.png){kind=icon}
返回
When Composite Likelihood meets Stochastic Approximation
.png){kind=icon}
PRE
AI
DOI：10.1080/01621459.2024.2436219.png)
.png){kind=icon}
原文求助.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
A composite likelihood is an inference function derived by multiplying a set of likelihood components. This approach provides a flexible framework for drawing inferences when the likelihood function of a statistical model is computationally intractable. While composite likelihood has computational advantages, it can still be demanding when dealing with numerous likelihood components and a large sample size. This article tackles this challenge by employing an approximation of the conventional composite likelihood estimator based on a stochastic optimization procedure. This novel estimator is shown to be asymptotically normally distributed around the true parameter. In particular, based on the relative divergent rate of the sample size and the number of iterations of the optimization, the variance of the limiting distribution is shown to compound for two sources of uncertainty: the sampling variability of the data and the optimization noise, with the latter depending on the sampling distribution used to construct the stochastic gradients. The advantages of the proposed framework are illustrated through simulation studies on two working examples: an Ising model for binary data and a gamma frailty model for count data. Finally, a real-data application is presented, showing its effectiveness in a large-scale mental health survey. Supplementary materials for this article are available online, including a standardized description of the materials available for reproducing the work.
Keyword:
Exchangeable variables central limit theorem
Gamma frailty model
Ising model
Pairwise likelihood
Stochastic gradient
期刊
J
IF:
3
论文数:
5.2K
被引数:
4.8W
机构
引用论文
Relationship of the strain-hardening exponent to the degree of strain and fulfillment of the exponential rule of hardening应变硬化指数与应变程度及硬化指数规则满足度的关系
PREAI
分享收藏EPR Spectroscopy as an Efficient Tool for Investigations of Polyelectrolyte Multilayer Growth and Local Chain DynamicsEPR波谱法作为研究聚电解质多层膜生长和局部链动力学的高效工具
PREAI
分享收藏
STATISTICAL INFERENCE FOR MODEL PARAMETERS IN STOCHASTIC GRADIENT DESCENT随机梯度下降中模型参数的统计推断.png)
ANNALS OF STATISTICS
OAAI
ANNALS OF STATISTICSIF3.7
OAAI分享收藏Concordance between Patients and Their Case Managers Using the Health of the Nation Outcome Scales
PREAI
分享收藏
查看更多内容