Browsing by Author "Chen, Liang"

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    Minimum Hellinger Distance Estimation of ARCH/GARCH Models
    (2018-05-11) Chen, Liang; Wu, Jingjing; Lu, Xuewen; Shen, Hua
    In this thesis, we proposed a minimum Hellinger distance estimator (MHDE) and a minimum profile Hellinger distance estimator (MPHDE) for estimating the parameters in the ARCH and GARCH models depending on whether the innovation distribution is specified or not. The asymptotic properties of MHDE and MPHDE were examined through graphs as the theoretical investigation of them are more involved and needs further study in the future research. Moreover, we demonstrated the finite-sample performance of both MHDE and MPHDE through simulation studies and compared them with the well-established methods including maximum likelihood estimation (MLE), Gaussian Quasi-MLE (GQMLE) and Non-Gaussian Quasi-MLE (NGQMLE). Our numerical results showed that MHDE and MPHDE have better performance in terms of bias, MSE and coverage probability (CP) when the data are contaminated, which testified to the robustness of MHD-type estimators.
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    Neuroprotection against traumatic brain injury by a peptide derived from the collapsin response mediator protein 2 (CRMP2)
    (The American Society for Biochemistry and Molecular Biology, Inc., 2011-08-09) Brittain, Joel M.; Chen, Liang; Wilson, Sarah M.; Brustovetsky, Tatiana; Gao, Xiang; Ashpole, Nicole M.; Molosh, Andrei I.; You, Haitao; Hudmon, Andy; Shekhar, Anantha S.; White, Fletcher A.; Zamponi, Gerald W.; Brustovetsky, Nickolay N.; Chen, Jinhui; Khanna, Rajesh
    Neurological disabilities following traumatic brain injury (TBI) may be due to excitotoxic neuronal loss. The excitotoxic loss of neurons following TBI occurs largely due to hyperactivation of N-methyl-d-aspartate receptors (NMDARs), leading to toxic levels of intracellular Ca(2+). The axon guidance and outgrowth protein collapsin response mediator protein 2 (CRMP2) has been linked to NMDAR trafficking and may be involved in neuronal survival following excitotoxicity. Lentivirus-mediated CRMP2 knockdown or treatment with a CRMP2 peptide fused to HIV TAT protein (TAT-CBD3) blocked neuronal death following glutamate exposure probably via blunting toxicity from delayed calcium deregulation. Application of TAT-CBD3 attenuated postsynaptic NMDAR-mediated currents in cortical slices. In exploring modulation of NMDARs by TAT-CBD3, we found that TAT-CBD3 induced NR2B internalization in dendritic spines without altering somal NR2B surface expression. Furthermore, TAT-CBD3 reduced NMDA-mediated Ca(2+) influx and currents in cultured neurons. Systemic administration of TAT-CBD3 following a controlled cortical impact model of TBI decreased hippocampal neuronal death. These findings support TAT-CBD3 as a novel neuroprotective agent that may increase neuronal survival following injury by reducing surface expression of dendritic NR2B receptors.
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    The Significance of Epidemic Plasmids in the Success of Multidrug-Resistant Drug Pandemic Extraintestinal Pathogenic Escherichia coli
    (2023-03-22) Pitout, Johann D. D.; Chen, Liang
    Abstract Epidemic IncF plasmids have been pivotal in the selective advantage of multidrug-resistant (MDR) extraintestinal pathogenic Escherichia coli (ExPEC). These plasmids have offered several advantages to their hosts that allowed them to coevolve with the bacterial host genomes and played an integral role in the success of ExPEC. IncF plasmids are large, mosaic, and often contain various types of antimicrobial resistance (AMR) and virulence associated factor (VAF) genes. The presence of AMR, VAF genes, several addition/restriction systems combined with truncated transfer regions, led to the fixation of IncF plasmids in certain ExPEC MDR clones, such as ST131 and ST410. IncF plasmids entered the ST131 ancestral lineage in the mid 1900s and different ST131 clade/CTX-M plasmid combinations coevolved over time. The IncF_CTX-M-15/ST131-C2 subclade combination emerged during the early 2000s, spread rapidly across the globe, and is one of the greatest clone/plasmid successes of the millennium. The ST410-B3 subclade containing blaCTX-M-15 incorporated the NDM-5 carbapenemase gene into existing IncF platforms, providing an additional positive selective advantage that included the carbapenems. A “plasmid-replacement” clade scenario occurred in the histories of ST131 and ST410 as different subclades gained different AMR genes on different IncF platforms. The use of antimicrobial agents will generate selection pressures that enhance the risks for the continuous emergence of MDR ExPEC clone/IncF plasmid combinations. The reasons for clade/IncF replacements and associations between certain clades and specific IncF plasmid types are unknown. Such information will aid in designing management and prevention strategies to combat AMR.
