163 research outputs found

    Exact Bayesian curve fitting and signal segmentation.

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    We consider regression models where the underlying functional relationship between the response and the explanatory variable is modeled as independent linear regressions on disjoint segments. We present an algorithm for perfect simulation from the posterior distribution of such a model, even allowing for an unknown number of segments and an unknown model order for the linear regressions within each segment. The algorithm is simple, can scale well to large data sets, and avoids the problem of diagnosing convergence that is present with Monte Carlo Markov Chain (MCMC) approaches to this problem. We demonstrate our algorithm on standard denoising problems, on a piecewise constant AR model, and on a speech segmentation problem

    The meaning of my feelings depends on who I am: work-related identifications shape emotion effects in organizations

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    Theory and research on affect in organizations has mostly approached emotions from a valence perspective, suggesting that positive emotions lead to positive outcomes and negative emotions to negative outcomes for organizations. We propose that cognition resulting from emotional experiences at work cannot be assumed based on emotion valence alone. Instead, building on appraisal theory and social identity theory, we propose that individual responses to discrete emotions in organizations are shaped by, and thus depend on, work-related identifications. We elaborate on this proposition specifically with respect to turnover intentions, theorizing how three discrete emotions - anger, guilt, and pride - differentially affect turnover intentions, depending on two work-related identifications - organizational and occupational identification. A longitudinal study involving 135 pilot instructors reporting emotions, work-related identifications, and turnover intentions over the course of one year provides general support for our proposition. Our theory and findings advance emotion and identity theories by explaining how the effects of emotions are dependent on the psychological context in which they are experienced

    Selective Responsiveness of Chronically Ill Children to Assessments of Depression

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    Many investigators have noted that depression is a common symptom among pediatric cancer patients. However, prevalence rates vary widely across studies This variation in prevalence rates may be due, in part, to selective reporting of patients based on measure, used and environmental cues. In this study, we evaluated 50 chronically ill pediatric patients (19 cancer and 31 diabetic patients) for their use of selective reporting of depression. Factors in the 2 x 2 design were Intervention (disclosure videotape and cartoon videotape) and Examiner (familiar examiner and unfamiliar examiner). In the intervention manipulation, subjects were shown either a videotape prompting the child that self-disclosure was appropriate or a tape of a cartoon (control condition). In the Examiner manipulation, subjects were administered the experimental measures by either a familiar (parent) or unfamiliar (research assistant) examiner. Dependent variables were the Children\u27s Depression inventory (CDI; Kovacs, 1981), the Depression scale of the Roberts Apperception Test for Children (RATC; McArthur & Roberts, 1982), and a depression measure taken from the Child Behavior Checktist (CBCL; Achenbach & Edelbrock, 1983). As hypothesized, the Examiner x intervention interaction revealed that children who did not view the disclosure videotape and who were tested by an unfamiliar examiner gave significantly lower self-reports of depression on the CDI than children in the other conditions. However, parent and child projective reports of depression did not vary as a function of experimental condition. The results are interpreted as selective responding on the part of pediatric patients. Limitations of assessing internal psychological states in children are discussed

    The Factor Analytic Structure of the Roberts Apperception Test for Children: A Comparison of the Standardization Sample with a Sample of Chronically Ill Children

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    A confirmatory principal component factor analysis of the Roberts Apperception Test for Children was conducted using the standardization sample and a sample of chronically ill children. An interpretation of three- and four-factor solutions identified the three-factor solution as superior to the four-factor solution as measured by chi-square goodness of fit and coefficients of convergence. A cluster analysis using Ward\u27s minimum variance method was calculated to determine the typical profiles that best describe the chronically ill sample. Results of this analysis reveal two distinct profiles that differ primarily on the level of adaptive psychological functioning
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