Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Concept Videos

Residuals and Least-Squares Property01:11

Residuals and Least-Squares Property

8.8K
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...
8.8K
Multiple Regression01:25

Multiple Regression

3.7K
Multiple regression assesses a linear relationship between one response or dependent variable and two or more independent variables. It has many practical applications.
Farmers can use multiple regression to determine the crop yield based on more than one factor, such as water availability, fertilizer, soil properties, etc. Here, the crop yield is the response or dependent variable as it depends on the other independent variables. The analysis requires the construction of a scatter plot...
3.7K
Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving01:29

Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving

222
Mechanistic models play a crucial role in algorithms for numerical problem-solving, particularly in nonlinear mixed effects modeling (NMEM). These models aim to minimize specific objective functions by evaluating various parameter estimates, leading to the development of systematic algorithms. In some cases, linearization techniques approximate the model using linear equations.
In individual population analyses, different algorithms are employed, such as Cauchy's method, which uses a...
222
One-Compartment Open Model: Wagner-Nelson and Loo Riegelman Method for ka Estimation01:24

One-Compartment Open Model: Wagner-Nelson and Loo Riegelman Method for ka Estimation

1.0K
This lesson introduces two critical methods in pharmacokinetics, the Wagner-Nelson and Loo-Riegelman methods, used for estimating the absorption rate constant (ka) for drugs administered via non-intravenous routes. The Wagner-Nelson method relates ka to the plasma concentration derived from the slope of a semilog percent unabsorbed time plot. However, it is limited to drugs with one-compartment kinetics and can be impacted by factors like gastrointestinal motility or enzymatic degradation.
On...
1.0K
Regression Analysis01:11

Regression Analysis

7.6K
Regression analysis is a statistical tool that describes a mathematical relationship between a dependent variable and one or more independent variables.
In regression analysis, a regression equation is determined based on the line of best fit– a line that best fits the data points plotted in a graph. This line is also called the regression line. The algebraic equation for the regression line is called the regression equation. It is represented as:
7.6K
Regression Toward the Mean01:52

Regression Toward the Mean

6.8K
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...
6.8K

You might also read

Related Articles

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

Sort by
Same author

A new class of efficient and debiased two-step shrinkage estimators: method and application.

Journal of applied statistics·2022
Same author

Modified ridge-type for the Poisson regression model: simulation and application.

Journal of applied statistics·2022
Same author

A new kind of stochastic restricted biased estimator for logistic regression model.

Journal of applied statistics·2022
Same author

A new Poisson Liu Regression Estimator: method and application.

Journal of applied statistics·2022
Same author

Modified Kibria-Lukman (MKL) estimator for the Poisson Regression Model: application and simulation.

F1000Research·2022
Same author

Unbiased K-L estimator for the linear regression model.

F1000Research·2022

Related Experiment Video

Updated: Dec 19, 2025

Development of an Individual-Tree Basal Area Increment Model using a Linear Mixed-Effects Approach
04:35

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

Published on: July 3, 2020

3.6K

Two-Parameter Modified Ridge-Type M-Estimator for Linear Regression Model.

Adewale F Lukman1, Kayode Ayinde2, B M Golam Kibria3

  • 1Department of Physical Sciences, Landmark University, Omu-Aran, Nigeria.

Thescientificworldjournal
|June 9, 2020
PubMed
Summary
This summary is machine-generated.

This study introduces a new two-parameter ridge-type modified M-estimator (RTMME) to address multicollinearity and outliers in linear regression. The RTMME demonstrates superior performance over existing methods in simulations and a case study.

More Related Videos

Assessing Cerebral Autoregulation via Oscillatory Lower Body Negative Pressure and Projection Pursuit Regression
11:26

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

Published on: December 10, 2014

12.7K
A Method of Trigonometric Modelling of Seasonal Variation Demonstrated with Multiple Sclerosis Relapse Data
10:46

A Method of Trigonometric Modelling of Seasonal Variation Demonstrated with Multiple Sclerosis Relapse Data

Published on: December 9, 2015

10.9K

Related Experiment Videos

Last Updated: Dec 19, 2025

Development of an Individual-Tree Basal Area Increment Model using a Linear Mixed-Effects Approach
04:35

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

Published on: July 3, 2020

3.6K
Assessing Cerebral Autoregulation via Oscillatory Lower Body Negative Pressure and Projection Pursuit Regression
11:26

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

Published on: December 10, 2014

12.7K
A Method of Trigonometric Modelling of Seasonal Variation Demonstrated with Multiple Sclerosis Relapse Data
10:46

A Method of Trigonometric Modelling of Seasonal Variation Demonstrated with Multiple Sclerosis Relapse Data

Published on: December 9, 2015

10.9K

Area of Science:

  • Statistics
  • Econometrics
  • Data Science

Background:

  • The general linear regression model is widely used, with Ordinary Least Squares (OLS) as a common parameter estimation method.
  • OLS estimators face challenges with multicollinearity and outliers, potentially compromising analysis results.
  • Existing methods may not effectively handle the combined impact of these issues.

Purpose of the Study:

  • To propose a novel two-parameter ridge-type modified M-estimator (RTMME).
  • To address the dual problems of multicollinearity and outliers in linear regression analysis.
  • To evaluate the performance of the RTMME against established estimators.

Main Methods:

  • Development of a new M-estimator incorporating ridge-type adjustments.
  • Theoretical derivations to establish the properties of the RTMME.
  • Monte Carlo simulations to compare estimator performance under various conditions.
  • Application to a numerical example for practical validation.

Main Results:

  • The proposed RTMME effectively mitigates issues arising from multicollinearity and outliers.
  • Simulation results indicate that RTMME outperforms the modified ridge-type estimator and other compared estimators.
  • The theoretical properties of RTMME support its robustness and efficiency.

Conclusions:

  • The RTMME offers a robust and effective solution for linear regression problems with multicollinearity and outliers.
  • This new estimator provides a valuable alternative for practitioners dealing with contaminated data.
  • The study confirms the practical utility and statistical advantages of the RTMME.