Sampling from Dirichlet process mixture models with unknown concentration parameter: mixing issues in large data implementations [0.03%]
带有未知浓度参数的狄利克雷过程混合模型的大数据实现中的混合问题及样本抽取问题
David I Hastie,Silvia Liverani,Sylvia Richardson
David I Hastie
We consider the question of Markov chain Monte Carlo sampling from a general stick-breaking Dirichlet process mixture model, with concentration parameter [Formula: see text]. This paper introduces a Gibbs sampling algorithm that combines th...
Scalable estimation strategies based on stochastic approximations: Classical results and new insights [0.03%]
基于随机逼近的可扩展估计策略：经典结果与新见解
Edoardo M Airoldi,Panos Toulis
Edoardo M Airoldi
Estimation with large amounts of data can be facilitated by stochastic gradient methods, in which model parameters are updated sequentially using small batches of data at each step. Here, we review early work and modern results that illustr...
Piecewise Approximate Bayesian Computation: fast inference for discretely observed Markov models using a factorised posterior distribution [0.03%]
分段式近似贝叶斯计算：使用因子化后验分布对离散观测马尔可夫模型进行快速推理
S R White,T Kypraios,S P Preston
S R White
Many modern statistical applications involve inference for complicated stochastic models for which the likelihood function is difficult or even impossible to calculate, and hence conventional likelihood-based inferential techniques cannot b...
Group descent algorithms for nonconvex penalized linear and logistic regression models with grouped predictors [0.03%]
分组预测子的非凸惩罚线性和逻辑回归模型的组下降算法
Patrick Breheny,Jian Huang
Patrick Breheny
Penalized regression is an attractive framework for variable selection problems. Often, variables possess a grouping structure, and the relevant selection problem is that of selecting groups, not individual variables. The group lasso has be...
Shrinkage Estimation of Varying Covariate Effects Based On Quantile Regression [0.03%]
基于分位数回归的变量协方差效应收缩估计法研究
Limin Peng,Jinfeng Xu,Nancy Kutner
Limin Peng
Varying covariate effects often manifest meaningful heterogeneity in covariate-response associations. In this paper, we adopt a quantile regression model that assumes linearity at a continuous range of quantile levels as a tool to explore s...
Majorization Minimization by Coordinate Descent for Concave Penalized Generalized Linear Models [0.03%]
坐标下降的最大化最小化算法在凹惩罚广义线性模型中的应用
Dingfeng Jiang,Jian Huang
Dingfeng Jiang
Recent studies have demonstrated theoretical attractiveness of a class of concave penalties in variable selection, including the smoothly clipped absolute deviation and minimax concave penalties. The computation of the concave penalized sol...
Jinfeng Xu,Chenlei Leng,Zhiliang Ying
Jinfeng Xu
A rank-based variable selection procedure is developed for the semiparametric accelerated failure time model with censored observations where the penalized likelihood (partial likelihood) method is not directly applicable. The new method pe...
A Tutorial on Rank-based Coefficient Estimation for Censored Data in Small- and Large-Scale Problems [0.03%]
基于秩的系数估计法在小规模和大规模问题中的删失数据教程
Matthias Chung,Qi Long,Brent A Johnson
Matthias Chung
The analysis of survival endpoints subject to right-censoring is an important research area in statistics, particularly among econometricians and biostatisticians. The two most popular semiparametric models are the proportional hazards mode...
Flexible mixture modeling via the multivariate t distribution with the Box-Cox transformation: an alternative to the skew-t distribution [0.03%]
基于多元t分布与Box-Cox变换的灵活混合建模及其在偏斜t分布中的应用
Kenneth Lo,Raphael Gottardo
Kenneth Lo
Cluster analysis is the automated search for groups of homogeneous observations in a data set. A popular modeling approach for clustering is based on finite normal mixture models, which assume that each cluster is modeled as a multivariate ...
Hua Zhou,David Alexander,Kenneth Lange
Hua Zhou
In many statistical problems, maximum likelihood estimation by an EM or MM algorithm suffers from excruciatingly slow convergence. This tendency limits the application of these algorithms to modern high-dimensional problems in data mining, ...