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Open Access Publications from the University of California

Scholarly Works (120 results)

  • Article
  • Peer Reviewed
UCLA Previously Published Works (2018)
    Cover page: Impact of the Cotton–Mouton effect on Faraday polarimetry measurements using circular polarization
    • Article
    • Peer Reviewed
    UCLA Previously Published Works (2011)
    Immigration status is a likely deterrent of mental health care utilization in the United States. Using the Medical Expenditure Panel Survey and National Health Interview survey from 2002 to 2006, multivariable logistic regressions were used to estimate the effects of immigration status on mental health care utilization among patients with depression or anxiety disorders. Multivariate regressions showed that immigrants were significantly less likely to take any prescription drugs, but not significantly less likely to have any physician visits compared to US-born citizens. Results also showed that improving immigrants' health care access and health insurance coverage could potentially reduce disparities between US-born citizens and immigrants by 14-29% and 9-28% respectively. Policy makers should focus on expanding the availability of regular sources of health care and immigrant health coverage to reduce disparities on mental health care utilization. Targeted interventions should also focus on addressing immigrants' language barriers, and providing culturally appropriate services.
      Cover page: Estimating the Effects of Immigration Status on Mental Health Care Utilizations in the United States
      • Article
      • Peer Reviewed
      UCLA Previously Published Works (2014)

      Objective

      We study the association between the timing of the Great Recession (GR) and health spending among uninsured adults distinguishing by citizenship/nativity status and time of U.S. residence.

      Data source

      Uninsured U.S. citizens and noncitizens from the 2005-2006 and 2008-2009 Medical Expenditure Panel Survey.

      Study design

      The probability of reporting any health spending and the natural logarithm of health spending are our main dependent variables. We compare health spending across population categories before/during the GR. Subsequently, we implement two-part regression analyses of total and specific health-spending measures. We predict average health spending before/during the GR with a smearing estimation.

      Principal findings

      The probability of reporting any spending diminished for recent immigrants compared to citizens during the GR. For those with any spending, recent immigrants reported higher spending during the GR (27 percent). Average reductions in total spending were driven by the decline in the share of the population reporting any spending among citizens and noncitizens.

      Conclusions

      Our study findings suggest that recent immigrants could be forgoing essential care, which later translates into higher spending. It portrays the vulnerability of a population that would remain exposed to income shocks, even after the Affordable Care Act (ACA) implementation.
        Cover page: The Great Recession and Health Spending among Uninsured U.S. Immigrants: Implications for the Affordable Care Act Implementation
        • Thesis
        • Peer Reviewed
        UC San Diego Electronic Theses and Dissertations (2018)

        The main aim of this dissertation is to study the prediction of financial returns or squared financial returns. As is known, financial returns data have the distribution with fatter tails than the normal and often show significant correlation and the phenomenon of volatility clustering. To capture these features is the most challenging thing in modeling financial returns data. The most popular nonlinear time series models for financial returns data now are ARCH (Autoregressive Conditional Heteroscedasticity) and GARCH (Generalized Autoregressive Conditional Heteroscedasticity) models. Early this century, a Model-free prediction approach was also derived to understand this complex type of data. One application of the model-free approach, NoVaS (Normalizing and Variance-Stabilizing) transformation has been proved to outperform ARCH and GARCH models under stationary financial data. In Chapter 1, we extend the realm of application of NoVaS to non-stationary data and compare the performance with GARCH in the one-step point prediction and prediction intervals of squared financial returns. In addition, we show the applicability of NoVaS transformation for estimating realized volatility. A new approach to the multi-step ahead prediction of squared financial returns is defined and analyzed in Chapter 2. Our work on linear time series model such as Autoregression are shown in the last two chapters. Chapter 3 describes in detail the situations that a simplified autoregressive models should be considered and the theoretical support was also given there for further study. In the last Chapter, we construct the prediction intervals of regression with model selections in the bootstrap world, which give better performance than the standard bootstrap methods. All the work here are mainly focusing on financial returns time series.

