The Experts below are selected from a list of 60033 Experts worldwide ranked by ideXlab platform
Barbara C Tilley - One of the best experts on this subject based on the ideXlab platform.
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continuous Time Markov Chain approaches for analyzing transtheoretical models of health behavioral change a case study and comparison of model estimations
Statistical Methods in Medical Research, 2018Co-Authors: Wenyaw Chan, Barbara C TilleyAbstract:Continuous Time Markov Chain models are frequently employed in medical research to study the disease progression but are rarely applied to the transtheoretical model, a psychosocial model widely used in the studies of health-related outcomes. The transtheoretical model often includes more than three states and conceptually allows for all possible instantaneous transitions (referred to as general continuous Time Markov Chain). This complicates the likelihood function because it involves calculating a matrix exponential that may not be simplified for general continuous Time Markov Chain models. We undertook a Bayesian approach wherein we numerically evaluated the likelihood using ordinary differential equation solvers available from the gnu scientific library. We compared our Bayesian approach with the maximum likelihood method implemented with the R package MSM. Our simulation study showed that the Bayesian approach provided more accurate point and interval estimates than the maximum likelihood method, especially in complex continuous Time Markov Chain models with five states. When applied to data from a four-state transtheoretical model collected from a nutrition intervention study in the next step trial, we observed results consistent with the results of the simulation study. Specifically, the two approaches provided comparable point estimates and standard errors for most parameters, but the maximum likelihood offered substantially smaller standard errors for some parameters. Comparable estimates of the standard errors are obtainable from package MSM, which works only when the model estimation algorithm converges.
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continuous Time Markov Chain approaches for analyzing transtheoretical models of health behavioral change a case study and comparison of model estimations
Statistical Methods in Medical Research, 2018Co-Authors: Wenyaw Chan, Barbara C TilleyAbstract:Continuous Time Markov Chain models are frequently employed in medical research to study the disease progression but are rarely applied to the transtheoretical model, a psychosocial model widely us...
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analysis of transtheoretical model of health behavioral changes in a nutrition intervention study a continuous Time Markov Chain model with bayesian approach
Statistics in Medicine, 2015Co-Authors: Wenyaw Chan, Chulin Tsai, Momiao Xiong, Barbara C TilleyAbstract:Continuous Time Markov Chain (CTMC) models are often used to study the progression of chronic diseases in medical research but rarely applied to studies of the process of behavioral change. In studies of interventions to modify behaviors, a widely used psychosocial model is based on the transtheoretical model that often has more than three states (representing stages of change) and conceptually permits all possible instantaneous transitions. Very little attention is given to the study of the relationships between a CTMC model and associated covariates under the framework of transtheoretical model. We developed a Bayesian approach to evaluate the covariate effects on a CTMC model through a log-linear regression link. A simulation study of this approach showed that model parameters were accurately and precisely estimated. We analyzed an existing data set on stages of change in dietary intake from the Next Step Trial using the proposed method and the generalized multinomial logit model. We found that the generalized multinomial logit model was not suitable for these data because it ignores the unbalanced data structure and temporal correlation between successive measurements. Our analysis not only confirms that the nutrition intervention was effective but also provides information on how the intervention affected the transitions among the stages of change. We found that, compared with the control group, subjects in the intervention group, on average, spent substantively less Time in the precontemplation stage and were more/less likely to move from an unhealthy/healthy state to a healthy/unhealthy state.
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analysis of transtheoretical model of health behavioral changes in a nutrition intervention study a continuous Time Markov Chain model with bayesian approach
Statistics in Medicine, 2015Co-Authors: Wenyaw Chan, Chulin Tsai, Momiao Xiong, Barbara C TilleyAbstract:Continuous Time Markov Chain (CTMC) models are often used to study the progression of chronic diseases in medical research but rarely applied to studies of the process of behavioral change. In studies of interventions to modify behaviors, a widely used psychosocial model is based on the transtheoretical model that often has more than three states (representing stages of change) and conceptually permits all possible instantaneous transitions. Very little attention is given to the study of the relationships between a CTMC model and associated covariates under the framework of transtheoretical model. We developed a Bayesian approach to evaluate the covariate effects on a CTMC model through a log-linear regression link. A simulation study of this approach showed that model parameters were accurately and precisely estimated. We analyzed an existing data set on stages of change in dietary intake from the Next Step Trial using the proposed method and the generalized multinomial logit model. We found that the generalized multinomial logit model was not suitable for these data because it ignores the unbalanced data structure and temporal correlation between successive measurements. Our analysis not only confirms that the nutrition intervention was effective but also provides information on how the intervention affected the transitions among the stages of change. We found that, compared with the control group, subjects in the intervention group, on average, spent substantively less Time in the precontemplation stage and were more/less likely to move from an unhealthy/healthy state to a healthy/unhealthy state. Copyright © 2015 John Wiley & Sons, Ltd.
