Following the outbreak of COVID-19, various containment measures have been taken, including the use of quarantine. At present, the quarantine period is the same for everyone, since it is implicitly assumed that the in...
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Following the outbreak of COVID-19, various containment measures have been taken, including the use of quarantine. At present, the quarantine period is the same for everyone, since it is implicitly assumed that the incubation period distribution of COVID-19 is the same regardless of age or gender. For testing the effects of age and gender on the incubation period of COVID-19, a novel two-component mixture regression model is proposed. An expectation-maximization (em) algorithm is adopted to obtain estimates of the parameters of interest, and the simulation results show that the proposed method outperforms the simple regression method and has robustness. The proposed method is applied to a Zhejiang COVID-19 dataset, and it is found that age and gender statistically have no effect on the incubation period of COVID-19, which indicates that the quarantine measure currently in operation is reasonable. [ABSTRACT FROM AUTHOR]
The performance of units in the same batch can exhibit considerable heterogeneity due to the variation in the raw materials and fluctuation in the manufacturing process. For products suffering performance degradation ...
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The performance of units in the same batch can exhibit considerable heterogeneity due to the variation in the raw materials and fluctuation in the manufacturing process. For products suffering performance degradation in their use, such heterogeneity often results in an increase in the dispersion of the degradation paths of units in a population. The degradation rate of products can be unit-specific and often treated as random effects. This paper develops a novel random-effects Wiener process model to account for the unit-to-unit heterogeneity in the degradation, where the generalized inverse Gaussian (GIG) distribution is used to model the unit-specific degradation rate. The GIG distribution is a very general distribution with broad applications, which includes the inverse Gaussian (IG) distribution and the Gamma distribution as special cases. We investigate the model properties and develop an expectation maximization (em) algorithm for parameter estimation. By comparing the proposed model with existing models on two real degradation datasets of the infrared LEDs and the GaAs lasers, we show that the proposed model is quite effective for degradation modeling with heterogeneous rates.
In this article, we aimed to calculate the value at risk (VaR), which is one of the financial risk calculation methods, by using mixture of two different distributions when the financial data does not fit the normal d...
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In this article, we aimed to calculate the value at risk (VaR), which is one of the financial risk calculation methods, by using mixture of two different distributions when the financial data does not fit the normal distribution. The normal-logDagum distribution consisting of mixture of Normal and log-Dagum distributions is proposed to calculate the VaR for non-normal financial data in the study. The expected-maximization (em) algorithm for the maximum likelihood estimates of the parameters of normal-logDagum was defined. In application, the stocks of bank and telecommunication companies were examined. VaR values obtained with different distributions are compared numerically. As a result of the comparison, it was seen that the modeling based on normal-logDagum distribution is more successful in the statistical modeling of financial data.
The problem of parameter estimation for nonlinear state-space models is addressed using the expectation-maximisation algorithm. Model states and parameters are iteratively estimated using cubature Kalman smoothing and...
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The problem of parameter estimation for nonlinear state-space models is addressed using the expectation-maximisation algorithm. Model states and parameters are iteratively estimated using cubature Kalman smoothing and maximum a posteriori estimation. A modification to this technique is proposed by weighting measurement samples so the algorithm equally tries to approximate all system dynamics, even those poorly represented in the measurements. The method is applied to parameter estimation of a vehicle dynamics model.
This article introduces a spatial mixture model for the modeling and clustering of georeferenced data. In this model, the spatial dependence of the data is taken into account through the mixture weights, which are mod...
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ISBN:
(纸本)9783030604707;9783030604691
This article introduces a spatial mixture model for the modeling and clustering of georeferenced data. In this model, the spatial dependence of the data is taken into account through the mixture weights, which are modeled by logistic transformations of spatial coordinates. In this way, the observations are supposed to be independent but not identically distributed, their dependence being transferred to the parameters of these logistic functions. A specific em algorithm is used for parameter estimation via the maximum likelihood method, which incorporates a Newton-Raphson algorithm for estimating the logistic functions coefficients. The experiments, carried out on synthetic images, give encouraging results in terms of segmentation accuracy.
