Distributed estimation and inference for semiparametric binary response models
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Publication:6608674
DOI10.1214/24-aos2376MaRDI QIDQ6608674
Xi Chen, Unnamed Author, Wei-Dong Liu, Yi-Chen Zhang
Publication date: 20 September 2024
Published in: The Annals of Statistics (Search for Journal in Brave)
binary response modelsemiparametric inferencedivide and conquermaximum score estimatordistributed inference
Nonparametric regression and quantile regression (62G08) Asymptotic distribution theory in statistics (62E20) Asymptotic properties of nonparametric inference (62G20)
Cites Work
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- Exact computation of max weighted score estimators
- A partially linear framework for massive heterogeneous data
- Cube root asymptotics
- Semiparametric analysis of discrete response. Asymptotic properties of the maximum score estimator
- Asymptotic efficiency in semi-parametric models with censoring
- Maximum score estimation of the stochastic utility model of choice
- Superefficiency in nonparametric function estimation
- Distributed testing and estimation under sparse high dimensional models
- Divide and conquer in nonstandard problems and the super-efficiency phenomenon
- A distributed one-step estimator
- Nonparametric and semiparametric models.
- Circumventing superefficiency: an effective strategy for distributed computing in non-standard problems
- Distributed linear regression by averaging
- Distributed adaptive Huber regression
- Nonregular and minimax estimation of individualized thresholds in high dimension with binary responses
- Scalable estimation and inference for censored quantile regression process
- Distributed estimation of principal eigenspaces
- Quantile regression under memory constraint
- Distributed inference for quantile regression processes
- The Dantzig selector: statistical estimation when \(p\) is much larger than \(n\). (With discussions and rejoinder).
- Smoothed quantile regression with large-scale inference
- Family ties and corruption
- Divide and Conquer Kernel Ridge Regression: A Distributed Algorithm with Minimax Optimal Rates
- A Massive Data Framework for M-Estimators with Cubic-Rate
- A split-and-conquer approach for analysis of
- Covariate-adjusted generalized linear models
- Consequences and Detection of Misspecified Nonlinear Regression Models
- A Smoothed Maximum Score Estimator for the Binary Response Model
- An Adaptive, Rate-Optimal Test of a Parametric Mean-Regression Model Against a Nonparametric Alternative
- Statistical tests and identifiability conditions for pooling and analyzing multisite datasets
- WONDER: Weighted one-shot distributed ridge regression in high dimensions
- Heterogeneity-aware and communication-efficient distributed statistical inference
- Communication-Efficient Distributed Statistical Inference
- High-Dimensional Quantile Regression: Convolution Smoothing and Concave Regularization
- A review of distributed statistical inference
- Maximum Likelihood Estimation of Misspecified Models
- First-Order Newton-Type Estimator for Distributed Estimation and Inference
- Communication-Efficient Accurate Statistical Estimation
- Asymptotic normality of a change plane estimator in fixed dimension with near-optimal rate
- Statistical inference in massive data sets
- Smoothing Quantile Regressions
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