Existing research on platoon dispersion models either describe homogeneous traffic flow feature, or are in lack of analytical solutions. By analyzing the field data, the truncated mixed simplified phase-type distribut...
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Existing research on platoon dispersion models either describe homogeneous traffic flow feature, or are in lack of analytical solutions. By analyzing the field data, the truncated mixed simplified phase-type distribution is proved to be capable of capturing the characteristics of heterogeneous traffic flow with an excellent fitting result. In light of this, we derive a generic heterogeneous platoon dispersion model with truncated mixed simplified phase-type of speed in the forms of integrable functions. Numerical case studies are conducted to compare the performance of the proposed model and the conventional models (i.e., the Robertson model and truncated mixed Gaussian model). The results show that the proposed model not only better captures the platoon dispersion laws of heterogeneous traffic flow, but also presents higher computational efficiency, which provides practical implications on traffic signal control. Copyright (C) 2017 John Wiley & Sons, Ltd.
Multi-type recurrent event data arise in many situations when two or more different event types may occur repeatedly over an observation period. For example, in a randomized controlled clinical trial to study the effi...
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Multi-type recurrent event data arise in many situations when two or more different event types may occur repeatedly over an observation period. For example, in a randomized controlled clinical trial to study the efficacy of nutritional supplements for skin cancer prevention, there can be two types of skin cancer events occur repeatedly over time. The research objectives of analyzing such data often include characterizing the event rate of different event types, estimating the treatment effects on each event process, and understanding the correlation structure among different event types. In this paper, we propose the use of a proportional intensity model with multivariate random effects to model such data. The proposed model can take into account the dependence among different event types within a subject as well as the treatment effects. Maximum likelihood estimates of the regression coefficients, variance-covariance components, and the nonparametric baseline intensity function are obtained via a Monte Carlo Expectation-Maximization (MCem) algorithm. The expectation step of the algorithm involves the calculation of the conditional expectations of the random effects by using the Metropolis-Hastings sampling. Our proposed method can easily handle recurrent event data that have more than two types of events. Simulation studies were used to validate the performance of the proposed method, followed by an application to the skin cancer prevention data. (C) 2016 Elsevier B.V. All rights reserved.
Normal mixture regression models are one of the most important statistical data analysis tools in a heterogeneous population. When the data set under consideration involves asymmetric outcomes, in the last two decades...
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Normal mixture regression models are one of the most important statistical data analysis tools in a heterogeneous population. When the data set under consideration involves asymmetric outcomes, in the last two decades, the skew normal distribution has been shown beneficial in dealing with asymmetric data in various theoretic and applied problems. In this paper, we propose and study a novel class of models： a skew-normal mixture of joint location, scale and skewness models to analyze the heteroscedastic skew-normal data coming from a heterogeneous population. The issues of maximum likelihood estimation are addressed. In particular, an Expectation-Maximization （em） algorithm for estimating the model parameters is developed. Properties of the estimators of the regression coefficients are evaluated through Monte Carlo experiments. Results from the analysis of a real data set from the Body Mass Index （BMI） data are presented.
We introduce new estimation methods for a subclass of the Gaussian scale mixture models for wavelet trees by Wainwright, Simoncelli and Willsky that rely on modern results for composite likelihoods and approximate Bay...
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We introduce new estimation methods for a subclass of the Gaussian scale mixture models for wavelet trees by Wainwright, Simoncelli and Willsky that rely on modern results for composite likelihoods and approximate Bayesian inference. Our methodology is illustrated for denoising and edge detection problems in two-dimensional images. Copyright (c) 2017 John Wiley & Sons, Ltd.
This paper presents an improved Rao-Blackwellized particle filtering framework with consideration of the particle swarm characteristics in FastSLAM, called Relational FastSLAM or R-FastSLAM. The R-FastSLAM seeks to co...
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This paper presents an improved Rao-Blackwellized particle filtering framework with consideration of the particle swarm characteristics in FastSLAM, called Relational FastSLAM or R-FastSLAM. The R-FastSLAM seeks to cope with the inherent problems of FastSLAM, i.e., a particle depletion problem and an error accumulation problem in large environments. The R-FastSLAM uses the particle swarm characteristics in calculating the importance weight and maintaining a particle formation. We assign more accurate weights to particles by clustering them using the Expectation-Maximization (em) algorithm according to an adaptive weight compensation scheme. In addition, particles constitute an adaptive triangular mesh formation to maintain multiple data association hypotheses without any resampling step. Its outstanding accomplishments are verified on simulations and a test using the Victoria Park dataset by comparing the standard FastSLAM 2.0 with the particle swarm optimization based FastSLAM.
