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Related Concept Videos

Variability: Analysis01:11

Variability: Analysis

Measures of variability are statistical metrics that reveal the dispersion pattern within a dataset. They are pivotal in biostatistics, providing insights into the heterogeneity within health and biological data. Variability signifies the degree to which data points diverge from one another, helping researchers understand the potential range of values and associated uncertainty within the data.
The range is a simple measure of variability, indicating the difference between the highest and...
Types of Selection01:46

Types of Selection

Natural selection influences the frequencies of particular alleles and phenotypes within populations in several different ways. Primarily, natural selection can be directional, stabilizing, or disruptive. Directional selection favors one extreme trait and shifts the population towards that phenotype while selecting against individuals displaying alternate traits. Stabilizing selection favors an intermediate trait with a narrow range of variation. Deviation from the optimal phenotype towards an...
Frequency-dependent Selection01:21

Frequency-dependent Selection

When the fitness of a trait is influenced by how common it is (i.e., its frequency) relative to different traits within a population, this is referred to as frequency-dependent selection. Frequency-dependent selection may occur between species or within a single species. This type of selection can either be positive—with more common phenotypes having higher fitness—or negative, with rarer phenotypes conferring increased fitness.Positive Frequency-Dependent SelectionIn positive...
Outliers and Influential Points01:08

Outliers and Influential Points

An outlier is an observation of data that does not fit the rest of the data. It is sometimes called an extreme value. When you graph an outlier, it will appear not to fit the pattern of the graph. Some outliers are due to mistakes (for example, writing down 50 instead of 500), while others may indicate that something unusual is happening. Outliers are present far from the least squares line in the vertical direction. They have large "errors," where the "error" or residual is the vertical...
Statistical Analysis: Overview01:11

Statistical Analysis: Overview

When we take repeated measurements on the same or replicated samples, we will observe inconsistencies in the magnitude. These inconsistencies are called errors. To categorize and characterize these results and their errors, the researcher can use statistical analysis to determine the quality of the measurements and/or suitability of the methods.
One of the most commonly used statistical quantifiers is the mean, which is the ratio between the sum of the numerical values of all results and the...
Multi-input and Multi-variable systems01:22

Multi-input and Multi-variable systems

Cruise control systems in cars are designed as multi-input systems to maintain a driver's desired speed while compensating for external disturbances such as changes in terrain. The block diagram for a cruise control system typically includes two main inputs: the desired speed set by the driver and any external disturbances, such as the incline of the road. By adjusting the engine throttle, the system maintains the vehicle's speed as close to the desired value as possible.
In the absence of...

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Updated: Jun 2, 2026

Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances
07:35

Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances

Published on: October 11, 2018

A Selective Overview of Variable Selection in High Dimensional Feature Space.

Jianqing Fan1, Jinchi Lv

  • 1Frederick L. Moore '18 Professor of Finance, Department of Operations Research and Financial Engineering, Princeton University, Princeton, NJ 08544, USA ( jqfan@princeton.edu ).

Statistica Sinica
|May 17, 2011
PubMed
Summary
This summary is machine-generated.

High dimensional statistical problems require efficient variable selection. This review covers recent advances in methods and theory for high dimensional variable selection, including non-concave penalties and ultra-high dimensional approaches.

Related Experiment Videos

Last Updated: Jun 2, 2026

Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances
07:35

Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances

Published on: October 11, 2018

Area of Science:

  • Statistics
  • Machine Learning
  • Data Science

Background:

  • High dimensional statistical problems are prevalent across scientific fields.
  • Variable selection is crucial for statistical learning and scientific discovery.
  • Traditional best subset selection is computationally intensive for modern applications.

Purpose of the Study:

  • To provide a concise overview of recent theoretical and methodological developments in high dimensional variable selection.
  • To discuss the limitations and capabilities of current high dimensional variable selection techniques.
  • To highlight the role of penalty functions and non-concave penalties in statistical modeling.

Main Methods:

  • Review of penalized likelihood methods for high dimensional data.
  • Discussion of theoretical advancements in variable selection.
  • Exploration of non-concave penalized likelihood properties.
  • Overview of ultra-high dimensional variable selection strategies.

Main Results:

  • Penalized likelihood methods offer effective solutions for high dimensionality.
  • Understanding dimensionality limits and penalty functions drives methodological progress.
  • Non-concave penalties offer unique advantages in high dimensional statistical modeling.
  • Independence screening and two-scale methods show promise for ultra-high dimensions.

Conclusions:

  • Recent developments have significantly advanced high dimensional variable selection theory and methods.
  • Non-concave penalized likelihood plays a key role in modern statistical modeling.
  • Continued research is essential for tackling ultra-high dimensional statistical challenges.