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    Regularized estimation of linear functionals of precision matrices for high-dimensional time series

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    This paper studies a Dantzig-selector type regularized estimator for linear functionals of high-dimensional linear processes. Explicit rates of convergence of the proposed estimator are obtained and they cover the broad regime from i.i.d. samples to long-range dependent time series and from sub-Gaussian innovations to those with mild polynomial moments. It is shown that the convergence rates depend on the degree of temporal dependence and the moment conditions of the underlying linear processes. The Dantzig-selector estimator is applied to the sparse Markowitz portfolio allocation and the optimal linear prediction for time series, in which the ratio consistency when compared with an oracle estimator is established. The effect of dependence and innovation moment conditions is further illustrated in the simulation study. Finally, the regularized estimator is applied to classify the cognitive states on a real fMRI dataset and to portfolio optimization on a financial dataset.Comment: 44 pages, 4 figure

    LINKING ENVIRONMENTAL EXPOSURES AND HEALTH EFFECTS: USING EXISTING DATA TO EXPLORE THE RELATIONSHIPS BETWEEN ENVIRONMENT AND CHRONIC DISEASES

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    The environment plays an important role in the health of communities. However, few health systems exist at the state and/or local levels to efficiently track the potential health effects associated with environmental exposure. The objectives of this dissertation are 1) to use secondary data for assessing the possible associations between health outcomes and environmental exposure and/or hazard; 2) to explore possible methods of data linkage and analyses which can be used by state and local environmental health tracking agencies and 3) to bring positive contributions to the development of national Environmental Public Health Tracking Network (EPHT). In this project, the Three Mile Island (TMI) cohort data (1979-1995) and Pennsylvania (PA) Cancer registry data were used to evaluate the associations between cigarette smoking and adult leukemia. A case-crossover analysis was performed with PA cardiopulmonary hospital admission data and local air pollution data to assess the health effects of air pollutants on cardiopulmonary disease before and after the elimination of a major point source of air pollution. A case-control study was also conducted to examine the associations between term low birth weight and particulate air pollution. The results showed that cigarette smoking could increase the risk of acute myeloid leukemia (AML). In addition, particulate air pollution is significantly associated with cardiovascular hospitalization and low birth weight in term infant. In conclusion, the findings suggest that environmental hazards have adverse health effects on a number of health endpoints. Secondary data can be a great resource for environmental public health tracking, which is of public health relevance. The use of existing data is an effective way to assess the potential health effects associated with environmental exposures after an appropriate study design with a feasible data linkage and correct methods of data analyses was developed
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