Results 191 to 200 of about 63,489 (260)

Optimal subsampling for regression with mixed‐type predictors

Canadian Journal of Statistics, EarlyView.
Abstract Subsampling has emerged as an appealing strategy to mitigate the computational and storage challenges imposed by large datasets. Recent subsampling techniques have shown notable computational gains for data dominated by numerical predictors. However, real‐world datasets frequently contain both numerical and categorical predictors.
Jiaqing Zhu, Lin Wang, Fasheng Sun
wiley   +1 more source

Integrating preliminary test and Stein-type techniques to improve estimation in the time-dependent Cox model. [PDF]

PLoS One
Ramezani R, Rabiei MR, Arashi M.
europepmc   +1 more source

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

Canadian 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

Extreme conditional quantile estimation for time series

Canadian Journal of Statistics, EarlyView.
Abstract We consider the estimation of an extreme conditional quantile QY(1−p|x0)$$ {Q}_Y\left(1-p|{x}_0\right) $$ for a heavy‐tailed distribution in the case of a strictly stationary time series (Xt,Yt)t∈ℤ$$ {\left({X}_t,{Y}_t\right)}_{t\in \mathbb{Z}} $$. Here, QY(·|x0)$$ {Q}_Y\left(\cdotp |{x}_0\right) $$ denotes the conditional quantile function of
Yuri Goegebeur, Armelle Guillou, Jing Qin   +2 more
wiley   +1 more source

Calibrated Model Criticism Using Split Predictive Checks. [PDF]

J Am Stat Assoc
Li J, Huggins JH.
europepmc   +1 more source

Sparse maximum likelihood estimation of regression models

Canadian 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

Demystifying Prediction-Powered Inference. [PDF]

Harv Data Sci Rev
Song Y, Kluger DM, Parikh H, Gu T.
europepmc   +1 more source

Order restricted row‐column cluster randomized design

Canadian Journal of Statistics, EarlyView.
Abstract This research introduces a novel two‐stage cluster randomized design, the order restricted row‐column cluster randomized design. The new design builds upon the cluster randomized block design by incorporating a second layer of blocking, achieved through ranking cluster units that are randomly sampled from the population.
Dalton Hopper, Omer Ozturk, Olena Kravchuk   +2 more
wiley   +1 more source

Self-Supervised OCT Representation Learning with Local Dimensionality Regularization for Automated Retinal Disease Diagnosis. [PDF]

Sensors (Basel)
Sun X, Lin W, Yuan C, Xu J, Luo Y.
europepmc   +1 more source

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

Canadian 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