The motivation for this article comes from our development of soft sensors for chemical processes where several challenges are encountered. For example, quality variables in chemical processes are often measured off-l...
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The motivation for this article comes from our development of soft sensors for chemical processes where several challenges are encountered. For example, quality variables in chemical processes are often measured off-line through laboratory analysis. Collection of samples and subsequent analyses inevitably introduce uncertain time delays associated with the irregularly sampled quality variables, which add significant difficulty in identification of process with multirate (MR) data. Considering the MR system with random sampling delays described by a finite impulse response (FIR) model, an Expectation-Maximization (em)-based algorithm to estimate its parameters along with the time delays is developed. Based on the identified FIR model, two algorithms are proposed to recover the approximate output error (OE) or transfer function model. Two simulation examples as well as a pilot-scale experiment are provided to illustrate the effectiveness of the proposed methods. (c) 2013 American Institute of Chemical Engineers AIChE J, 59: 4124-4132, 2013
Cure rate models have been developed for the analysis of time to event data with cured fraction. In this paper we propose a proportional hazards competing risks regression model for the analysis of survival data with ...
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Cure rate models have been developed for the analysis of time to event data with cured fraction. In this paper we propose a proportional hazards competing risks regression model for the analysis of survival data with cured fraction. Estimation of the proposed model parameters is done by maximum likelihood method via em algorithm. Simulation work is carried out to examine influence of sample size on bias and variability of estimators. We applied the model to real life time data.
Hidden Markov models (HMMs) are widely used for modeling multivariate time series data. However, all collected data is not always useful for distinguishing between states. In these situations, feature selection should...
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ISBN:
(纸本)9783030295165;9783030295158
Hidden Markov models (HMMs) are widely used for modeling multivariate time series data. However, all collected data is not always useful for distinguishing between states. In these situations, feature selection should be implemented to save the expense of collecting and processing low utility data. Feature selection for HMMs has been studied but most existing methods assume that the observed data follows a Gaussian distribution. In this paper, a method for simultaneously estimating parameters and selecting features for an HMM with discrete observations is presented. The presented method is an extension of the feature saliency HMM which was originally developed to incorporate feature cost into the feature selection process. Expectation-maximization algorithms are derived for features following a Poisson distribution and features following a discrete non-parametric distribution. The algorithms are evaluated on synthetic data sets and a real-world event detection data set that is composed of both Poisson and non-parametric features.
Recently, Asimit et al. have used an em algorithm to estimate the parameters of Marshall-Olkin bivariate Pareto distribution with location and scale. The distribution has seven parameters. We describe different other ...
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Recently, Asimit et al. have used an em algorithm to estimate the parameters of Marshall-Olkin bivariate Pareto distribution with location and scale. The distribution has seven parameters. We describe different other variations of the em algorithm. Numerical simulation is performed to identify the best algorithm among them. A real-life data analysis is also shown for illustrative purposes.
This paper investigates a parameter estimation problem for batch processes through the maximum likelihood method. In batch processes, the initial state usually relates to the states of previous batches. The proposed a...
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This paper investigates a parameter estimation problem for batch processes through the maximum likelihood method. In batch processes, the initial state usually relates to the states of previous batches. The proposed algorithm takes batch-to-batch correlations into account by employing an initial state transition equation to model the dynamics along the batch dimension. By treating the unmeasured states and the parameters as hidden variables, the maximum likelihood estimation is accomplished through the expectation-maximization (em) algorithm, where the smoothing for the terminal state and the filtering for the initial state are specially considered. Due to the nonlinearity and non-Gaussianity in the state space model, particle filtering methods are employed for the implementation of filtering and smoothing. Through alternating between the expectation step and the maximization step, the unknown parameters along with states are estimated. Simulation examples demonstrate the proposed estimation approach. (C) 2013 Elsevier Ltd. All rights reserved.
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