Estimation Methods
Estimation Methods - Related Videos
05:35
A Point-of-Care Method with Integrated Decision Support Tool to Estimate Anemia at Population Level
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Cited by 3 •
2024
An accurate hemoglobin estimation method is lacking at the point of care and may hinder population-based programs for treating anemia. Therefore, we developed a point-of-care method based on pooled capillary blood and an auto-analyzer integrated into a custom software application to categorize the hemoglobin values into different grades of anemia.
08:15
Estimation of Nephron Number in Whole Kidney using the Acid Maceration Method
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Cited by 16 •
2019
Estimates of whole kidney nephron number are important clinically and experimentally, as there is an inverse association between nephron number and an enhanced risk of renal and cardiovascular disease.Herein, the use of the acid maceration method, which provides fast and reliable estimates of whole kidney nephron number, is demonstrated.
00:09
Leaf Area Index Estimation Using Three Distinct Methods in Pure Deciduous Stands
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Cited by 13 •
2019
An accurate estimation of leaf area index (LAI) is crucial for many models of material and energy fluxes within plant ecosystems and between an ecosystem and the atmospheric boundary layer. Therefore, three methods (litter traps, needle technique, and PCA) for taking precise LAI measurements were in the presented protocol.
01:06
What are Estimates?
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2023
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,...
07:05
The Precision of Visual Working Memory with Delayed Estimation
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2023
Source: Laboratory of Jonathan Flombaum—Johns Hopkins University Human memory is limited. Throughout most of its history, experimental psychology has focused on investigating the discrete, quantitative limits of memory—how many individual pieces of information a person can remember. Recently, experimental psychologists have also become interested in more qualitative limits—how precisely is information stored? The concept of memory precision can be both intuitive and elusive at once. It is...
FAQs
Estimator quality is multidimensional. Bias, consistency, and efficiency are distinct performance properties, while uncertainty describes how much qualification accompanies an estimate. Considering these criteria prevents evaluation based only on the calculated value. This matters when selecting or comparing estimation methods for population means, proportions, or regression coefficients.
A point estimate communicates one selected value, whereas interval estimation communicates a range with an associated confidence level. The choice affects how results are reported: a point estimate emphasizes a concise summary, while an interval makes uncertainty part of the result. Both formats can describe unknown population characteristics and support later statistical decisions.
Maximum likelihood, method of moments, and Bayesian estimation are alternative approaches within the broader task of estimating unknown characteristics. They should not be selected by name alone. The resulting estimator must also be considered through bias, consistency, efficiency, and uncertainty, keeping the choice connected to the quality of the inference produced.
A typical workflow begins with observed sample data and an unknown population characteristic to be estimated. The analyst calculates an estimator, evaluates its performance using properties such as bias, consistency, and efficiency, and then reports either a point estimate or an interval with a confidence level. This sequence connects calculation with interpretation.
Estimation methods provide quantities that can be used beyond reporting a parameter value. The resulting estimates support prediction and hypothesis testing, linking sample-based calculations to broader statistical inference. They can also contribute to evidence-based decisions, because the estimate and its uncertainty provide information for judging population characteristics and drawing conclusions from data.
Estimation methods are relevant wherever researchers infer population characteristics from sample data. The overview identifies applications in biology, economics, engineering, and public health, as well as uses involving parameter estimation, prediction, hypothesis testing, and evidence-based decisions. Their broad value comes from connecting observed data with conclusions about unknown quantities.