In this paper, an Expectation-Maximization (em) based iterative data detection method for downlink of a multicarrier system is proposed. The proposed method has low computational cost when compared with the other iter...
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In this paper, an Expectation-Maximization (em) based iterative data detection method for downlink of a multicarrier system is proposed. The proposed method has low computational cost when compared with the other iterative ones since it does not require any matrix inversion if the requirements on users' data are met. The performance of the resulting algorithm is compared with Minimum Mean Squared Error (MMSE) estimator in terms of Symbol Error Rate (SER) for downlink of a Multi Carrier-Code Division Multiple Access (MC-CDMA) system in the presence of frequency selective channels using computer simulations. It is illustrated that the proposed algorithm outperforms the MMSE.
Univariate Birnbaum-Saunders models have been widely applied to fatigue studies. Calculation of fatigue life is of great importance in determining the reliability of materials. We propose and derive new multivariate g...
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Univariate Birnbaum-Saunders models have been widely applied to fatigue studies. Calculation of fatigue life is of great importance in determining the reliability of materials. We propose and derive new multivariate generalized Birnbaum-Saunders regression models. We use the maximum likelihood method and the em algorithm to estimate their parameters. We carry out a simulation study to evaluate the performance of the corresponding maximum likelihood estimators. We illustrate the new models with real-world multivariate fatigue data.
A semi-parametric mixture of quantile regressions model is proposed to allow regressions of the conditional quantiles, such as the median, on the covariates without any parametric assumption on the error densities. Th...
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A semi-parametric mixture of quantile regressions model is proposed to allow regressions of the conditional quantiles, such as the median, on the covariates without any parametric assumption on the error densities. The median as a measure of center is known to be more robust to skewness and outliers than the mean. Modeling the quantiles instead of the mean not only improves the robustness of the model but also reveals a fuller picture of the data by fitting varying quantile functions. The proposed semi-parametric mixture of quantile regressions model is proven to be identifiable under certain weak conditions. A kernel density based em-type algorithm is developed to estimate the model parameters, while a stochastic version of the em-type algorithm is constructed for the variance estimation. A couple of simulation studies and several real data applications are conducted to show the effectiveness of the proposed model. (C) 2014 Elsevier B.V. All rights reserved.
Domain experts can often quite reliably specify the sign of influences between variables in a Bayesian network. If we exploit this prior knowledge in estimating the probabilities of the network, it is more likely to b...
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Domain experts can often quite reliably specify the sign of influences between variables in a Bayesian network. If we exploit this prior knowledge in estimating the probabilities of the network, it is more likely to be accepted by its users and may in fact be better calibrated with reality. We present two algorithms that exploit prior knowledge of qualitative influences in learning the parameters of a Bayesian network from incomplete data. The isotonic regression em, or irem, algorithm adds an isotonic regression step to standard em in each iteration, to obtain parameter estimates that satisfy the given qualitative influences. In an attempt to reduce the computational burden involved, we further define the qirem algorithm that enforces the constraints imposed by the qualitative influences only once, after convergence of standard em. We evaluate the performance of both algorithms through experiments. Our results demonstrate that exploitation of the qualitative influences improves the parameter estimates over standard em, and more so if the proportion of missing data is relatively large. The results also show that the qirem algorithm performs just as well as its computationally more expensive counterpart irem. (C) 2015 Elsevier Inc. All rights reserved.
We consider estimation of the unknown parameters of Chen distribution [Chen Z. A new two-parameter lifetime distribution with bathtub shape or increasing failure rate function. Statist Probab Lett. 2000;49:155-161] wi...
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We consider estimation of the unknown parameters of Chen distribution [Chen Z. A new two-parameter lifetime distribution with bathtub shape or increasing failure rate function. Statist Probab Lett. 2000;49:155-161] with bathtub shape using progressive-censored samples. We obtain maximum likelihood estimates by making use of an expectation-maximization algorithm. Different Bayes estimates are derived under squared error and balanced squared error loss functions. It is observed that the associated posterior distribution appears in an intractable form. So we have used an approximation method to compute these estimates. A Metropolis-Hasting algorithm is also proposed and some more approximate Bayes estimates are obtained. Asymptotic confidence interval is constructed using observed Fisher information matrix. Bootstrap intervals are proposed as well. Sample generated from MH algorithm are further used in the construction of HPD intervals. Finally, we have obtained prediction intervals and estimates for future observations in one- and two-sample situations. A numerical study is conducted to compare the performance of proposed methods using simulations. Finally, we analyse real data sets for illustration purposes.
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