          Cover page: Prediction in Time Series Models and Model-free Inference with a Specialization in Financial Return Data
          • Thesis
          • Peer Reviewed
          UC Irvine Electronic Theses and Dissertations (2015)

          With the advancement of signal processing and other enabling technologies, new products and services have appeared in large numbers over the past decade, and are changing people's daily lives quickly and profoundly. They could not have occurred without the rapid development in areas like digital communications, information theory, detection and estimation, where resource allocation plays a crucial role.

          In this work, we study resource allocation for two classes of problems. The first class is rate allocation in digital systems. The functionality of modern digital systems can be broadly divided into two parts: communications and source coding. For communications, we systematically study the allocation problem from a game theory perspective for the multiuser downlink broadcast channel, and apply the solutions to the special case where spatial block diagonalization is combined with time-sharing to multiplex a subset of the users. For source coding, we consider the achievable sum-rate/distortion tradeoff for the Gaussian central estimation officer problem with a scalar source having arbitrary memory. We formulate the variational problem of minimizing the sum rate subject to a distortion constraint, and the conventional Lagrange method is extended to solve the problem. A sufficient condition is also found that can be used to verify if the necessary solution results in the minimal sum rate.

          The second class of problems is target localization. We analyze the performance of a reduced-dimension separable space-time adaptive processing algorithm for radar systems under the large array assumption. The study shows that in the asymptotic sense the simplified scheme performs as well as the fully adaptive algorithm with a significant saving in computational complexity. For target localization in wireless systems, we propose a two-stage approach in order to handle non-line-of-sight transmission based on mild assumptions regarding the propagation environments. For the first stage of positioning, we analyze the cases of scattering and reflection respectively, and propose methods to estimate the position and velocity of the moving target. Once the estimation is done, the results can be used as the initial values of extended Kalman filters in the second stage to track the subsequent movements of the target.

            Cover page: Resource Allocation in Communication, Quantization, and Localization
            • Article
            • Peer Reviewed
            UCLA Previously Published Works (2017)

            Objective

            To estimate health care expenditure trends among young adults ages 19-25 before and after the 2010 implementation of the Affordable Care Act (ACA) provision that extended eligibility for dependent private health insurance coverage.

            Data sources

            Nationally representative Medical Expenditure Panel Survey data from 2008 to 2012.

            Study design

            We conducted repeated cross-sectional analyses and employed a difference-in-differences quantile regression model to estimate health care expenditure trends among young adults ages 19-25 (the treatment group) and ages 27-29 (the control group).

            Principal findings

            Our results show that the treatment group had 14 percent lower overall health care expenditures and 21 percent lower out-of-pocket payments compared with the control group in 2011-2012. The overall reduction in health care expenditures among young adults ages 19-25 in years 2011-2012 was more significant at the higher end of the health care expenditure distribution. Young adults ages 19-25 had significantly higher emergency department costs at the 10th percentile in 2011-2012. Differences in the trends of costs of private health insurance and doctor visits are not statistically significant.

