1,720,989 research outputs found
sj-pdf-1-smm-10.1177_09622802221146308 - Supplemental material for Estimation of the average treatment effect with variable selection and measurement error simultaneously addressed for potential confounders
No full textSupplemental material, sj-pdf-1-smm-10.1177_09622802221146308 for Estimation of the average treatment effect with variable selection and measurement error simultaneously addressed for potential confounders by Grace Y. Yi and Li-Pang Chen in Statistical Methods in Medical Research</p
A 14 × 14 confusion matrix: Model fittness based on the LR-HeteNet method for the training data .
No full textA 14 × 14 confusion matrix: Model fittness based on the LR-HeteNet method for the training data .</p
A 14 × 14 confusion matrix: Prediction based on the LR-HeteNet method for the testing data .
No full textA 14 × 14 confusion matrix: Prediction based on the LR-HeteNet method for the testing data .</p
A 14 × 14 confusion matrix: Prediction based on the MLR-HomoNet method for the testing data .
No full textA 14 × 14 confusion matrix: Prediction based on the MLR-HomoNet method for the testing data .</p
Diagram of data analysis and implementation of the package BOOME.
No full textDiagram of data analysis and implementation of the package BOOME.</p
Heatmaps for the fitted values based on two proposed methods under the training data.
No full textThe left panel is obtained by Algorithm 1, the right panel is obtained by Algorithm 2. Z represents the proportion of (mis)classification.</p
Prediction of classification for the testing data .
No full textPrediction of classification for the testing data .</p
Variable selection and estimation for misclassified binary responses and multivariate error-prone predictors
No full textIn statistical analysis or supervised learning, classification has been an attractive topic. Typically, a main goal is to adopt predictors to characterize the primarily interested binary random variables. To model a binary response and predictors, parametric structures, such as logistic regression models or probit models, are perhaps commonly used approaches. However, due to the convenience of data collection, existence of non-informative variables as well as inevitability of measurement error in both responses and predictors become ubiquitous. The simultaneous appearance of these complex features make data analysis become challenging. To address those concerns, we propose a valid inferential method to deal with measurement error and handle variable selection simultaneously. Specifically, we focus on logistic regression or probit models, and propose estimating functions by incorporating corrected responses and predictors. After that, we develop the boosting procedure with error-eliminated estimating functions accommodated to do variable selection and estimation. To justify the proposed method, we examine the convergence of the boosting algorithm and rigorously establish the theoretical results. Through numerical studies, we find that the proposed method accurately retains informative predictors and gives precise estimators, and its performance is generally better than that without measurement error correction. The supplementary materials of this paper, including proofs of theoretical results and computer code, are available online.</p
Simulation results for two regression models with <i>n</i> = 100.
No full textSimulation results for two regression models with n = 100.</p
Estimation results based on the probit model.
No full textEstimation results based on the probit model.</p
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