<p>We propose nonparametric Bayesian models for supervised dimension</p><p>reduction and regression problems. Supervised dimension reduction is</p><p>a setting where one needs to reduce the dimensionality of the</p><p>predictors or find the dimension reduction subspace and lose little</p><p>or no predictive information. Our first method retrieves the</p><p>dimension reduction subspace in the inverse regression framework by</p><p>utilizing a dependent Dirichlet process that allows for natural</p><p>clustering for the data in terms of both the response and predictor</p><p>variables. Our second method is based on ideas from the gradient</p><p>learning framework and retrieves the dimension reduction subspace</p><p>through coherent nonparametric Bayesian kernel models. We also</p><p>discuss and provide a new rationalization of kernel regression based</p><p>on nonparametric Bayesian models allowing for direct and formal</p><p>inference on the uncertain regression functions. Our proposed models</p><p>apply for high dimensional cases where the number of variables far</p><p>exceed the sample size, and hold for both the classical setting of</p><p>Euclidean subspaces and the Riemannian setting where the marginal</p><p>distribution is concentrated on a manifold. Our Bayesian perspective</p><p>adds appropriate probabilistic and statistical frameworks that allow</p><p>for rich inference such as uncertainty estimation which is important</p><p>for measuring the estimates. Formal probabilistic models with</p><p>likelihoods and priors are given and efficient posterior sampling</p><p>can be obtained by Markov chain Monte Carlo methodologies,</p><p>particularly Gibbs sampling schemes. For the supervised dimension</p><p>reduction as the posterior draws are linear subspaces which are</p><p>points on a Grassmann manifold, we do the posterior inference with</p><p>respect to geodesics on the Grassmannian. The utility of our</p><p>approaches is illustrated on simulated and real examples.</p> / Dissertation

Thesis (Ph. D.)--Rutgers University, 2009. / "Graduate Program in Biomedical Engineering." Includes bibliographical references (p. 54-55).

Thesis (M. Phil.)--Hong Kong University of Science and Technology, 2003. / Includes bibliographical references (leaves 46-49). Also available in electronic version. Access restricted to campus users.

This thesis explores the temporal analysis of text using the implicit temporal cues present in document. We consider the case when all explicit temporal expressions such as specific dates or years are removed from the text and a bag of words based approach is used for timestamp prediction for the text. A set of gold standard text documents with times- tamps are used as the training set. We also predict time spans for Wikipedia biographies based on their text. We have training texts from 3800 BC to present day. We partition this timeline into equal sized chronons and build a probability histogram for a test document over this chronon sequence. The document is assigned to the chronon with the highest probability. We use 2 approaches: 1) a generative language model with Bayesian priors, and 2) a KL divergence based model. To counter the sparsity in the documents and chronons we use 3 different smoothing techniques across models. We use 3 diverse datasets to test our mod- els: 1) Wikipedia Biographies, 2) Guttenberg Short Stories, and 3) Wikipedia Years dataset. Our models are trained on a subset of Wikipedia biographies. We concentrate on two prediction tasks: 1) time-stamp prediction for a generic text or mid-span prediction for a Wikipedia biography , and 2) life-span prediction for a Wikipedia biography. We achieve an f-score of 81.1% for life-span prediction task and a mean error of around 36 years for mid-span prediction for biographies from present day to 3800 BC. The best model gives a mean error of 18 years for publication date prediction for short stories that are uniformly distributed in the range 1700 AD to 2010 AD. Our models exploit the temporal distribu- tion of text for associating time. Our error analysis reveals interesting properties about the models and datasets used. We try to combine explicit temporal cues extracted from the document with its implicit cues and obtain combined prediction model. We show that a combination of the date-based predictions and language model divergence predictions is highly effective for this task: our best model obtains an f-score of 81.1% and the median error between actual and predicted life span midpoints is 6 years. This would be one of the emphasis for our future work. The above analyses demonstrates that there are strong temporal cues within texts that can be exploited statistically for temporal predictions. We also create good benchmark datasets along the way for the research community to further explore this problem. / text

The purpose of this study was to investigate physical activity (PA) engagement among older adults (OA) following discharge from a medically supervised group exercise program and to explore the facilitators and barriers that influenced maintained PA engagement. While facilitators and barriers to PA among OA in general have been well documented, facilitators and barriers particular to maintaining PA after discharge from a supervised exercise program have not been widely explored with qualitative methods or a mixed method design. Data for this mixed method, case study approach were collected in two phases. In Phase I, questionnaires were used to investigate PA engagement as well as semi-structured qualitative interviews were completed (n = 12; Mage = 80.0 years) to explore facilitators and barriers that influenced PA engagement. In Phase II, reviews of medical charts were conducted retrospectively to gather further information on PA engagement and barriers (n = 12). All 12 individuals in Phase I remained engaged in PA activity 2 – 48 months post completion of the medically supervised exercise program. This particular group of OA identified facilitators for and barriers to maintaining PA that were personally-, socially-, and program-based, and also provided suggestions to alleviate cited barriers. Four themes identified in regards to PA engagement were: (1) Personal drive: highly aware of the need to keep moving; (2) Social connections and support: we all need people; (3) Program components matter; and, (4) Convenient, Affordable, Relevant: suggestions to improve program access. The power of multi-level, multi-sector approaches that consider the broader determinants of health was highlighted in this study. Participants identified the need for health care providers (HCP) and PA instructors to continue to communicate the benefits of PA, the importance of ongoing HCP support, and the necessity of working across sectors to reduce program related barriers to promote PA engagement among OA discharged from a medically supervised exercise program. / Graduate / melodyburgoyne@hotmail.com

Using a social constructionist perspective, this thesis explores the development of the concepts of “parental alienation syndrome” and “false allegations” in the context of custody and access, as ‘social problems’. Following Joel Best’s framework for critically analysing social problems, it examines the life course of these concepts through an historical account of Canada’s divorce arena and recent changes to custody and access law. It analyzes the reasoning and motives of the major claimsmakers: the Fathers’ Right Movement, medical experts, the legal arena and the counter-claims of Feminist activists. It examines the role of the supervised access facilitator in the construction of the concepts as ‘social problems’. The theories of psychiatrist Richard Gardner are examined in particular, due to their pivotal role in the advancement of the claimsmakers’ goals. Finally, empirical studies are reviewed and analyzed, demonstrating how the concepts of “parental alienation syndrome” and “false allegations” have mutated and permeated the domain of divorce and access in Western society. / Thesis (Master, Sociology) -- Queen's University, 2008-09-04 11:36:28.395

Thesis (Ph. D.)--University of Washington, 2007. / Vita. Includes bibliographical references (p. 120-127).

Thesis (M.S.)--Oregon State University, 2009. / Printout. Includes bibliographical references (leaves 38-40). Also available on the World Wide Web.

Thesis (Ph.D)--Electrical and Computer Engineering, Georgia Institute of Technology, 2010. / Committee Chair: Hu, Xiaoping; Committee Co-Chair: Vachtsevanos, George; Committee Member: Butera, Robert; Committee Member: Gurbaxani, Brian; Committee Member: Mayberg, Helen; Committee Member: Yezzi, Anthony. Part of the SMARTech Electronic Thesis and Dissertation Collection.

Thesis (Ph. D.)--West Virginia University, 2005. / Title from document title page. Document formatted into pages; contains x, 97 p. : ill. (some col.). Includes abstract. Includes bibliographical references (p. 81-83).