Estimation of High-Dimensional Graphical Models Using Regularized Score Matching [0.03%]
高维图模型的正则化分数匹配估计方法
Lina Lin,Mathias Drton,Ali Shojaie
Lina Lin
Graphical models are widely used to model stochastic dependences among large collections of variables. We introduce a new method of estimating undirected conditional independence graphs based on the score matching loss, introduced by Hyvär...
Yining Chen,Jon A Wellner
Yining Chen
We prove that the convex least squares estimator (LSE) attains a n-1/2 pointwise rate of convergence in any region where the truth is linear. In addition, the asymptotic distribution can be characterized by a modified invelope process. Anal...
Takumi Saegusa,Ali Shojaie
Takumi Saegusa
We introduce a general framework for estimation of inverse covariance, or precision, matrices from heterogeneous populations. The proposed framework uses a Laplacian shrinkage penalty to encourage similarity among estimates from disparate, ...
Kean Ming Tan,Daniela Witten
Kean Ming Tan
In this manuscript, we study the statistical properties of convex clustering. We establish that convex clustering is closely related to single linkage hierarchical clustering and k-means clustering. In addition, we derive the range of the t...
A test of homogeneity for age-dependent branching processes with immigration [0.03%]
基于年龄的带移民分支过程的齐性检验
Ollivier Hyrien,Nikolay M Yanev,Craig T Jordan
Ollivier Hyrien
We propose a novel procedure to test whether the immigration process of a discretely observed age-dependent branching process with immigration is time-homogeneous. The construction of the test is motivated by the behavior of the coefficient...
Abel Rodríguez,Alex Lenkoski,Adrian Dobra
Abel Rodríguez
Standard Gaussian graphical models implicitly assume that the conditional independence among variables is common to all observations in the sample. However, in practice, observations are usually collected from heterogeneous populations wher...
Computationally efficient confidence intervals for cross-validated area under the ROC curve estimates [0.03%]
交叉验证下ROC曲线下方面积估计的有效置信区间计算方法
Erin LeDell,Maya Petersen,Mark van der Laan
Erin LeDell
In binary classification problems, the area under the ROC curve (AUC) is commonly used to evaluate the performance of a prediction model. Often, it is combined with cross-validation in order to assess how the results will generalize to an i...
Mark S Handcock,Krista J Gile,Corinne M Mar
Mark S Handcock
Respondent-Driven Sampling (RDS) is n approach to sampling design and inference in hard-to-reach human populations. It is often used in situations where the target population is rare and/or stigmatized in the larger population, so that it i...
Rahul Mazumder,Trevor Hastie
Rahul Mazumder
The graphical lasso [5] is an algorithm for learning the structure in an undirected Gaussian graphical model, using ℓ1 regularization to control the number of zeros in the precision matrix Θ = Σ-1 [2, 11]. The R package GLASSO [5] is pop...
Comment on "Dynamic treatment regimes: technical challenges and applications" [0.03%]
有关“动态治疗方案”的评论：技术挑战及其应用
Yair Goldberg,Rui Song,Donglin Zeng et al.
Yair Goldberg et al.
Inference for parameters associated with optimal dynamic treatment regimes is challenging as these estimators are nonregular when there are non-responders to treatments. In this discussion, we comment on three aspects of alleviating this no...