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

Statistical Inference Techniques in Hypothesis Testing: Parametric Versus Nonparametric Data01:16

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, comparing...
Statistical Hypothesis Testing01:16

Statistical Hypothesis Testing

Hypothesis testing is a critical statistical procedure facilitating informed, evidence-based decisions. It begins with a hypothesis, which is a tentative explanation, or a prediction about a population parameter. This hypothesis can be either a null hypothesis (H0), indicating no effect or difference, or an alternative hypothesis (Ha), suggesting an effect or difference.
Statistical significance measures the probability that an observed result occurred by chance. If this probability, known as...
Decision Making: Traditional Method01:14

Decision Making: Traditional Method

The process of hypothesis testing based on the traditional method includes calculating the critical value, testing the value of the test statistic using the sample data, and interpreting these values.
First, a specific claim about the population parameter is decided based on the research question and is stated in a simple form. Further, an opposing statement to this claim is also stated. These statements can act as null and alternative hypotheses, out of which a null hypothesis would be a...
Accuracy and Errors in Hypothesis Testing01:13

Accuracy and Errors in Hypothesis Testing

Hypothesis testing is a fundamental statistical tool that begins with the assumption that the null hypothesis H0 is true. During this process, two types of errors can occur: Type I and Type II. A Type I error refers to the incorrect rejection of a true null hypothesis, while a Type II error involves the failure to reject a false null hypothesis.
In hypothesis testing, the probability of making a Type I error, denoted as α, is commonly set at 0.05. This significance level indicates a 5% chance...
What are Estimates?01:06

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 as the mean,...
Statistical Methods to Analyze Parametric Data: Student t-Test and Goodness-of-Fit Test01:09

Statistical Methods to Analyze Parametric Data: Student t-Test and Goodness-of-Fit Test

In parametric statistics, two fundamental tests stand out for their utility and wide application: the Student's t-test and goodness-of-fit tests. These tests provide researchers with a robust method for drawing insights from data, testing hypotheses, and making informed decisions based on their findings.
The Student's t-test is a statistical test that examines if there is a statistically significant difference between the means of two groups. This test is instrumental when dealing with data...

You might also read

Related Articles

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

Sort by
Same author

Empowering classification for multivariate functional data with simultaneous feature selection.

Statistical methods in medical research·2026
Same author

Kin Keeper<sup>SM</sup> Breast and Cervical Cancer Prevention: An Educational Intervention: A Community-Based Randomized Controlled Trial in Black, Latina, and Arab Women.

Journal of cancer education : the official journal of the American Association for Cancer Education·2026
Same author

LTOFusion: A Learning-to-Optimize Framework With Flow Matching for Unsupervised Image Fusion.

IEEE transactions on image processing : a publication of the IEEE Signal Processing Society·2026
Same author

A nonparametric dependent competing risk method for net survival analysis.

The international journal of biostatistics·2026
Same author

Genetic dissection of adult-plant resistance to stripe rust in winter wheat line Tianmin 668.

Plant disease·2026
Same author

Unintended Vagus Nerve Stimulation From Cuff Electrode During MRI: Combined Effects of Gradient and Radiofrequency Fields.

Magnetic resonance in medicine·2026
Same journal

Inference on data with both multiplicative and additive measurement errors.

Scandinavian journal of statistics, theory and applications·2026
Same journal

Xuran Meng and Yi Li's contribution to the Discussion of "On optimal linear prediction" by I. Helland.

Scandinavian journal of statistics, theory and applications·2026
Same journal

Post-selection inference for high-dimensional mediation analysis with survival outcomes.

Scandinavian journal of statistics, theory and applications·2025
Same journal

Post-selection inference for the Cox model with interval-censored data.

Scandinavian journal of statistics, theory and applications·2025
Same journal

Enriched Pitman-Yor processes.

Scandinavian journal of statistics, theory and applications·2025
Same journal

A New Paradigm for High-dimensional Data: Distance-Based Semiparametric Feature Aggregation Framework via Between-Subject Attributes.

Scandinavian journal of statistics, theory and applications·2024
See all related articles

Related Experiment Video

Updated: May 10, 2026

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

Evaluating Statistical Hypotheses Using Weakly-Identifiable Estimating Functions.

Guanqun Cao1, David Todem, Lijian Yang

  • 1Department of Statistics and Probability, Michigan State University.

Scandinavian Journal of Statistics, Theory and Applications
|June 22, 2013
PubMed
Summary
This summary is machine-generated.

This study introduces a new statistical testing method for models with non- and weakly-identified parameters. This approach allows for hypothesis evaluation and significance assessment in complex semiparametric models.

Keywords:
estimating equationsglobal sensitivity analysisinfimum and supremum statisticsmissing not at randommodel misspecificationpseudolikelihood

More Related Videos

Applying an eMASS Customization Program as a Research Tool to Evaluate Consumer Benefits
08:27

Applying an eMASS Customization Program as a Research Tool to Evaluate Consumer Benefits

Published on: September 27, 2019

The Innovation Arena: A Method for Comparing Innovative Problem-Solving Across Groups
14:14

The Innovation Arena: A Method for Comparing Innovative Problem-Solving Across Groups

Published on: May 13, 2022

Related Experiment Videos

Last Updated: May 10, 2026

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

Applying an eMASS Customization Program as a Research Tool to Evaluate Consumer Benefits
08:27

Applying an eMASS Customization Program as a Research Tool to Evaluate Consumer Benefits

Published on: September 27, 2019

The Innovation Arena: A Method for Comparing Innovative Problem-Solving Across Groups
14:14

The Innovation Arena: A Method for Comparing Innovative Problem-Solving Across Groups

Published on: May 13, 2022

Area of Science:

  • Statistics
  • Econometrics
  • Biostatistics

Background:

  • Many statistical models face challenges with non- and weakly-identified parameters.
  • Identifiability issues hinder conventional hypothesis testing and significance assessment.

Purpose of the Study:

  • To develop a novel statistical testing procedure for semiparametric models with non- and weakly-identified parameters.
  • To provide a method for evaluating hypotheses and assessing statistical significance in the presence of identifiability concerns.

Main Methods:

  • Constructing a test statistic from a general estimating function for finite-dimensional parameters.
  • Leaving infinite-dimensional nuisance parameters unspecified.
  • Deriving the limiting distribution of the test statistic.
  • Proposing resampling methods to approximate the asymptotic distribution.

Main Results:

  • The developed testing procedure is applicable to semiparametric models with non- and weakly-identified parameters.
  • Theoretical justification for the limiting distribution and resampling approaches is provided.
  • The methodology demonstrates practical utility through simulations and real-world data analysis.

Conclusions:

  • The proposed method extends statistical testing capabilities for challenging model identifiability.
  • It offers a robust framework for hypothesis evaluation in semiparametric settings.
  • The approach is validated by simulation studies and application to breast cancer quality-of-life data.