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

Statistical Inference Techniques in Hypothesis Testing: Parametric Versus Nonparametric Data

Statistical Inference Techniques in Hypothesis Testing: Parametric Versus Nonparametric Data

Statistical inference techniques, paramount in hypothesis testing, differentiate into two broad categories: parametric and nonparametric statistics.
Parametric statistics, as the name suggests, assumes that data follow a specific distribution, often a normal distribution. This assumption enables robust hypothesis testing and estimation. Parametric methods, like the Student's t-test or Goodness-of-fit test, are frequently employed in biostatistics due to their robustness. For instance,...

What are Estimates?

What are Estimates?

It isn't easy to measure a parameter such as the mean height or the mean weight of a population. So, we draw samples from the population and calculate the mean height or mean weight of the individuals in the sample. This sample data acts as a representative measure of the population parameter. These sample statistics are known as estimates.
The estimate for the mean of a sample is denoted by ͞x, whereas the mean of the population is designated as μ. Further, parameters such...

Prediction Intervals

Prediction Intervals

The interval estimate of any variable is known as the prediction interval. It helps decide if a point estimate is dependable.
However, the point estimate is most likely not the exact value of the population parameter, but close to it. After calculating point estimates, we construct interval estimates, called confidence intervals or prediction intervals. This prediction interval comprises a range of values unlike the point estimate and is a better predictor of the observed sample value, y.

Improving Translational Accuracy

Improving Translational Accuracy

Base complementarity between the three base pairs of mRNA codon and the tRNA anticodon is not a failsafe mechanism. Inaccuracies can range from a single mismatch to no correct base pairing at all. The free energy difference between the correct and nearly correct base pairs can be as small as 3 kcal/ mol. With complementarity being the only proofreading step, the estimated error frequency would be one wrong amino acid in every 100 amino acids incorporated. However, error frequencies observed in...

Improving Translational Accuracy

Improving Translational Accuracy

Theory of Attribution I: Correspondent Inference Theory

Theory of Attribution I: Correspondent Inference Theory

Correspondent inference theory, proposed by Jones and Davis in 1965, seeks to explain how individuals infer stable personality traits from observed behaviors. It suggests that people attribute actions to underlying dispositions rather than external circumstances, particularly when the behavior appears intentional and socially significant.Voluntary Behavior and Dispositional AttributionAccording to this theory, individuals are more likely to attribute behavior to personal traits when it appears...

You might also read

Related Articles

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

Sort by

Same author

The subpleural pulmonary microvasculature in newborn yak (Bos grunniens).

Veterinary research communications·2008

Same author

Experimental confirmation of potential swept source optical coherence tomography performance limitations.

Applied optics·2008

Same author

A germin-like protein gene family functions as a complex quantitative trait locus conferring broad-spectrum disease resistance in rice.

Plant physiology·2008

Same author

[Spatial and temporal changes of palatal cell proliferation and cell apoptosis of retinoic acid induced mouse cleft palate in different embryonic stages].

Hua xi kou qiang yi xue za zhi = Huaxi kouqiang yixue zazhi = West China journal of stomatology·2008

Same author

Identification of an Atlantic salmon IFN multigene cluster encoding three IFN subtypes with very different expression properties.

Developmental and comparative immunology·2008

Same author

Non-Gaussian statistics and superdiffusion in a driven-dissipative dusty plasma.

Physical review. E, Statistical, nonlinear, and soft matter physics·2008

Search research articles

Related Experiment Video

Updated: Nov 4, 2025

Identification and Classification of Position-specific GABAA Receptor Subunit Missense Variants for Their Role In Hippocampal Pyramidal Neurons

Identification and Classification of Position-specific GABA_A Receptor Subunit Missense Variants for Their Role In Hippocampal Pyramidal Neurons

Published on: June 6, 2025

Improved Inference for Imputation-Based Semisupervised Learning Under Misspecified Setting.

Shaogao Lv, Linsen Wei, Qian Zhang

IEEE Transactions on Neural Networks and Learning Systems

|May 24, 2021

Summary

This summary is machine-generated.

This study theoretically justifies semisupervised learning (SSL) with kernel methods, showing unlabeled data improves inference even with misspecified settings. The proposed estimator offers smaller asymptotic variance than supervised methods alone.

More Related Videos

A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment

A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment

Published on: January 11, 2020

Related Experiment Videos

Last Updated: Nov 4, 2025

Identification and Classification of Position-specific GABAA Receptor Subunit Missense Variants for Their Role In Hippocampal Pyramidal Neurons

Identification and Classification of Position-specific GABA_A Receptor Subunit Missense Variants for Their Role In Hippocampal Pyramidal Neurons

Published on: June 6, 2025

A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment

A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment

Published on: January 11, 2020

Area of Science:

Machine Learning
Statistical Learning Theory

Background:

Semisupervised learning (SSL) is widely studied but often relies on unverifiable distributional assumptions.
Existing SSL algorithms may struggle when assumptions like cluster or low-density separation are not met in practice.

Purpose of the Study:

To provide a theoretical justification for using semisupervised learning (SSL) with kernel methods in misspecified settings.
To quantify the benefit of unlabeled data for improving inference in machine learning tasks.

Main Methods:

Investigated SSL using kernel methods under a misspecified setting, where the target function is not strictly within the hypothesis space.
Developed a two-step estimation procedure for theoretical analysis.
Analyzed the asymptotic variance of the proposed estimator.

Main Results:

Demonstrated that unlabeled data can be effectively exploited to improve inference, even when distributional assumptions are not perfectly met.
The proposed pointwise nonparametric estimator exhibits a smaller asymptotic variance compared to a purely supervised estimator.
Theoretical findings are supported by simulation experiments.

Conclusions:

Semisupervised learning with kernel methods offers a robust approach, providing theoretical guarantees for improved inference under mild, practical assumptions.
The proposed method effectively reduces estimation variance by leveraging unlabeled data, outperforming traditional supervised learning in misspecified scenarios.