Despite the growing interest in future-oriented cognition in various areas of psychology, there is still little empirical data regarding the occurrence and nature of future-oriented thoughts in daily life. In this study, participants recorded future-oriented thoughts occurring in natural settings and rated their characteristics and functions. The results show that future-oriented thoughts occur frequently in daily life and can take different representational formats (more or less abstract), embrace various thematic contents (e.g. work, relationships) and serve a range of functions (e.g. action planning, decision making). The functions and characteristics of thoughts differed according to their temporal distance, with thoughts referring to the near future being more specific and serving action planning to a greater extent than thoughts concerning the far future. The characteristics of future thoughts were also related to affective content, with positive thoughts being more frequent, more specific, and associated with more visual images than negative thoughts. Copyright © 2009 John Wiley & Sons, Ltd

The present longitudinal study traced the ideal self of 120 adolescents to the ideals that parents hold for themselves and for their children. Ideals were assessed using Q sorts for personality attributes and life goals. After permutation analysis was used to control for random similarity, moderate parent-child ideal-self similarity was evident. Three intermediate transmission steps accounted for this intergenerational similarity: (a) transfer of parents' ideal self to ideals for their children, (b) children's perception, and (c) acceptance of these parental ideals. The last 2 processes related to parenting practices, with parental warmth augmenting parent-child concordance and restrictiveness reducing it. Compared with boys' ideal self, that of girls was more similar to the ideals that parents hold for their children and for themselves. Analyses of patterns over time suggested a growth of ideal-self stability across adolescence.

A wavelet-based forecasting method for time series is introduced. It is based on a multiple resolution decomposition of the signal, using the redundant "à trous" wavelet transform which has the advantage of being shift-invariant.The result is a decomposition of the signal into a range of frequency scales. The prediction is based on a small number of coefficients on each of these scales. In its simplest form it is a linear prediction based on a wavelet transform of the signal. This method uses sparse modelling, but can be based on coefficients that are summaries or characteristics of large parts of the signal. The lower level of the decomposition can capture the long-range dependencies with only a few coefficients, while the higher levels capture the usual short-term dependencies.We show the convergence of the method towards the optimal prediction in the autoregressive case. The method works well, as shown in simulation studies, and studies involving financial data.

To assess the quality of the fit in a multiple linear regression, the coefficient of determination or R 2 is a very simple tool, yet the most used by practitioners. Indeed, it is reported in most statistical analyzes, and although it is not recommended as a final model selection tool, it provides an indication of the suitability of the chosen explanatory variables in predicting the response. In the classical setting, it is well known that the least-squares fit and coefficient of determination can be arbitrary and/or misleading in the presence of a single outlier. In many applied settings, the assumption of normality of the errors and the absence of outliers are difficult to establish. In these cases, robust procedures for estimation and inference in linear regression are available and provide a suitable alternative.In this paper we present a companion robust coefficient of determination that has several desirable properties not shared by others. It is robust to deviations from the specified regression model (like the presence of outliers), it is efficient if the errors are normally distributed, it does not make any assumption on the distribution of the explanatory variables (and therefore no assumption on the unconditional distribution of the responses). We also show that it is a consistent estimator of the population coefficient of determination. A simulation study and two real datasets support the appropriateness of this estimator, compared with classical (leastsquares) and several previously proposed robust R 2 , even for small sample sizes.

Recent methodological researches produced permutation methods to test parameters in presence of nuisance variables in linear models or repeated measures ANOVA. Permutation tests are also particularly useful to overcome the multiple comparisons problem as they are used to test the effect of factors or variables on signals while controlling the family-wise error rate (FWER). This article introduces the permuco package which implements several permutation methods. They can all be used jointly with multiple comparisons procedures like the cluster-mass tests or threshold-free cluster enhancement (TFCE). The permuco package is designed, first, for univariate permutation tests with nuisance variables, like regression and ANOVA; and secondly, for comparing signals as required, for example, for the analysis of event-related potential (ERP) of experiments using electroencephalography (EEG). This article describes the permutation methods and the multiple comparisons procedures implemented. A tutorial for each of theses cases is provided.