Penalization and shrinkage methods produced unreliable clinical prediction models especially when sample size was small

Shrinkage estimation of dispersion in Negative Binomial models for RNA-seq experiments with small sample size

MOTIVATION RNA-seq experiments produce digital counts of reads that are affected by both biological and technical variation. To distinguish the systematic changes in expression between conditions from noise, the counts are frequently modeled by the Negative Binomial distribution. However, in experiments with small sample size, the per-gene estimates of the dispersion parameter are unreliable. ...

Evaluation and selection of models for out-of-sample prediction when the sample size is small relative to the complexity of the data-generating process

In regression with random design, we study the problem of selecting a model that performs well for out-of-sample prediction. We do not assume that any of the candidate models under consideration is correct. Our analysis is based on explicit finite-sample results. Our main findings differ from that of other analyses that are based on traditional large-sample limit approximations, because we cons...

Sample size in clinical research: when is enough enough?

Cross-validation and bootstrapping are unreliable in small sample classification

0167-8655/$ see front matter 2008 Elsevier B.V. A doi:10.1016/j.patrec.2008.06.018 * Corresponding authors. Address: Department o University, Academic Hospital, SE-751 85 Uppasala, Sw fax: +46 18 611 37 03. E-mail addresses: [email protected] (A angstrom.uu.se (M.G. Gustafsson). The interest in statistical classification for critical applications such as diagnoses of patient samples ...

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.