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理论统计学是统计学的一个分支,它是把研究对象一般化、抽象化,以概率论为基础,从纯理论的角度,对统计方法加以推导论证,中心的内容是统计推断问。
Local Learning for Covariate Selection in Nonparametric Causal Effect Estimation with Latent Variables
Arxiv
0+阅读 · 4月3日
Unifying Different Theories of Conformal Prediction
Arxiv
0+阅读 · 4月3日
Estimation of the complier causal hazard ratio under dependent censoring
Arxiv
0+阅读 · 4月2日
Universal Log-Optimality for General Classes of e-processes and Sequential Hypothesis Tests
Arxiv
0+阅读 · 4月3日
On the Geometry of Receiver Operating Characteristic and Precision-Recall Curves
Arxiv
0+阅读 · 4月2日
Testing independence and conditional independence in high dimensions via coordinatewise Gaussianization
Testing independence and conditional independence in high dimensions via coordinatewise Gaussianization
Arxiv
0+阅读 · 4月3日
Probabilistic morphisms and Bayesian supervised learning
Arxiv
0+阅读 · 4月3日
Computing High-dimensional Confidence Sets for Arbitrary Distributions
Computing High-dimensional Confidence Sets for Arbitrary Distributions
Arxiv
0+阅读 · 4月3日
The Markov approximation of the periodic multivariate Poisson autoregression
The Markov approximation of the periodic multivariate Poisson autoregression
Arxiv
0+阅读 · 4月3日
Multiple Testing under High-dimensional Dynamic Factor Model
Arxiv
0+阅读 · 4月2日
Contextual Dynamic Pricing: Algorithms, Optimality, and Local Differential Privacy Constraints
Contextual Dynamic Pricing: Algorithms, Optimality, and Local Differential Privacy Constraints
Arxiv
0+阅读 · 4月3日
Heavy-Tailed NGG Mixture Models
Heavy-Tailed NGG Mixture Models
Arxiv
0+阅读 · 4月3日
Fermat Distance-to-Measure: a robust Fermat-like metric
Fermat Distance-to-Measure: a robust Fermat-like metric
Arxiv
0+阅读 · 4月3日
Bayesian Inference for High-dimensional Time Series with a Directed Acyclic Graphical Structure
Bayesian Inference for High-dimensional Time Series with a Directed Acyclic Graphical Structure
Arxiv
0+阅读 · 4月3日
Non-parametric cure models through extreme-value tail estimation
Arxiv
0+阅读 · 3月31日
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