Wenyaw Chan - One of the best experts on this subject based on the ideXlab platform.
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continuous Time Markov Chain approaches for analyzing transtheoretical models of health behavioral change a case study and comparison of model estimations
Statistical Methods in Medical Research, 2018Co-Authors: Wenyaw Chan, Barbara C TilleyAbstract:Continuous Time Markov Chain models are frequently employed in medical research to study the disease progression but are rarely applied to the transtheoretical model, a psychosocial model widely us...
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continuous Time Markov Chain approaches for analyzing transtheoretical models of health behavioral change a case study and comparison of model estimations
Statistical Methods in Medical Research, 2018Co-Authors: Wenyaw Chan, Barbara C TilleyAbstract:Continuous Time Markov Chain models are frequently employed in medical research to study the disease progression but are rarely applied to the transtheoretical model, a psychosocial model widely used in the studies of health-related outcomes. The transtheoretical model often includes more than three states and conceptually allows for all possible instantaneous transitions (referred to as general continuous Time Markov Chain). This complicates the likelihood function because it involves calculating a matrix exponential that may not be simplified for general continuous Time Markov Chain models. We undertook a Bayesian approach wherein we numerically evaluated the likelihood using ordinary differential equation solvers available from the gnu scientific library. We compared our Bayesian approach with the maximum likelihood method implemented with the R package MSM. Our simulation study showed that the Bayesian approach provided more accurate point and interval estimates than the maximum likelihood method, especially in complex continuous Time Markov Chain models with five states. When applied to data from a four-state transtheoretical model collected from a nutrition intervention study in the next step trial, we observed results consistent with the results of the simulation study. Specifically, the two approaches provided comparable point estimates and standard errors for most parameters, but the maximum likelihood offered substantially smaller standard errors for some parameters. Comparable estimates of the standard errors are obtainable from package MSM, which works only when the model estimation algorithm converges.
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analysis of transtheoretical model of health behavioral changes in a nutrition intervention study a continuous Time Markov Chain model with bayesian approach
Statistics in Medicine, 2015Co-Authors: Wenyaw Chan, Chulin Tsai, Momiao Xiong, Barbara C TilleyAbstract:Continuous Time Markov Chain (CTMC) models are often used to study the progression of chronic diseases in medical research but rarely applied to studies of the process of behavioral change. In studies of interventions to modify behaviors, a widely used psychosocial model is based on the transtheoretical model that often has more than three states (representing stages of change) and conceptually permits all possible instantaneous transitions. Very little attention is given to the study of the relationships between a CTMC model and associated covariates under the framework of transtheoretical model. We developed a Bayesian approach to evaluate the covariate effects on a CTMC model through a log-linear regression link. A simulation study of this approach showed that model parameters were accurately and precisely estimated. We analyzed an existing data set on stages of change in dietary intake from the Next Step Trial using the proposed method and the generalized multinomial logit model. We found that the generalized multinomial logit model was not suitable for these data because it ignores the unbalanced data structure and temporal correlation between successive measurements. Our analysis not only confirms that the nutrition intervention was effective but also provides information on how the intervention affected the transitions among the stages of change. We found that, compared with the control group, subjects in the intervention group, on average, spent substantively less Time in the precontemplation stage and were more/less likely to move from an unhealthy/healthy state to a healthy/unhealthy state.
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analysis of transtheoretical model of health behavioral changes in a nutrition intervention study a continuous Time Markov Chain model with bayesian approach
Statistics in Medicine, 2015Co-Authors: Wenyaw Chan, Chulin Tsai, Momiao Xiong, Barbara C TilleyAbstract:Continuous Time Markov Chain (CTMC) models are often used to study the progression of chronic diseases in medical research but rarely applied to studies of the process of behavioral change. In studies of interventions to modify behaviors, a widely used psychosocial model is based on the transtheoretical model that often has more than three states (representing stages of change) and conceptually permits all possible instantaneous transitions. Very little attention is given to the study of the relationships between a CTMC model and associated covariates under the framework of transtheoretical model. We developed a Bayesian approach to evaluate the covariate effects on a CTMC model through a log-linear regression link. A simulation study of this approach showed that model parameters were accurately and precisely estimated. We analyzed an existing data set on stages of change in dietary intake from the Next Step Trial using the proposed method and the generalized multinomial logit model. We found that the generalized multinomial logit model was not suitable for these data because it ignores the unbalanced data structure and temporal correlation between successive measurements. Our analysis not only confirms that the nutrition intervention was effective but also provides information on how the intervention affected the transitions among the stages of change. We found that, compared with the control group, subjects in the intervention group, on average, spent substantively less Time in the precontemplation stage and were more/less likely to move from an unhealthy/healthy state to a healthy/unhealthy state. Copyright © 2015 John Wiley & Sons, Ltd.
