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

Stratified Sampling Method01:16

Stratified Sampling Method

11.6K
Sampling is a technique to select a portion (or subset) of the larger population and study that portion (the sample) to gain information about the population. The sampling method ensures that samples are drawn without bias and accurately represent the population. Because measuring the entire population in a study is not practical, researchers use samples to represent the population of interest.
To choose a stratified sample, divide the population into groups called strata and then take a...
11.6K
Estimating Population Mean with Known Standard Deviation01:16

Estimating Population Mean with Known Standard Deviation

8.2K
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 +...
8.2K
Estimating Population Mean with Unknown Standard Deviation01:22

Estimating Population Mean with Unknown Standard Deviation

7.5K
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...
7.5K
Cluster Sampling Method01:20

Cluster Sampling Method

11.5K
Appropriate sampling methods ensure that samples are drawn without bias and accurately represent the population. Because measuring the entire population in a study is not practical, researchers use samples to represent the population of interest.
To choose a cluster sample, divide the population into clusters (groups) and then randomly select some of the clusters. All the members from these clusters are in the cluster sample. For example, if you randomly sample four departments from your...
11.5K
What are Estimates?01:06

What are Estimates?

4.9K
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...
4.9K
Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving01:29

Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving

25
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...
25

You might also read

Related Articles

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

Sort by
Same author

Hemodynamic effects of umbilical cord milking versus early cord clamping in nonvigorous intrauterine growth restricted neonates: a randomized controlled trial.

European journal of pediatrics·2026
Same author

Physical and mental health impact of perimenopause, menopause and post menopause in a diverse global population (MARIE Project- Global Chapter WP 2a): cross-sectional quantitative data from a mixed-methods study.

EClinicalMedicine·2026
Same author

Optimal site of tactile stimulation during initial steps of neonatal resuscitation: a three-arm randomized controlled trial.

Resuscitation·2026
Same author

Isolation, characterization and evaluation of growth kinetics and multilineage differentiation of ovine ovarian mesenchymal stem cells.

Theriogenology·2026
Same author

Topical application of emollients to maintain skin integrity in preterm neonates: a systematic review and meta-analysis.

European journal of pediatrics·2026
Same author

Development and characterization of a reserpine-induced mouse model of depression: An integrated in-vivo and in-silico approach.

Naunyn-Schmiedeberg's archives of pharmacology·2026

Related Experiment Video

Updated: May 7, 2025

Sampling Soils in a Heterogeneous Research Plot
07:11

Sampling Soils in a Heterogeneous Research Plot

Published on: January 7, 2019

34.3K

Optimizing population mean estimation in stratified sampling using linear cost: A simulation study.

Poonam Singh1, Prayas Sharma2, Rajesh Singh1

  • 1Department of Statistics, Banaras Hindu University, Varanasi, India.

Heliyon
|December 30, 2024
PubMed
Summary
This summary is machine-generated.

This study introduces new generalized exponential estimators for stratified sampling, improving efficiency and reducing survey costs. These novel methods offer superior performance compared to existing techniques in real-world applications.

Keywords:
Auxiliary informationCostInteger programming problemsLagrange's multiplierMean square error (MSE)OptimizationPercentage relative efficiency (PRE)SimulationStratified sampling

More Related Videos

Inverse Probability of Treatment Weighting Propensity Score using the Military Health System Data Repository and National Death Index
06:55

Inverse Probability of Treatment Weighting Propensity Score using the Military Health System Data Repository and National Death Index

Published on: January 8, 2020

14.3K
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

3.3K

Related Experiment Videos

Last Updated: May 7, 2025

Sampling Soils in a Heterogeneous Research Plot
07:11

Sampling Soils in a Heterogeneous Research Plot

Published on: January 7, 2019

34.3K
Inverse Probability of Treatment Weighting Propensity Score using the Military Health System Data Repository and National Death Index
06:55

Inverse Probability of Treatment Weighting Propensity Score using the Military Health System Data Repository and National Death Index

Published on: January 8, 2020

14.3K
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

3.3K

Area of Science:

  • Statistics
  • Survey Methodology
  • Applied Mathematics

Background:

  • Improving sampling efficiency is a persistent challenge.
  • Simultaneously enhancing estimator efficacy and optimizing survey costs is crucial in fields like medicine, agriculture, and transportation.

Purpose of the Study:

  • To develop a family of generalized exponential estimators for population mean estimation in stratified sampling.
  • To optimize survey costs using integer programming and Lagrange multipliers within a fixed budget.

Main Methods:

  • Derivation of the Mean Square Error (MSE) for proposed estimators.
  • Formulation of an optimization problem to refine estimator performance under cost constraints.
  • Utilization of integer programming and Lagrange multipliers for cost optimization.

Main Results:

  • Proposed generalized exponential estimators significantly outperform existing alternatives.
  • Theoretical and empirical evaluations confirm the superiority of the new estimators.
  • Demonstrated practical relevance and theoretical robustness through real-world dataset validation.

Conclusions:

  • The developed estimators offer a robust and efficient solution for stratified sampling.
  • The methodology effectively balances estimator performance with survey cost optimization.
  • Findings have broad applicability across various data collection domains.