Browsing by Author "Miao, Yinsen"

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    Impact of hypothermia on implementation of CPAP for neonatal respiratory distress syndrome in a low-resource setting
    (Public Library of Science, 2018) Carns, Jennifer; Kawaza, Kondwani; Quinn, M.K.; Miao, Yinsen; Guerra, Rudy; Molyneux, Elizabeth; Oden, Maria; Richards-Kortum, Rebecca; Bioengineering; Statistics
    Background: Neonatal hypothermia is widely associated with increased risks of morbidity and mortality, but remains a pervasive global problem. No studies have examined the impact of hypothermia on outcomes for preterm infants treated with CPAP for respiratory distress syndrome (RDS). Methods: This retrospective analysis assessed the impact of hypothermia on outcomes of 65 neonates diagnosed with RDS and treated with either nasal oxygen (N = 17) or CPAP (N = 48) in a low-resource setting. A classification tree approach was used to develop a model predicting survival for subjects diagnosed with RDS. Findings: Survival to discharge was accurately predicted based on three variables: mean temperature, treatment modality, and mean respiratory rate. None of the 23 neonates with a mean temperature during treatment below 35.8°C survived to discharge, regardless of treatment modality. Among neonates with a mean temperature exceeding 35.8°C, the survival rate was 100% for the 31 neonates treated with CPAP and 36.4% for the 11 neonates treated with nasal oxygen (p<0.001). For neonates treated with CPAP, outcomes were poor if more than 50% of measured temperatures indicated hypothermia (5.6% survival). In contrast, all 30 neonates treated with CPAP and with more than 50% of temperature measurements above 35.8°C survived to discharge, regardless of initial temperature. Conclusion: The results of our study suggest that successful implementation of CPAP to treat RDS in low-resource settings will require aggressive action to prevent persistent hypothermia. However, our results show that even babies who are initially cold can do well on CPAP with proper management of hypothermia.
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    Salable Bayesian Algorithms for Quantitative Geosteering
    (2019-12-05) Miao, Yinsen; Vannucci, Marina
    Geosteering is the iterative process of navigating the Bottom Hole Assembly (BHA) in a given geological setting in order to achieve pre-specified targets. To guide the directional drilling process, directional survey and logging-while-drilling (LWD) sensor measurements are used to estimate BHA position and the lateral changes of the geological structure. Two types of contemporary geosteering approaches, namely, model-based and stratification-based, are introduced. In the Chapter 1, we formulate the stratification-based approach as a Bayesian optimization procedure: the log from a pilot reference well is used as a stratigraphic signature of the geological structure in a given region; the observed log sequence acquired along the wellbore is projected into the stratigraphic domain given a proposed earth model and directional survey; the pattern similarity between the converted log and the signature is measured by a correlation coefficient; then stochastic searching is performed on the space of all possible earth models to maximize the similarity under constraints of the prior understanding of the drilling process and target formation; finally inference is made based on the samples simulated from the posterior distribution using Stochastic Approximation Monte Carlo (SAMC). In chapter 2, we propose an efficient non-linear state space model approach to solve the model-based aspect of geosteering. This chapter is an extension to the chapter 1 whose limitations are further addressed here by taking the sequential nature of the acquired sensor measurements into account. For posterior inference of the latent states and model parameters, we apply extended Kalman filter, particle filter with Gibbs and particle filter with Metropolis Hasting. Our proposed methods consistently achieve good performance on synthetic datasets in term of high correlations between the interpreted log and reference log, and provides similar interpretations as the geosteering geologists on real wells. We developed C/C++ based Python packages gs_scpm and gs_smc that are efficient enough to provide accurate steering guidance to the geologists in real-time and those software packages are deployed via https://www.geodesic.ai . Variable selection, also known as feature selection in the machine learning literature, plays an indispensable role in scientific studies. In many research areas with massive data, finding a subset of representative features that best explain the outcome of interest has become a critical component in any researcher's workflow. In chapter 3, we focus on Bayesian variable selection regression models for count data, and specifically on the negative binomial linear regression model. We address the variable selection problem via spike-and-slab priors. For posterior inference, we review standard MCMC methods and also investigate computationally more efficient variational inference approaches that use data augmentation techniques. We investigate performance of the methods via simulation studies and benchmark datasets. We provide C/C++ code at https://github.com/marinavannucci/snbvbs that help to considerably speed up the variable selection inference process for the negative binomial regression models.
