Pages that link to "Item:Q3069890"
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The following pages link to Multivariate Statistical Process Control Using LASSO (Q3069890):
Displaying 46 items.
- Multivariate EWMA control chart based on a variable selection using AIC for multivariate statistical process monitoring (Q491685) (← links)
- On-line control of false discovery rates for multiple datastreams (Q680387) (← links)
- LASSO-based multivariate linear profile monitoring (Q763195) (← links)
- On the distribution of the \(\operatorname{T}^2\) statistic, used in statistical process monitoring, for high-dimensional data (Q826657) (← links)
- Simultaneous monitoring of process mean vector and covariance matrix via penalized likelihood estimation (Q1623641) (← links)
- Controlling bivariate categorical processes using scan rules (Q1694510) (← links)
- Bayesian sequential update for monitoring and control of high-dimensional processes (Q2095228) (← links)
- Statistical Perspectives on “Big Data” (Q2787301) (← links)
- Comparison of Phase II Control Charts Based on Variable Selection Methods (Q2787310) (← links)
- A Double Sequential Weighted Probability Ratio Test for One-Sided Composite Hypotheses (Q2864685) (← links)
- Sequential Two-stage D-optimality Sensitivity Test for Binary Response Data (Q2943798) (← links)
- A spatial rank-based multivariate EWMA control chart (Q3166684) (← links)
- Adaptive multivariate EWMA charts for monitoring sparse mean shifts based on parameter optimization design (Q3389673) (← links)
- Thresholding-based outlier detection for high-dimensional data (Q4960672) (← links)
- Directional monitoring and diagnosis for covariance matrices (Q5073412) (← links)
- Univariate fast initial response statistical process control with taut strings (Q5093000) (← links)
- Outlier detection in non-parametric profile monitoring (Q5095840) (← links)
- A setwise EWMA scheme for monitoring high-dimensional datastreams (Q5107058) (← links)
- Sequential LND sensitivity test for binary response data (Q5129120) (← links)
- Asymptotic optimality of double sequential mixture likelihood ratio test (Q5219949) (← links)
- Multivariate Control Chart Based on Multivariate Smirnov Test (Q5265815) (← links)
- A Distribution-free Multivariate Change-point Model for Statistical Process Control (Q5265818) (← links)
- A Lasso-type Robust Variable Selection for Time-Course Microarray Data (Q5265839) (← links)
- An Empirical-Likelihood-Based Multivariate EWMA Control Scheme (Q5299086) (← links)
- Rapid detection of hot-spots via tensor decomposition with applications to crime rate data (Q5865400) (← links)
- Big Data? Statistical Process Control Can Help! (Q5869301) (← links)
- Large-Scale Datastreams Surveillance via Pattern-Oriented-Sampling (Q5885104) (← links)
- Discussion on “Sequential detection/isolation of abrupt changes” by Igor V. Nikiforov (Q5890984) (← links)
- Statistical quality control using image intelligence: A sparse learning approach (Q6051603) (← links)
- Fault classification for high‐dimensional data streams: A directional diagnostic framework based on multiple hypothesis testing (Q6077375) (← links)
- Post Hotelling's <i>T</i> -square procedure to identify fault variables (Q6552930) (← links)
- An adaptive multivariate EWMA control chart for monitoring missing data (Q6592377) (← links)
- Activation discovery with FDR control: application to fMRI data (Q6593379) (← links)
- Dynamic modeling and online monitoring of tensor data streams with application to passenger flow surveillance (Q6616320) (← links)
- Real-Time Monitoring of High-Dimensional Functional Data Streams via Spatio-Temporal Smooth Sparse Decomposition (Q6622420) (← links)
- Reliable Post-Signal Fault Diagnosis for Correlated High-Dimensional Data Streams (Q6631073) (← links)
- Bandit Change-Point Detection for Real-Time Monitoring High-Dimensional Data Under Sampling Control (Q6631108) (← links)
- A Bayesian Partially Observable Online Change Detection Approach with Thompson Sampling (Q6631124) (← links)
- A Covariate-Regulated Sparse Subspace Learning Model and Its Application to Process Monitoring and Fault Isolation (Q6631131) (← links)
- Joint Diagnosis of High-Dimensional Process Mean and Covariance Matrix based on Bayesian Model Selection (Q6631154) (← links)
- A General Framework for Robust Monitoring of Multivariate Correlated Processes (Q6631176) (← links)
- Adaptive Process Monitoring Using Covariate Information (Q6631892) (← links)
- A Diagnostic Procedure for High-Dimensional Data Streams via Missed Discovery Rate Control (Q6636529) (← links)
- Statistical Process Monitoring from Industry 2.0 to Industry 4.0: Insights into Research and Practice (Q6637476) (← links)
- Monitoring of group-structured high-dimensional processes via sparse group Lasso (Q6638857) (← links)
- On monitoring high-dimensional processes with individual observations (Q6659076) (← links)
