Small size sampling

Small sample size generalization

The generalization of linear classifiers is considered for training sample sizes smaller than the feature size. It is shown that there exists a good linear classifier, that is better than the Nearest Mean classifier for sample sizes for which Fisher’s linear discriminant cannot be used. The use and performance of this small sample size classifier is illustrated by some examples.

Sampling and Sample Size Calculation Authors

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Centre Sampling for Estimating Elusive Population Size

The estimation of the size of an elusive population is a problem frequently addressed in many fields of applications. In the paper a sampling strategy for the estimation of the size, named centre sampling, is proposed, and its properties are evaluated in a design-based approach. The estimator is admissible, consistent and the design is measurable. The expressions of the variance of the populati...

Sample size calculation and sampling techniques.

The sample size required in importance sampling

The goal of importance sampling is to estimate the expected value of a given function with respect to a probability measure ν using a random sample of size n drawn from a different probability measure μ. If the two measures μ and ν are nearly singular with respect to each other, which is often the case in practice, the sample size required for accurate estimation is large. In this article it is...