700 research outputs found

    DS 636: Data Analytics with R Programming

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    DS 644: Introduction to Big Data

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    Computation of risk measures in finance and parallel real-time scheduling

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    Many application areas employ various risk measures, such as a quantile, to assess risks. For example, in finance, risk managers employ a quantile to help determine appropriate levels of capital needed to be able to absorb (with high probability) large unexpected losses in credit portfolios comprising loans, bonds, and other financial instruments subject to default. This dissertation discusses the computation of risk measures in finance and parallel real-time scheduling. Firstly, two estimation approaches are compared for one risk measure, a quantile, via randomized quasi-Monte Carlo (RQMC) in an asymptotic setting where the number of randomizations for RQMC grows large, but the size of the low-discrepancy point set remains fixed. In the first method, for each randomization, it computes an estimator of the cumulative distribution function (CDF), which is inverted to obtain a quantile estimator, and the overall quantile estimator is the sample average of the quantile estimators across randomizations. The second approach instead computes a single quantile estimator by inverting one CDF estimator across all randomizations. Because quantile estimators are generally biased, the first method leads to an estimator that does not converge to the true quantile as the number of randomizations goes to infinity. In contrast, the second estimator does, and a central limit theorem is established for it. To get an improvement, we use conditional Monte Carlo (CMC) to obtain a smoother estimate of the distribution function, and we combine this with the second RQMC to further reduce the variance. The result is a much more accurate quantile estimator, whose mean square error can converge even faster than the canonical rate of O(1/n). Secondly, another risk measure is estimated, namely economic capital (EC), which is defined as the difference between a quantile and the mean of the loss distribution, given a stochastic model for a portfolio’s loss over a given time horizon. This work applies measure-specific importance sampling to separately estimate the two components of the EC, which can lead to a much smaller variance than when estimating both terms simultaneously. Finally, for parallel real-time tasks, the federated scheduling paradigm, which assigns each parallel task a set of dedicated cores, achieves good theoretical bounds by ensuring exclusive use of processing resources to reduce interferences. However, because cores share the last-level cache and memory bandwidth resources, in practice tasks may still interfere with each other despite executing on dedicated cores. To tackle this issue, this work presents a holistic resource allocation framework for parallel real-time tasks under federated scheduling. Under the proposed framework, in addition to dedicated cores, each parallel task is also assigned with dedicated cache and memory bandwidth resources. This work also shows the study of the characteristics of parallel tasks upon different resource allocations following a measurement-based approach and proposes a technique to handle the challenge of tremendous profiling for all resource allocation combinations under this approach. Further, it proposes a holistic resource allocation algorithm that well balances the allocation between different resources to achieve good schedulability. Additionally, this work provides a full implementation of the framework by extending the federated scheduling system with Intel’s Cache Allocation Technology and MemGuard. It also demonstrates the practicality of the proposed framework via extensive numerical evaluations and empirical experiments using real benchmark programs. In the end, the discussion about the application of risk measures for real-time scheduling is given for future work

    A Study on the Low Kindergarten Enrollment Rate in the Nu Nationality Inhabited Areas: An Indigenous Case Study Based on the “Cultural - Ecological Theory”

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    Since the traditional economic factors cannot fully explain the low kindergarten enrollment in the Nu nationality inhabited area, this paper rebuilds an analysis framework for the low kindergarten enrollment rate based on the “cultural-ecological” theory. With the in-depth investigation on the social culture of the Nu nationality inhabited area and the low kindergarten enrollment rate, from the macro, middle and micro leve

    Embedded Multilevel Regression and Poststratification: Model-based Inference with Incomplete Auxiliary Information

