Zobrazeno 1 - 10
of 143
pro vyhledávání: '"Xia Lucy"'
Balancing influential covariates is crucial for valid treatment comparisons in clinical studies. While covariate-adaptive randomization is commonly used to achieve balance, its performance can be inadequate when the number of baseline covariates is l
Externí odkaz:
http://arxiv.org/abs/2406.08968
Unsupervised learning has been widely used in many real-world applications. One of the simplest and most important unsupervised learning models is the Gaussian mixture model (GMM). In this work, we study the multi-task learning problem on GMMs, which
Externí odkaz:
http://arxiv.org/abs/2209.15224
The Neyman-Pearson (NP) binary classification paradigm constrains the more severe type of error (e.g., the type I error) under a preferred level while minimizing the other (e.g., the type II error). This paradigm is suitable for applications such as
Externí odkaz:
http://arxiv.org/abs/2112.00329
Publikováno v:
In Journal of Econometrics February 2024 239(2)
Publikováno v:
In Journal of Econometrics June 2024
Autor:
Stallcup Michael R, Kolonel Laurence N, Henderson Brian E, Le Marchand Loic, Sheng Xin, Ha Helen, Xia Lucy, Hsu Chris, Garcia Rachel R, Haiman Christopher A, Greene Geoffrey L, Press Michael F
Publikováno v:
BMC Cancer, Vol 9, Iss 1, p 43 (2009)
Abstract Background Only a limited number of studies have performed comprehensive investigations of coding variation in relation to breast cancer risk. Given the established role of estrogens in breast cancer, we hypothesized that coding variation in
Externí odkaz:
https://doaj.org/article/039c1c1f95004c74a764422cf4ee6bbe
Publikováno v:
Journal of the American Statistical Association, 2020
This paper addresses the challenges in classifying textual data obtained from open online platforms, which are vulnerable to distortion. Most existing classification methods minimize the overall classification error and may yield an undesirably large
Externí odkaz:
http://arxiv.org/abs/1802.02558
The Neyman-Pearson (NP) paradigm in binary classification seeks classifiers that achieve a minimal type II error while enforcing the prioritized type I error controlled under some user-specified level $\alpha$. This paradigm serves naturally in appli
Externí odkaz:
http://arxiv.org/abs/1802.02557
We describe inferactive data analysis, so-named to denote an interactive approach to data analysis with an emphasis on inference after data analysis. Our approach is a compromise between Tukey's exploratory (roughly speaking "model free") and confirm
Externí odkaz:
http://arxiv.org/abs/1707.06692
We develop tools to do valid post-selective inference for a family of model selection procedures, including choosing a model via cross-validated Lasso. The tools apply universally when the following random vectors are jointly asymptotically multivari
Externí odkaz:
http://arxiv.org/abs/1703.06559