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We propose using the integrated periodogram to classify time series. The method assigns a new time series to the group that minimizes the distance between the series integrated periodogram and the group mean of integrated periodograms. Local computation of these periodograms allows the application of this approach to nonstationary time series. Since the integrated periodograms are curves, we apply functional data depth-based techniques to make the classification robust, which is a clear advantage over other competitive procedures. The method provides small error rates for both simulated and real data. It improves existing approaches and presents good computational behavior.

Informal care is today the form of support most commonly used by those who need other people in order to carry out certain activities that are considered basic (eating, dressing, taking a shower, etc.), in Spain and in most other countries in the region. The possible labour opportunity costs incurred by these informal carers, the vast majority of whom are middle-aged women, have not as yet been properly quantified in Spain. It is, however, crucially important to know these quantities at a time when public authorities appear to be determined to extend the coverage offered up to now as regards long-term care.In this context, we use the Spanish subsample of the European Community Household Panel (1994)(1995)(1996)(1997)(1998)(1999)(2000)(2001) to estimate a dynamic ordered probit and so attempt to examine the effects of various types of informal care on labour behaviour. The results obtained indicate the existence of labour opportunity costs for those women who live with the dependent person they care for, but not for those who care for someone outside the household. Furthermore, whereas caregiving for more than a year has negative effects on labour force participation, the same cannot be said of those who "start caregiving" and "stop caregiving". JEL codes: J14, J2, I10

Abstract:The analysis of expression and CGH arrays plays a central role in the study of complex diseases, especially cancer, including fi nding markers for early diagnosis and prognosis, choosing an optimal therapy, or increasing our understanding of cancer development and metastasis. Asterias (http://www.asterias.info) is an integrated collection of freely-accessible web tools for the analysis of gene expression and aCGH data. Most of the tools use parallel computing (via MPI) and run on a server with 60 CPUs for computation; compared to a desktop or server-based but not parallelized application, parallelization provides speed ups of factors up to 50. Most of our applications allow the user to obtain additional information for user-selected genes (chromosomal location, PubMed ids, Gene Ontology terms, etc.) by using clickable links in tables and/or fi gures. Our tools include: normalization of expression and aCGH data (DNMAD); converting between different types of gene/clone and protein identifi ers (IDconverter/IDClight); fi ltering and imputation (preP); fi nding differentially expressed genes related to patient class and survival data (Pomelo II); searching for models of class prediction (Tnasas); using random forests to search for minimal models for class prediction or for large subsets of genes with predictive capacity (GeneSrF); searching for molecular signatures and predictive genes with survival data (SignS); detecting regions of genomic DNA gain or loss (ADaCGH). The capability to send results between different applications, access to additional functional information, and parallelized computation make our suite unique and exploit features only available to web-based applications.