Search research articles

ABOUT JoVE

Overview Leadership Blog JoVE Help Center

AUTHORS

Publishing Process Editorial Board Scope & Policies Peer Review FAQ Submit

LIBRARIANS

Testimonials Subscriptions Access Resources Library Advisory Board FAQ

RESEARCH

JoVE Journal Methods Collections JoVE Encyclopedia of Experiments Archive

EDUCATION

JoVE Core JoVE Business JoVE Science Education JoVE Lab Manual Faculty Resource Center Faculty Site

Terms & Conditions of Use

Related Concept Videos

The R Chart

The R Chart

In statistical process control, control charts, particularly R charts, are instrumental in monitoring process variations and identifying non-random patterns that run charts might miss. R charts track the variability within process subgroups, which is crucial when standard deviation use is impractical or unknown process variations exist.
R charts are pivotal for pinpointing shifts in process variability. Stability is indicated when all data points remain within the defined upper and lower...

Regression Toward the Mean

Regression Toward the Mean

Regression toward the mean (“RTM”) is a phenomenon in which extremely high or low values—for example, and individual’s blood pressure at a particular moment—appear closer to a group’s average upon remeasuring. Although this statistical peculiarity is the result of random error and chance, it has been problematic across various medical, scientific, financial and psychological applications. In particular, RTM, if not taken into account, can interfere when researchers try to extrapolate results...

The X̄ Chart

The X̄ Chart

The x̄ chart is a statistical tool for monitoring the means in a process.
The x̄ chart, often known as the individual control chart, is a crucial tool in statistical process control. It is designed to monitor process behavior and performance over time and is widely used in various industries to ensure that processes are operating at their optimum capacity and within specified limits.
A x̄ chart is constructed by plotting individual measurements of a quality characteristic in the order in which...

Interpreting R Charts

Interpreting R Charts

R chart, or range chart, is a fundamental tool in statistical process control used to monitor the variability within a process. It complements the X-bar (x̄) chart by focusing on the range of the data, rather than individual values, providing a clear picture of the process dispersion over time.
An R chart plots the range of subsets of measurements collected from a process. Each point on the chart represents the range—defined as the difference between the maximum and minimum values—of a sample...

Survival Curves

Survival Curves

Survival curves are graphical representations that depict the survival experience of a population over time, offering an intuitive way to track the proportion of individuals who remain event-free at each time point. These curves are widely used in fields such as medicine, public health, and reliability engineering to visualize and compare survival probabilities across different groups or conditions.
The Kaplan-Meier estimator is the most common method for constructing survival curves. This...

Residuals and Least-Squares Property

Residuals and Least-Squares Property

The vertical distance between the actual value of y and the estimated value of y. In other words, it measures the vertical distance between the actual data point and the predicted point on the line
If the observed data point lies above the line, the residual is positive, and the line underestimates the actual data value for y. If the observed data point lies below the line, the residual is negative, and the line overestimates the actual data value for y.
The process of fitting the best-fit...

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by

Same author

A generalized likelihood ratio test for monitoring profile data.

Journal of applied statistics·2022

Same author

Designs for order-of-addition experiments.

Journal of applied statistics·2022

Same author

<i>N</i><sup>6</sup>-Methyladenosine Methylomic Landscape of Lung Tissues in Murine Acute Allergic Asthma.

Frontiers in immunology·2021

Same author

Semi-Supervised Deep Transfer Learning for Benign-Malignant Diagnosis of Pulmonary Nodules in Chest CT Images.

IEEE transactions on medical imaging·2021

Same author

Anti-fatigue effects of fermented soybean protein peptides in mice.

Journal of the science of food and agriculture·2021

Same author

Effects of topical application of different molecular weight marine fish skin collagen oligopeptides on UVB-induced photoaging rat skin.

Journal of cosmetic dermatology·2021

Search research articles

Related Experiment Video

Updated: May 12, 2026

Establishing a Competing Risk Regression Nomogram Model for Survival Data

Establishing a Competing Risk Regression Nomogram Model for Survival Data

Published on: October 23, 2020

PROFILE CONTROL CHARTS BASED ON NONPARAMETRIC L-1 REGRESSION METHODS.

Ying Wei¹, Zhibiao Zhao, Dennis K J Lin

¹Department of Biostatistics, Columbia University, 722 West 168th St., New York, New York 10032, USA.

The Annals of Applied Statistics

|March 30, 2013

Summary

This summary is machine-generated.

This study introduces a new nonparametric L-1 location-scale model for monitoring functional profiles. It effectively screens profile shapes by analyzing location shifts, local distortions, and overall deviations, offering valuable insights for quality control.

Keywords:

Functional data L-1 regression nonparametric methods profile control charts

More Related Videos

Assessing Cerebral Autoregulation via Oscillatory Lower Body Negative Pressure and Projection Pursuit Regression

Assessing Cerebral Autoregulation via Oscillatory Lower Body Negative Pressure and Projection Pursuit Regression

Published on: December 10, 2014

Development of an Individual-Tree Basal Area Increment Model using a Linear Mixed-Effects Approach

Development of an Individual-Tree Basal Area Increment Model using a Linear Mixed-Effects Approach

Published on: July 3, 2020

Related Experiment Videos

Last Updated: May 12, 2026

Establishing a Competing Risk Regression Nomogram Model for Survival Data

Establishing a Competing Risk Regression Nomogram Model for Survival Data

Published on: October 23, 2020

Assessing Cerebral Autoregulation via Oscillatory Lower Body Negative Pressure and Projection Pursuit Regression

Assessing Cerebral Autoregulation via Oscillatory Lower Body Negative Pressure and Projection Pursuit Regression

Published on: December 10, 2014

Development of an Individual-Tree Basal Area Increment Model using a Linear Mixed-Effects Approach

Development of an Individual-Tree Basal Area Increment Model using a Linear Mixed-Effects Approach

Published on: July 3, 2020

Area of Science:

Statistical Process Control
Nonparametric Statistics
Quality Engineering

Background:

Traditional statistical process control often uses univariate data.
Modern quality assessment frequently requires analyzing functional relationships between variables.
Monitoring functional profiles is crucial in many industries.

Purpose of the Study:

To develop a novel nonparametric L-1 location-scale model for screening functional profile shapes.
To quantify profile deviations using three distinct metrics: location shifts, local shape distortions, and overall shape deviations.

Main Methods:

Development of a nonparametric L-1 location-scale model.
Quantification of profile shape characteristics through three individual metrics.
Application of the model to vertical density profile data.

Main Results:

The proposed model effectively screens the shapes of functional profiles.
The analysis of vertical density profile data yielded interesting insights.
The model successfully identified location shifts, local distortions, and overall deviations.

Conclusions:

The novel nonparametric L-1 location-scale model provides a robust method for functional profile monitoring.
The approach offers a valuable tool for quality control in applications involving complex functional data.
Further insights can be gained by applying this method to various profile data sets.