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

Estimating Population Mean with Unknown Standard Deviation01:22

Estimating Population Mean with Unknown Standard Deviation

8.7K
In practice, we rarely know the population standard deviation. In the past, when the sample size was large, this did not present a problem to statisticians. They used the sample standard deviation s as an estimate for σ and proceeded as before to calculate a confidence interval with close enough results. However, statisticians ran into problems when the sample size was small. A small sample size caused inaccuracies in the confidence interval.
William S. Gosset (1876–1937) of the...
8.7K
Distributions to Estimate Population Parameter01:26

Distributions to Estimate Population Parameter

5.0K
The accurate values of population parameters such as population proportion, population mean, and population standard deviation (or variance) are usually unknown. These are fixed values that can only be estimated from the data collected from the samples. The estimates of each of these parameters are sample proportion, the sample mean, and sample standard deviation (or variance). To obtain the values of these sample statistics, data are required that have particular distribution and central...
5.0K
Estimating Population Standard Deviation01:26

Estimating Population Standard Deviation

3.3K
When the population standard deviation is unknown and the sample size is large, the sample standard deviation s is commonly used as a point estimate of σ. However, it can sometimes under or overestimate the population standard deviation. To overcome this drawback, confidence intervals are determined to estimate population parameters and eliminate any calculation bias accurately. However, this only applies to random samples from normally distributed populations. Knowing the sample mean and...
3.3K
Estimating Population Mean with Known Standard Deviation01:16

Estimating Population Mean with Known Standard Deviation

9.5K
To construct a confidence interval for a single unknown population mean μ, where the population standard deviation is known, we need sample mean as an estimate for μ and we need the margin of error. Here, the margin of error (EBM) is called the error bound for a population mean (abbreviated EBM). The sample mean is the point estimate of the unknown population mean μ.
The confidence interval estimate will have the form as follows:
(point estimate - error bound, point estimate +...
9.5K
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

1.1K
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...
1.1K
Poisson Probability Distribution01:09

Poisson Probability Distribution

11.5K
A Poisson probability distribution is a discrete probability distribution. It gives the probability of a number of events occurring in a fixed interval of time or space if these events happen at a known average rate and independently of the time since the last event. For example, a book editor might be interested in the number of words spelled incorrectly in a particular book. It might be that, on average, there are five words spelled incorrectly in 100 pages. The interval is 100 pages.
The...
11.5K

You might also read

Related Articles

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

Sort by
Same author

Nutrition knowledge, label use, and dietary diversity among a sample of university students in Bangladesh: a cross-sectional investigation.

Journal of nutritional science·2026
Same author

A cluster level study for the identification of the disparities in birth intervals between rural and urban areas of Bangladesh.

PloS one·2026
Same author

Prevalence, Risk Factors, and Antibiogram Analysis of Bovine Mastitis in Northern Bangladesh.

Veterinary sciences·2025
Same author

Food safety practice and its associated factors among household food handlers in Patuakhali, Bangladesh: A cross-sectional study.

PloS one·2025
Same author

Dominant predictors of postnatal care utilization among ever-married mothers of reproductive age in Bangladesh.

BMC pregnancy and childbirth·2025
Same author

Impact of parental education on number of under five children death per mother in Bangladesh.

PloS one·2025
Same journal

Fast penalized generalized estimating equations for large longitudinal functional datasets.

Biometrics·2026
Same journal

Causally-interpretable random-effects meta-analysis.

Biometrics·2026
Same journal

Statistical inference for mean function of partially observed functional time series.

Biometrics·2026
Same journal

Subgroup identification via Interaction Tree and Mixed Model for Repeated Measures with application to Alzheimer's disease.

Biometrics·2026
Same journal

Finite mixtures of linear quantile regressions with concomitant variables: a solution to endogeneity in longitudinal data modeling.

Biometrics·2026
Same journal

Discussion on "INTACT: a method for integration of longitudinal physical activity data from multiple sources" by Jingru Zhang, Erjia Cui, Hongzhe Li, and Haochang Shou.

Biometrics·2026
See all related articles

Related Experiment Video

Updated: Jan 2, 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

3.6K

Estimating overdispersion in sparse multinomial data.

Farzana Afroz1, Matt Parry2, David Fletcher2

  • 1Department of Statistics, Faculty of Science, University of Dhaka, Dhaka, Bangladesh.

Biometrics
|December 1, 2019
PubMed
Summary
This summary is machine-generated.

A new method for estimating overdispersion in multinomial data, common in life sciences, offers improved accuracy, especially for sparse datasets. This quasi-likelihood approach is more robust than existing methods for analyzing biological data.

Keywords:
Dirichlet-multinomiallack-of-fitmark-recapturemultinomialoverdispersionsparse data

More Related Videos

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
Basics of Multivariate Analysis in Neuroimaging Data
06:35

Basics of Multivariate Analysis in Neuroimaging Data

Published on: July 24, 2010

17.3K

Related Experiment Videos

Last Updated: Jan 2, 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

3.6K
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
Basics of Multivariate Analysis in Neuroimaging Data
06:35

Basics of Multivariate Analysis in Neuroimaging Data

Published on: July 24, 2010

17.3K

Area of Science:

  • Life sciences, including ecology, evolution, and biostatistics.
  • Statistical modeling of biological data.

Background:

  • Multinomial data are prevalent in fields like mark-recapture studies and phylogenetics.
  • Overdispersion, where variance exceeds multinomial model predictions, is common in biological data.
  • Existing estimation methods (Pearson's X², deviance D) have limitations with sparse data.

Purpose of the Study:

  • To develop a novel, more robust estimator for overdispersion in multinomial data.
  • To address the variability and bias issues associated with current estimation techniques, particularly for sparse data.
  • To improve the accuracy of statistical models in life sciences research.

Main Methods:

  • Derivation of a new quasi-likelihood estimator for overdispersion.
  • Comparison of the new estimator with existing methods based on Pearson's X² and deviance D.
  • Validation through a simulation study and application to a mark-recapture study of swifts.

Main Results:

  • The newly derived estimator exhibits smaller asymptotic variance compared to the X²-based estimator, especially for sparse data.
  • The new estimator demonstrated the lowest root mean squared error across various simulated scenarios.
  • The study highlights the practical performance differences using real-world mark-recapture data.

Conclusions:

  • The proposed estimator provides a more reliable method for quantifying overdispersion in multinomial data.
  • This advancement is particularly beneficial for analyzing sparse biological datasets, enhancing statistical inference.
  • The findings offer a valuable tool for researchers in life sciences dealing with overdispersed multinomial data.