Results 161 to 170 of about 1,456,492 (292)

Large parameter asymptotic analysis for homogeneous normalized random measures with independent increments

open access: yesCanadian Journal of Statistics, EarlyView.
Abstract Homogeneous normalized random measures with independent increments represent a broad class of Bayesian nonparametric priors and thus are widely used. In this article, we obtain the strong law of large numbers, the central limit theorem (CLT), and the functional central limit theorem (fCLT) of such measures when the concentration parameter a ...
Junxi Zhang, Shui Feng, Yaozhong Hu
wiley   +1 more source

Sparse maximum likelihood estimation of regression models

open access: yesCanadian Journal of Statistics, EarlyView.
Abstract For regression model selection and estimation, we study a small set of candidate models of maximum likelihood from which all information criteria such as the Akaike information criterion (AIC) and the Bayesian information criterion (BIC) choose their models.
Min Tsao
wiley   +1 more source

Learning sparse mixture‐of‐experts generalized linear models in ultrahigh dimensions

open access: yesCanadian Journal of Statistics, EarlyView.
Abstract Motivated by the challenges of heterogeneity and high dimensionality in the analysis of modern data, we investigate continuous regularization methods for learning sparse mixture‐of‐experts generalized linear models (MoE‐GLM). Although there are foundational results about regularized estimators in a broad class of regression models including ...
Pengqi Liu, Abbas Khalili
wiley   +1 more source

Model selection for causal inference in missing exposure problems

open access: yesCanadian Journal of Statistics, EarlyView.
Abstract In causal inference, properly selecting the propensity score (PS) model is an important topic and has been widely investigated in observational studies. However, there are very few studies investigating the model selection issue for causal inference when the exposure is missing at random (MAR). When the exposure variable contains missing data,
Yuliang Shi, Yeying Zhu, Joel A. Dubin
wiley   +1 more source

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