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estimating transition probabilities for ignorable intermittent missing data in a discrete Time Markov Chain
Communications in Statistics - Simulation and Computation, 2010Co-Authors: Hung Wen Yeh, Wenyaw Chan, Elaine Symanski, Barry R DavisAbstract:This article considers a discrete-Time Markov Chain for modeling transition probabilities when multiple successive observations are missing at random between two observed outcomes using three methods: a na\"ive analog of complete-case analysis using the observed one-step transitions alone, a non data-augmentation method (NL) by solving nonlinear equations, and a data-augmentation method, the Expectation-Maximization (EM) algorithm. The explicit form of the conditional log-likelihood given the observed information as required by the E step is provided, and the iterative formula in the M step is expressed in a closed form. An empirical study was performed to examine the accuracy and precision of the estimates obtained in the three methods under ignorable missing mechanisms of missing completely at random and missing at random. A dataset from the mental health arena was used for illustration. It was found that both data-augmentation and nonaugmentation methods provide accurate and precise point estimation, a...
Yubin Zhao - One of the best experts on this subject based on the ideXlab platform.
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a hybrid secure scheme for wireless sensor networks against timing attacks using continuous Time Markov Chain and queueing model
Sensors, 2016Co-Authors: Tianhui Meng, Xiaofan Li, Sha Zhang, Yubin ZhaoAbstract:Wireless sensor networks (WSNs) have recently gained popularity for a wide spectrum of applications. Monitoring tasks can be performed in various environments. This may be beneficial in many scenarios, but it certainly exhibits new challenges in terms of security due to increased data transmission over the wireless channel with potentially unknown threats. Among possible security issues are timing attacks, which are not prevented by traditional cryptographic security. Moreover, the limited energy and memory resources prohibit the use of complex security mechanisms in such systems. Therefore, balancing between security and the associated energy consumption becomes a crucial challenge. This paper proposes a secure scheme for WSNs while maintaining the requirement of the security-performance tradeoff. In order to proceed to a quantitative treatment of this problem, a hybrid continuous-Time Markov Chain (CTMC) and queueing model are put forward, and the tradeoff analysis of the security and performance attributes is carried out. By extending and transforming this model, the mean Time to security attributes failure is evaluated. Through tradeoff analysis, we show that our scheme can enhance the security of WSNs, and the optimal rekeying rate of the performance and security tradeoff can be obtained.
Janet S Sinsheimer - One of the best experts on this subject based on the ideXlab platform.
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Bayesian selection of continuous-Time Markov Chain evolutionary models
Molecular Biology and Evolution, 2001Co-Authors: Marc A Suchard, R. E. Weiss, Janet S SinsheimerAbstract:We develop a reversible jump Markov Chain Monte Carlo approach to estimating the posterior distribution of phylogenies based on aligned DNA/RNA sequences under several hierarchical evolutionary models. Using a proper, yet nontruncated and uninformative prior, we demonstrate the advantages of the Bayesian approach to hypothesis testing and estimation in phylogenetics by comparing different models for the infinitesimal rates of change among nucleotides, for the number of rate classes, and for the relationships among branch lengths. We compare the relative probabilities of these models and the appropriateness of a molecular clock using Bayes factors. Our most general model, first proposed by Tamura and Nei, parameterizes the infinitesimal change probabilities among nucleotides (A, G, C, T/U) into six parameters, consisting of three parameters for the nucleotide stationary distribution, two rate parameters for nucleotide transitions, and another parameter for nucleotide transversions. Nested models include the Hasegawa, Kishino, and Yano model with equal transition rates and the Kimura model with a uniform stationary distribution and equal transition rates. To illustrate our methods, we examine simulated data, 16S rRNA sequences from 15 contemporary eubacteria, halobacteria, eocytes, and eukaryotes, 9 primates, and the entire HIV genome of 11 isolates. We find that the Kimura model is too restrictive, that the Hasegawa, Kishino, and Yano model can be rejected for some data sets, that there is evidence for more than one rate class and a molecular clock among similar taxa, and that a molecular clock can be rejected for more distantly related taxa.
Yongjin Wang - One of the best experts on this subject based on the ideXlab platform.
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rare shock two factor stochastic volatility and currency option pricing
Applied Mathematical Finance, 2014Co-Authors: Guanying Wang, Xingchun Wang, Yongjin WangAbstract:In this paper, we develop an option valuation model where the dynamics of the spot foreign exchange rate is governed by a two-factor Markov-modulated jump-diffusion process. The short-term fluctuation of stochastic volatility is driven by a Cox--Ingersoll--Ross (CIR) process and the long-term variation of stochastic volatility is driven by a continuous-Time Markov Chain which can be interpreted as economy states. Rare events are governed by a compound Poisson process with log-normal jump amplitude and stochastic jump intensity is modulated by a common continuous-Time Markov Chain. Since the market is incomplete under regime-switching assumptions, we determine a risk-neutral martingale measure via the Esscher transform and then give a pricing formula of currency options. Numerical results are presented for investigating the impact of the long-term volatility and the annual jump intensity on option prices.
