Behavioural studies are commonly plagued with data that violate the assumptions of parametric statistics. Consequently, classic nonparametric methods (e.g. rank tests) and novel distribution-free methods (e.g. randomization tests) have been used to a great extent by behaviourists. However, the robustness of such methods in terms of statistical power and type I error have seldom been evaluated. ...

BACKGROUND We introduce a new criterion, the percentile inclusion probability, for comparing methods for calculating reference intervals. The criterion is compared with a previously published measure of reliability suggested by Linnet (Linnet K. Clin Chem 1987;33:381-6), the ratio of the width of the confidence interval for the percentile to that of the reference interval. METHODS Data were s...

Density estimation is a core operation of virtually all probabilistic learning methods (as opposed to discriminative methods). Approaches to density estimation can be divided into two principal classes, parametric methods, such as Bayesian networks, and nonparametric methods such as kernel density estimation and smoothing splines. While neither choice should be universally preferred for all sit...

Abstract This article presents basic ideas of finite mixture models in which the number components is known and distributions comprising are not assumed to come from any parametrically specified family. categorized under: Algorithms Computational Methods > Statistical Learning Exploratory Data Sciences Clustering Classification Graphical Analysis Nonparametric Models

Time series prediction covers a vast field of every-day statistical applications in medical, environmental and economic domains. In this paper we develop nonparametric prediction strategies based on the combination of a set of “experts” and show the universal consistency of these strategies under a minimum of conditions. We perform an indepth analysis of real-world data sets and show that these...