            Conclusions

            Increased health insurance enrollment as a consequence of the ACA provision for dependent coverage has successfully reduced spending and catastrophic expenditures, providing financial protections for young adults.
              Cover page: Reducing Young Adults’ Health Care Spending through the ACA Expansion of Dependent Coverage
              • Article
              • Peer Reviewed
              UC Irvine Previously Published Works (2013)
              In this paper, we consider a dynamic linear system in statespace form where the observation equation depends linearly on a set of parameters. We address the problem of how to dynamically calculate these parameters in order to minimize the mean-squared error (MSE) of the state estimate achieved by a Kalman filter. We formulate and solve two kinds of problems under a quadratic constraint on the observation parameters: minimizing the sum MSE (Min-Sum-MSE) or minimizing the maximum MSE (Min-Max-MSE). In each case, the optimization problem is divided into two sub-problems for which optimal solutions can be found: a semidefinite programming (SDP) problem followed by a constrained least-squares minimization. A more direct solution is shown to exist for the special case of a scalar observation; in particular, the Min-Sum-MSE problem is optimally solved utilizing Rayleigh quotient, and the Min-Max-MSE problemreduces to an SDP feasibility test that can be solved via the bisection method. © 2013 IEEE.
                Cover page: LINEARLY RECONFIGURABLE KALMAN FILTERING FOR A VECTOR PROCESSCreative Commons 'BY' version 4.0 license
                • Article
                • Peer Reviewed
                UC Irvine Previously Published Works (2014)
                We consider the problem of optimal power allocation in a sensor network where the sensors observe a dynamic parameter in noise and coherently amplify and forward their observations to a fusion center (FC). The FC uses the observations in a Kalman filter to track the parameter, and we show how to find the optimal gain and phase of the sensor transmissions under both global and individual power constraints in order to minimize the mean squared error (MSE) of the parameter estimate. For the case of a global power constraint, a closed-form solution can be obtained. A numerical optimization is required for individual power constraints, but the problem can be relaxed to a semidefinite programming problem (SDP), and we show that the optimal result can be constructed from the SDP solution. We also study the dual problem of minimizing global and individual power consumption under a constraint on the MSE. As before, a closed-form solution can be found when minimizing total power, while the optimal solution is constructed from the output of an SDP when minimizing the maximum individual sensor power. For purposes of comparison, we derive an exact expression for the outage probability on the MSE for equal-power transmission, which can serve as an upper bound for the case of optimal power control. Finally, we present the results of several simulations to show that the use of optimal power control provides a significant reduction in either MSE or transmit power compared with a non-optimized approach (i.e., equal power transmission).
                  Cover page: Optimal Power Allocation for Parameter Tracking in a Distributed Amplify-and-Forward Sensor NetworkCreative Commons 'BY' version 4.0 license
                  • Article
                  • Peer Reviewed
                  UC Irvine Previously Published Works (2013)
                  We consider a network of single-antenna sensors that observe an unknown deterministic parameter. Each sensor applies a phase shift to the observation and the sensors simultaneously transmit the result to a multi-antenna fusion center (FC). Based on its knowledge of the wireless channel to the sensors, the FC calculates values for the phase factors that minimize the variance of the parameter estimate, and feeds this information back to the sensors. The use of a phase-shift-only transmission scheme provides a simplified analog implementation at the sensor, and also leads to a simpler algorithm design and performance analysis. We propose two algorithms for this problem, a numerical solution based on a relaxed semidefinite programming problem, and a closed-form solution based on the analytic constant modulus algorithm. Both approaches are shown to provide performance close to the theoretical bound. We derive asymptotic performance analyses for cases involving large numbers of sensors or large numbers of FC antennas, and we also study the impact of phase errors at the sensor transmitters. Finally, we consider the sensor selection problem, in which only a subset of the sensors is chosen to send their observations to the FC. © 1991-2012 IEEE.
                    Cover page: Estimation in Phase-Shift and Forward Wireless Sensor NetworksCreative Commons 'BY' version 4.0 license
                    • Article
                    • Peer Reviewed
                    UC Santa Barbara Previously Published Works (2023)
                    Research on interactions involving police officers foregrounds the importance of their communicative practices for fostering civilians’ perceptions of police legitimacy. Building on this research, we describe a pattern of conduct that is a recurrent source of trouble in such encounters, which we call sequential standoffs. These standoffs emerge when two parties persistently pursue alternative courses of action, producing a stalemate in which neither progress in, nor exit from, either course of action appears viable. They are routinely resolved by officers (re)casting civilians’ pursuit of one course of action as constituting resistance to the officers’ proposed course of action, and thus as warranting officers’ use of coercive violence to resolve the stalemate. In some cases, however, officers resolve standoffs cooperatively using sequentially accommodative methods. We consider how these findings advance approaches to communicative dilemmas in policing, and their broader significance for scholars of social interaction, and of the interactional organization of conflicts.
                      Cover page: Sequential Standoffs in Police Encounters With the PublicCreative Commons 'BY-ND' version 4.0 license
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