We present a new class-based prediction algorithm for time series. Given time series produced by different underlying generating processes, the algorithm predicts future time series values based on past time series values for each generator. Unlike many algorithms, this algorithm predicts a distribution over future values. This prediction forms the basis for labelling part of a time series with...

Density Estimation: Deals with the problem of estimating probability density functions (PDFs) based on some data sampled from the PDF. May use assumed forms of the distribution, parameterized in some way (parametric statistics); or May avoid making assumptions about the form of the PDF (nonparametric statistics). We are concerned more here with the non-parametric case (see Roger Barlow’s lectur...

OBJECTIVE To examine spatial-temporal distribution and risk of suicide, as well as trends in suicide mortality rates, in the indigenous and non-indigenous population of the state of Mato Grosso do Sul, Brazil. METHODS Data were obtained from the Information Department of the Brazilian Unified Health System. Deaths recorded as voluntary self-inflicted injuries (ICD-10 codes X60.0 to X84.9) wer...

In this paper we suggest a completely nonparametric test for the assessment of similar marginals of a multivariate distribution function. This test is based on the asymptotic normality of Mallows distance between marginals. It is also shown that the n out of n bootstrap is weakly consistent, thus providing a theoretical justification to the work in Czado & Munk [12]. The test is extended to cro...

How Likert type measurement scales should be appropriately used and analysed has been debated for over 50 years, often to the great confusion of students, practitioners, allied health researchers and educators. Basically, there are two major competing views that have evolved somewhat independently of one another and of the associated empirical research literature on this ‘great debate’. Most re...