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Belle Movement Group

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Gregory Avdeev
Gregory Avdeev


From weird dream fragments to unsettling nightmares, our associations to each dream can tell us a little bit more about our unconscious. Tapping into these deeper meanings can help us explore who we are and help us navigate from unsettledness in the dream back to security. Co-hosts Dr. Ann Kelley and Sue Marriott discuss the power of association in dreams and connect those to our inner working models. For shownotes to join our ad-free feed


Aggression is not a feeling, it can be positive or negative goal-directed life energy. Learn to use this energy to propel us forward in conversation with guest expert Jeanne Bunker and co-host, Sue Marriott. What is healthy aggression? It can be a resource to help us navigate our personal goals, increase intimacy within our relationships, and to act as a catalyst for change when paired with desire. Follow along as Jeanne Bunker and Sue Marriott break down the negative connotations and provide perspective to harness this resource and help reclaim healthy aggression. Shownotes at Join our Neuronerd community at

Learn more about autobiographical memory and the hippocampus in managing stress. By blending neuroscience and a 3 R Spiral of change strategy, we can begin to sort through our defenses, rewire how we navigate our world, and move towards healing our relationships. Shownotes at Join our Neuronerd private community for an ad-free feed at

Ann and Sue get into the nitty gritty of exploring normal human defenses. Using the full body of work from translated relational neuroscience, modern attachment, and depth psychology they break down their professional and personal experience moving from armor back to more connected relationality. Show notes at and more episodes at

Economists and other social scientists often face situations where they have access to two datasets that they can use but one set of data suffers from censoring or truncation. If the censored sample is much bigger than the uncensored sample, it is common for researchers to use the censored sample alone and attempt to deal with the problem of partial observation in some manner. Alternatively, they simply use only the uncensored sample and ignore the censored one so as to avoid biases. It is rarely the case that researchers use both datasets together, mainly because they lack guidance about how to combine them. In this paper, we develop a tractable semiparametric framework for combining the censored and uncensored datasets so that the resulting estimators are consistent, asymptotically normal, and use all information optimally. When the censored sample, which we refer to as the master sample, is much bigger than the uncensored sample (which we call the refreshment sample), the latter can be thought of as providing identification where it is otherwise absent. In contrast, when the refreshment sample is large and could typically be used alone, our methodology can be interpreted as using information from the censored sample to increase effciency. To illustrate our results in an empirical setting, we show how to estimate the effect of changes in compulsory schooling laws on age at first marriage, a variable that is censored for younger individuals. We also demonstrate how refreshment samples for this application can be created by matching cohort information across census datasets. 041b061a72


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