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    Health disparity research often evaluates health outcomes across demographic subgroups. Multilevel regression and poststratification (MRP) is a popular approach for small subgroup estimation due to its ability to stabilize estimates by fitting multilevel models and to adjust for selection bias by poststratifying on auxiliary variables, which are population characteristics predictive of the analytic outcome. However, the granularity and quality of the estimates produced by MRP are limited by the availability of the auxiliary variables' joint distribution; data analysts often only have access to the marginal distributions. To overcome this limitation, we embed the estimation of population cell counts needed for poststratification into the MRP workflow: embedded MRP (EMRP). Under EMRP, we generate synthetic populations of the auxiliary variables before implementing MRP. All sources of estimation uncertainty are propagated with a fully Bayesian framework. Through simulation studies, we compare different methods and demonstrate EMRP's improvements over alternatives on the bias-variance tradeoff to yield valid subpopulation inferences of interest. As an illustration, we apply EMRP to the Longitudinal Survey of Wellbeing and estimate food insecurity prevalence among vulnerable groups in New York City. We find that all EMRP estimators can correct for the bias in classical MRP while maintaining lower standard errors and narrower confidence intervals than directly imputing with the WFPBB and design-based estimates. Performances from the EMRP estimators do not differ substantially from each other, though we would generally recommend the WFPBB-MRP for its consistently high coverage rates

    Automated and High Speed Machine Design for Telecommunication Products

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    This research focuses on an automated and high speed machine design, which is assembling the bronze wire inside the plastic block, according to the manufacturing and production specification. In this design project, automated system performs special kind of operation in assembly line and it divided in five different stations. The function at first station is wire loading, feeding and straightening the wire. The function in second station is uploading the plastic block into the assembly line and holding the block precisely for inserting the bronze wire. The third station include of wire inserting into the plastic block and cut wire at desire length. The fourth station takes care of bending wire and final station take care of inspection of final assembly product by using appropriate sensors techniques. This project is mainly focus on utilization of automated machinery with simple tooling and fixture with low cost, which properly inspected at each station and easier to maintain in working at "high speed" repeatedly. It also explains major design problems and understands of engineering technology and limitations

    Three Essays on Economic Evaluation of Health Intervention Programs and Health Policy

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    This dissertation is mainly focusing on an economic evaluation of a childhood obesity intervention program, after school physical activities and a nationwide social health care program. The analysis is conducted within three main essays. The purpose of the first essay is to estimate peer effects on third grade students’ BMI and to investigate the social and physiological explanations for such effects. The BMI of students from a childhood obesity intervention program (N=573) is used to assess peer effects on students’ BMI by identification of endogenous social effects. We apply IV regression to account for this endogenous effects. Strong peer effects are found for the overall sample, females and males (p<.1). However, when classifying students into improvement versus non-improvement groups, the peer effect is only found among females categorized in the improvement group (B=1.472) and males in the non-improvement group (B= 1.176). Thus in general, peer effects are found for students aged 8-11, with sex differences in the psychological and social behavioral motivations. In the second essay, we exploit the data from the Trends in International Mathematics and Science Study (TIMSS) 2011 to evaluate the effect of playing after school on academic performance by using a propensity score matching approach. We highlight that in addition to intrinsic characteristics of students, the extent to which after school activities affect academic performance depends on extrinsic factors such as parental involvement. In order to capture the heterogeneous effects of playing after school, we analyze the effect by separating the overall sample according to whether parents check their children’s homework and set specific times for after school homework. We further uncover heterogeneous effects of playing after school for different levels of parental involvement and supervision. The results show that playing after school significantly increases math and science test scores of students by 7.9 points and 4.2 points respectively. Moreover, this positive effect is stronger among students with greater parental involvement and supervision, but weaker or nonexistent among students with less parental involvement and supervision. The third essay fills the gap in the literature by examining the long-term causal effects of Medicaid enrollment on high school and college completion through a regression discontinuity design that exploits an eligibility discontinuity created by the Medicaid expansion of 1990. Using the American Community Survey data, we present evidence that Medicaid enrollment decreased high school completion rates by 3.6 percentage points (using local linear regression and IK bandwidth selector). However, we find little evidence of adverse impact of Medicaid on college completion. We also find heterogeneous effects by race/ethnicity. While Medicaid has no significant impact on educational achievement of blacks or Asian, Hispanics are negatively affected by Medicaid on both high school and college completion
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