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    Theories and Methodologies for Cognitive Machine Learning based on Denotational Mathematics
    (2018-06-22) Valipour, Mehrdad; Wang, Yingxu; Gavrilova, Marina L.; Yanushkevich, Svetlana N.; Chen, Zhangxing; Chen, Liang
    Learning is a cognitive process of knowledge and behavior acquisition for both humans and machines. Cognitive machine learning systems are increasingly demanded in modern knowledge-based industry, society, and everyday lives. This study on theories and applications of cognitive machine learning based on denotational mathematics is designed to develop methodologies, algorithms, and their implementations for machine enabled knowledge learning at the conceptual, phrasal, and sentence levels via cognitive computing technologies. The main objectives of this work are: a) To develop a cognitive and mathematics-based machine learning theory for knowledge acquisition and semantic manipulations; b) To enable machines to autonomously learn and understand semantics expressed in natural languages underpinned by unsupervised cognitive computing algorithms; and c) To design and implement a brain-inspired cognitive learning engine for inductively learning from the level of formal concepts to those of phrases and sentences. The thesis is embodied by three novel and autonomous machine knowledge learning algorithms underpinned by Wang’s denotational mathematics. In this research, properties of formal concepts and mathematical rules of concept algebra are formally studied. A method for building quantitative semantic hierarchies of formal concepts is implemented by cognitive machine learning. Theories and mathematical models for an unsupervised algorithm of phrase learning are developed based on rigorous concept comprehensions by cognitive machine learning. A machine knowledge learning system for sentence syntactic analysis and semantic synthesis is developed and implemented by novel cognitive computing technologies. This thesis does not only present a set of basic studies on machine learning challenges in the sixth category of knowledge learning and semantic comprehension, but also implement efficient cognitive machine learning systems mimicking human learning. This research will enable a wide range of industrial applications such as cognitive robotics, natural language comprehension systems, personal leaning assistants, cognitive search engines, and language translators.
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    Three Essays in Structural Estimation: Models of Matching and Asymmetric Information
    (2014-05-01) Chen, Liang; Choo, Eugene
    There is growing interest in using structural estimation methods to address economic questions. There are two main two advantages of using structural estimation methods. First, they can solve the endogeneity problem confronting numerous reduced-form regression works. Second, they estimate the deep parameters of the models, which allows researchers to analyze many interesting economic questions through counterfactual analysis. In this dissertation, I study three di erent economic questions by structurally estimating models of matching and asymmetric information. In the rst chapter, I develop an estimable model which illustrates that the presence of moral hazard not only leads to ine ciency caused by risk sharing across rms and CEOs, but also creates ine ciency due to a talent misallocation. A new empirical method is proposed to identify the separate surplus of both rms and CEOs in a matching market with moral hazard. An application of this method to the U.S market for CEOs shows that the aggregate e ciency loss due to talent misallocation is $12:64 billion. This is more than four times as large as the loss stemming from risk-sharing between rms and CEOs. In the second chapter, my coauthor and I propose a new approach to identify models with network e ects by invoking another side of the market. We show that other side of the market provides additional information for identi cation. Our running application investigates the importance of asymmetric information and network e ects in the yellow pages advertising industry. In the third chapter, I study estimation and non-parametric identi cation of a dynamic matching model with a broader class of generalized unobserved heterogeneities. I rst provide the identi cation results on the match surplus. I then show that the match equilibrium exists and is globally unique. Finally, I provide a new estimation method for our dynamic matching model, which provides more precise estimates than previous methods.