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

Estimating Population Mean with Unknown Standard Deviation

Estimating Population Mean with Unknown Standard Deviation

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 Guinness...

Distributions to Estimate Population Parameter

Distributions to Estimate Population Parameter

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...

Cluster Sampling Method

Cluster Sampling Method

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...

Estimating Population Mean with Known Standard Deviation

Estimating Population Mean with Known Standard Deviation

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 + error bound)
The...

One-Compartment Open Model: Wagner-Nelson and Loo Riegelman Method for ka Estimation

One-Compartment Open Model: Wagner-Nelson and Loo Riegelman Method for k_a Estimation

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...

Estimating Population Standard Deviation

Estimating Population Standard Deviation

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...

You might also read

Related Articles

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

Sort by

Same author

Longitudinal cognitive outcomes in two progressive supranuclear palsy clinical trials.

Alzheimer's & dementia : the journal of the Alzheimer's Association·2026

Same author

Longitudinal effects of inpatient music therapy dosage on pain intensity and opioid utilization among patients with hematologic or oncologic conditions: a retrospective cohort study.

Supportive care in cancer : official journal of the Multinational Association of Supportive Care in Cancer·2026

Same author

Effectiveness of Music Therapy on Utilization Outcomes among Surgical Patients with Mental Health Conditions: A Propensity-Score Matched Cohort Study.

Journal of integrative and complementary medicine·2026

Same author

Impact of sociodemographic, clinical, and intervention characteristics on pain intensity within a single music therapy session.

The journal of pain·2025

Same author

Depression and Anxiety Symptoms in Adults Displaced by Natural Disasters.

JAMA network open·2025

Same author

Language impairment is associated with faster progression in progressive supranuclear palsy-Richardson syndrome.

Alzheimer's & dementia : the journal of the Alzheimer's Association·2025

Same journal

Comparing Adaptive Interventions under a General Sequential Multiple Assignment Randomized Trial Design via Multiple Comparisons with the Best.

Journal of statistical planning and inference·2026

Same journal

Variable Selection in Ultra-high Dimensional Feature Space for the Cox Model with Interval-Censored Data.

Journal of statistical planning and inference·2026

Same journal

On semi-supervised estimation using exponential tilt mixture models.

Journal of statistical planning and inference·2025

Same journal

Regression-Assisted Bayesian Record Linkage for Causal Inference in Observational Studies with Covariates Spread Over Two Files.

Journal of statistical planning and inference·2024

Same journal

Efficient inference of parent-of-origin effect using case-control mother-child genotype data.

Journal of statistical planning and inference·2024

Same journal

Distributed eQTL analysis with auxiliary information.

Journal of statistical planning and inference·2024

See all related articles

Search research articles

Related Experiment Video

Updated: May 27, 2026

Determination of Aggregate Surface Morphology at the Interfacial Transition Zone (ITZ)

Determination of Aggregate Surface Morphology at the Interfacial Transition Zone (ITZ)

Published on: December 16, 2019

Kernel Smoothing Density Estimation when Group Membership is Subject to Missing.

Wan Tang¹, Hua He, Douglas Gunzler

¹Department of Biostatistics and Computational Biology, University of Rochester Medical Center, 601 Elmwood Ave, Box 630, Rochester, NY 14642, U.S.A.

Journal of Statistical Planning and Inference

|November 26, 2011

Summary

This summary is machine-generated.

This study introduces inverse probability approaches to estimate density functions when subject membership is missing in two-stage diagnostic studies. These methods improve accuracy by addressing missing data, unlike simply ignoring it.

More Related Videos

Trajectory Data Analyses for Pedestrian Space-time Activity Study

Trajectory Data Analyses for Pedestrian Space-time Activity Study

Published on: February 25, 2013

Related Experiment Videos

Last Updated: May 27, 2026

Determination of Aggregate Surface Morphology at the Interfacial Transition Zone (ITZ)

Determination of Aggregate Surface Morphology at the Interfacial Transition Zone (ITZ)

Published on: December 16, 2019

Trajectory Data Analyses for Pedestrian Space-time Activity Study

Trajectory Data Analyses for Pedestrian Space-time Activity Study

Published on: February 25, 2013

Area of Science:

Statistics
Data Analysis
Biostatistics

Background:

Density function estimation is crucial in data analysis.
Nonparametric kernel smoothing is effective for complete data.
Missing membership data in two-stage designs complicates analysis.

Purpose of the Study:

To develop and evaluate methods for kernel smoothing density estimation with missing membership data.
To address limitations of ignoring subjects with unknown membership (valid only under Missing Completely At Random - MCAR).

Main Methods:

Utilized inverse probability approaches to handle missing membership data.
Applied kernel smoothing techniques for density function estimation.
Conducted simulation studies to assess method performance.
Validated approaches using real-world mental health study data.

Main Results:

Inverse probability methods effectively address missing membership data in density estimation.
Simulation studies demonstrated the utility and accuracy of the proposed approaches.
Real data analysis in mental health confirmed the practical applicability.

Conclusions:

The proposed inverse probability methods provide a robust solution for density estimation in the presence of missing membership data.
These techniques are valuable for complex study designs like two-stage diagnostics.
The methods offer improved accuracy compared to naive approaches that ignore missing data.