point estimation

This is not different from the practice of point estimation.

Data from all concentrations are included when using point estimation techniques.

When the word "estimator" is used without a qualifier, it usually refers to point estimation.

The classic wisdom-of-the-crowds finding involves point estimation of a continuous quantity.

Instead of, or in addition to, point estimation, one can do interval estimation, such as confidence intervals.

In performing the point estimation techniques recommended in this manual, an all-data approach is used.

This is similar to the restrictions in point estimation to ensure certain desired properties, such as unbiasedness, consistency, efficiency, etc.

The fourth theme, which covers 30-40% of the exam, involves statistical inference using point estimation, confidence intervals, and significance tests.

Stochastic models are not applied for making point estimation rather interval estimation and they use different stochastic processes.

Theory of Point Estimation by E.L. Lehmann and G. Casella.

In addition, point estimation or regression approaches would require the specification by biologists or toxicologists of some low level of adverse effect that would be deemed acceptable or safe.

NOTE: For the NPDES Permit Program, the point estimation techniques are the preferred statistical methods in calculating end points for effluent toxicity tests.

Commonly known as Generalized Inference, the new concepts include Generalized P-Values, Generalized Confidence Intervals, and Generalized Point Estimation.

Inman, H.F. and Bradley, E.L. (1989) The overlapping coefficient as a measure of agreement between probability distributions and point estimation of the overlap of two normal densities.

In statistics, interval estimation is the use of sample data to calculate an interval of possible (or probable) values of an unknown population parameter, in contrast to point estimation, which is a single number.

These approximations are only valid near, but not at, the end point, and so the method differs from end point estimations by way of first- and second-derivative plots, which require data at the end point.

Alternative point estimation approaches most probably would require the services of a statistician to determine the appropriateness of the model (goodness of fit), higher order linear or nonlinear models, confidence intervals for estimates generated by inverse regression, etc.

In general, point estimation should be contrasted with interval estimation: such interval estimates are typically either confidence intervals in the case of frequentist inference, or credible intervals in the case of Bayesian inference.

For example, one common approach, called parametric empirical Bayes point estimation, is to approximate the marginal using the maximum likelihood estimate (MLE), or a Moments expansion, which allows one to express the hyperparameters in terms of the empirical mean and variance.

However, a point estimation is likely to be incorrect, because the sample size - in this case, the number of candies that are visible - is too small a number to be sure that it does not contain anomalies that differ from the population as a whole.