado: (2024)

Estimation beyond Missing (Completely) at Random
por: Ma, Tianyi, et al.
Publicado: (2024)

Confidence Intervals for Random Forest Permutation Importance with Missing Data
por: Föge, Nico, et al.
Publicado: (2025)

A Multiplicative Instrumental Variable Model for Data Missing Not-at-Random
por: Zhang, Yunshu, et al.
Publicado: (2025)

MMD Two-sample Testing in the Presence of Arbitrarily Missing Data
por: Zeng, Yijin, et al.
Publicado: (2024)

The Exchangeability Assumption for Permutation Tests of Multiple Regression Models: Implications for Statistics and Data Science Educators
por: Hardin, Johanna, et al.
Publicado: (2024)

Assumption Smuggling in Intermediate Outcome Tests of Causal Mechanisms
por: Blackwell, Matthew, et al.
Publicado: (2024)

Graphical Modelling without Independence Assumptions for Uncentered Data
por: Andrew, Bailey, et al.
Publicado: (2024)

Enhancing Data Completeness in Time Series: Imputation Strategies for Missing Data Using Significant Periodically Correlated Components
por: Ahmad, Asmaa, et al.
Publicado: (2025)

$t$-Testing the Waters: Empirically Validating Assumptions for Reliable A/B-Testing
por: Jeunen, Olivier
Publicado: (2025)

Exact Bounds of Spearman's footrule in the Presence of Missing Data with Applications to Independence Testing
por: Zeng, Yijin, et al.
Publicado: (2025)

Modeling Missing at Random Neuropsychological Test Scores Using a Mixture of Binomial Product Experts
por: Suen, Daniel, et al.
Publicado: (2023)

Generalizing Trimming Bounds for Endogenously Missing Outcome Data Using Random Forests
por: Samii, Cyrus, et al.
Publicado: (2023)

Design-Based Causal Inference with Missing Outcomes: Missingness Mechanisms, Imputation-Assisted Randomization Tests, and Covariate Adjustment
por: Heng, Siyu, et al.
Publicado: (2023)

Recursive Equations For Imputation Of Missing Not At Random Data With Sparse Pattern Support
por: Phung, Trung, et al.
Publicado: (2025)

Stress-Testing Assumptions: A Guide to Bayesian Sensitivity Analyses in Causal Inference
por: Oganisian, Arman
Publicado: (2026)

Assumption violations in causal discovery and the robustness of score matching
por: Montagna, Francesco, et al.
Publicado: (2023)

Inside Out: Externalizing Assumptions in Data Analysis as Validation Checks
por: Zhang, H. Sherry, et al.
Publicado: (2025)

Assumption-lean Inference for Network-linked Data
por: Li, Wei, et al.
Publicado: (2025)

Assumption Lean Regression
por: Berk, Richard A., et al.
Publicado: (2018)

Variational Bayes Inference for Spatial Error Models with Missing Data
por: Wijayawardhana, Anjana, et al.
Publicado: (2024)

Dependable Exploitation of High-Dimensional Unlabeled Data in an Assumption-Lean Framework
por: Ying, Chao, et al.
Publicado: (2026)

A General Approach for Calibration Weighting under Missing at Random
por: Kwon, Yonghyun, et al.
Publicado: (2025)

Causal Inference with Outcomes Truncated by Death and Missing Not at Random
por: Li, Wei, et al.
Publicado: (2024)

Testing Random Effects for Binomial Data
por: Kania, Lucas, et al.
Publicado: (2025)

Bayesian Inference for Non-Gaussian Simultaneous Autoregressive Models with Missing Data
por: Wijayawardhana, Anjana, et al.
Publicado: (2025)

Difference-in-differences Design with Outcomes Missing Not at Random
por: Shin, Sooahn
Publicado: (2024)

Assumption-Lean and Data-Adaptive Post-Prediction Inference
por: Miao, Jiacheng, et al.
Publicado: (2023)

Assumption-Lean Quantile Regression
por: Baklicharov, Georgi, et al.
Publicado: (2024)

Time-In-Range Analyses of Functional Data Subject to Missing with Applications to Inpatient Continuous Glucose Monitoring
por: Yu, Qi, et al.
Publicado: (2024)

Imputing Missing Values with External Data
por: Thiesmeier, Robert, et al.
Publicado: (2024)

Estimation of Semiparametric Factor Models with Missing Data
por: Zheng, Sijie
Publicado: (2025)

Balancing Weights for Non-monotone Missing Data
por: Dong, Jianing, et al.
Publicado: (2024)

Markov-Restricted Analysis of Randomized Trials with Non-Monotone Missing Binary Outcomes
por: Lee, Jaron J. R., et al.
Publicado: (2021)

Score Matching With Missing Data
por: Givens, Josh, et al.
Publicado: (2025)

Making Event Study Plots Honest: A Functional Data Approach to Causal Inference
por: Fang, Chencheng, et al.
Publicado: (2025)

Manipulating a Continuous Instrumental Variable in an Observational Study of Premature Babies: Algorithm, Partial Identification Bounds, and Inference under Randomization and Biased Randomization Assumptions
por: Chen, Zhe, et al.
Publicado: (2024)