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    Stochastic clustering and pattern matching for real-time geosteering
    (Society of Exploration Geophysicists, 2019) Wu, Mingqi; Miao, Yinsen; Panchal, Neilkunal; Kowal, Daniel R.; Vannucci, Marina; Vila, Jeremy; Liang, Faming
    We have developed a Bayesian statistical framework for quantitative geosteering in real time. Two types of contemporary geosteering approaches, model based and stratification based, are introduced. The latter is formulated as a Bayesian optimization procedure: The log from a pilot reference well is used as a stratigraphic signature of the geologic structure in a given region; the observed log sequence acquired along the wellbore is projected into the stratigraphic domain given a proposed earth model and directional survey; the pattern similarity between the converted log and the signature is measured by a correlation coefficient; then stochastic searching is performed on the space of all possible earth models to maximize the similarity under constraints of the prior understanding of the drilling process and target formation; finally, an inference is made based on the samples simulated from the posterior distribution using stochastic approximation Monte Carlo in which we extract the most likely earth model and the associated credible intervals as a quantified confidence indicator. We extensively test our method using synthetic and real geosteering data sets. Our method consistently achieves good performance on synthetic data sets with high correlations between the interpreted and the reference logs and provides similar interpretations as the geosteering geologists on four real wells. We also conduct a reliability performance test of the method on a benchmark set of 200 horizontal wells randomly sampled from the Permian Basin. Our Bayesian framework informs geologists with key drilling decisions in real time and helps them navigate the drilling bit into the target formation with confidence.
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    Tukey's transformational ladder for portfolio management
    (Springer, 2017) Ernst, Philip A.; Thompson, James R.; Miao, Yinsen
    Over the past half-century, the empirical finance community has produced vast literature on the advantages of the equally weighted Standard and Poor (S&P 500) portfolio as well as the often overlooked disadvantages of the market capitalization weighted S&P 500’s portfolio (see Bloomfield et al. in J Financ Econ 5:201–218, 1977; DeMiguel et al. in Rev Financ Stud 22(5):1915–1953, 2009; Jacobs et al. in J Financ Mark 19:62–85, 2014; Treynor in Financ Anal J 61(5):65–69, 2005). However, portfolio allocation based on Tukey’s transformational ladder has, rather surprisingly, remained absent from the literature. In this work, we consider the S&P 500 portfolio over the 1958–2015 time horizon weighted by Tukey’s transformational ladder (Tukey in Exploratory data analysis, Addison-Wesley, Boston, 1977): 1/x2,1/x,1/x−−√,log(x),x−−√,x,andx2, where x is defined as the market capitalization weighted S&P 500 portfolio. Accounting for dividends and transaction fees, we find that the 1/x2 weighting strategy produces cumulative returns that significantly dominate all other portfolio returns, achieving a compound annual growth rate of 18% over the 1958–2015 horizon. Our story is furthered by a startling phenomenon: both the cumulative and annual returns of the 1/x2 weighting strategy are superior to those of the 1 / x weighting strategy, which are in turn superior to those of the 1/x−−√ weighted portfolio, and so forth, ending with the x2 transformation, whose cumulative returns are the lowest of the seven transformations of Tukey’s transformational ladder. The order of cumulative returns precisely follows that of Tukey’s transformational ladder. To the best of our knowledge, we are the first to discover this phenomenon.