This paper is concerned with the identification problem of the Markov jump autoregressive exogenous system with an unknown time delay. The considered problem is solved using the expectation-maximization algorithm, whi...
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This paper is concerned with the identification problem of the Markov jump autoregressive exogenous system with an unknown time delay. The considered problem is solved using the expectation-maximization algorithm, which estimates the parameters of local models, Markov transition probabilities, and time delay simultaneously. A numerical example and a simulated continuous fermentation reactor example are given to illustrate the capability of the proposed method. It shows that the influences of time delay during identification can be overcome by the proposed algorithm effectively.
Effectively solving the label switching problem is critical for both Bayesian and Frequentist mixture model analyses. In this article, a new relabeling method is proposed by extending a recently developed modal cluste...
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Effectively solving the label switching problem is critical for both Bayesian and Frequentist mixture model analyses. In this article, a new relabeling method is proposed by extending a recently developed modal clustering algorithm. First, the posterior distribution is estimated by a kernel density from permuted MCMC or bootstrap samples of parameters. Second, a modal em algorithm is used to find the m! symmetric modes of the KDE. Finally, samples that ascend to the same mode are assigned the same label. Simulations and real data applications demonstrate that the new method provides more accurate estimates than many existing relabeling methods.
In this article, a non-iterative posterior sampling algorithm for linear quantile regression model based on the asymmetric Laplace distribution is proposed. The algorithm combines the inverse Bayes formulae, sampling/...
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In this article, a non-iterative posterior sampling algorithm for linear quantile regression model based on the asymmetric Laplace distribution is proposed. The algorithm combines the inverse Bayes formulae, sampling/importance resampling, and the expectation maximization algorithm to obtain independently and identically distributed samples approximately from the observed posterior distribution, which eliminates the convergence problems in the iterative Gibbs sampling and overcomes the difficulty in evaluating the standard deviance in the em algorithm. The numeric results in simulations and application to the classical Engel data show that the non-iterative sampling algorithm is more effective than the Gibbs sampling and em algorithm.
With the huge influx of various data nowadays, extracting knowledge from them has become an interesting but tedious task among data scientists, particularly when the data come in heterogeneous form and have missing in...
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With the huge influx of various data nowadays, extracting knowledge from them has become an interesting but tedious task among data scientists, particularly when the data come in heterogeneous form and have missing information. Many data completion techniques had been introduced, especially in the advent of kernel methods-a way in which one can represent heterogeneous data sets into a single form: as kernel matrices. However, among the many data completion techniques available in the literature, studies about mutually completing several incomplete kernel matrices have not been given much attention yet. In this paper, we present a new method, called Mutual Kernel Matrix Completion (MKMC) algorithm, that tackles this problem of mutually inferring the missing entries of multiple kernel matrices by combining the notions of data fusion and kernel matrix completion, applied on biological data sets to be used for classification task. We first introduced an objective function that will be minimized by exploiting the em algorithm, which in turn results to an estimate of the missing entries of the kernel matrices involved. The completed kernel matrices are then combined to produce a model matrix that can be used to further improve the obtained estimates. An interesting result of our study is that the E-step and the M-step are given in closed form, which makes our algorithm efficient in terms of time and memory. After completion, the ( completed) kernel matrices are then used to train an SVM classifier to test how well the relationships among the entries are preserved. Our empirical results show that the proposed algorithm bested the traditional completion techniques in preserving the relationships among the data points, and in accurately recovering the missing kernel matrix entries. By far, MKMC offers a promising solution to the problem of mutual estimation of a number of relevant incomplete kernel matrices.
Multiple image group cosegmentation (MGC) aims at segmenting common object from multiple group of images, which is a new cosegmentation research topic. The existing MGC methods formulate MGC as label assignment proble...
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Multiple image group cosegmentation (MGC) aims at segmenting common object from multiple group of images, which is a new cosegmentation research topic. The existing MGC methods formulate MGC as label assignment problem (Markov Random Field framework), which is observed to be sensitive to parameter setting. Meanwhile, it is also observed that large object variations and complicated backgrounds dramatically decrease the existing MGC performance. To this end, we propose a new object proposal based MGC model, with the aim of avoiding tedious parameter setting, and improving MGC performance. Our main idea is to formulate MGC as new region proposal selection task. A new energy function in term of proposal is proposed. Two aspects such as the foreground consistency within each single image group, and the group consistency among image groups are considered. The energy minimization method is designed in em framework. Two steps such as the loop belief propagation and foreground propagation are iteratively implemented for the minimization. We verify our method on ICoseg dataset. Six existing cosegmentation methods are used for the comparison. The experimental results demonstrate that the proposed method can not only improve MGC performance in terms of larger IOU values, but is also robust to the parameter setting.
This paper proposes a methodology that accounts for the selection effect due to non-random entry in duration models using latent-class models. A mixed proportional hazard model with continuous finite mixture unobserve...
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This paper proposes a methodology that accounts for the selection effect due to non-random entry in duration models using latent-class models. A mixed proportional hazard model with continuous finite mixture unobserved heterogeneity (MPH-CFM) is introduced to correct for the potential bias induced by the selection effect. Conditions for identification, consistency, and asymptotic normality of the MPH-CFM are provided. The estimator is used to investigate the duration of new entrant Canadian manufacturing firms. For the current application, the MPH-CFM is compared with alternative duration models and found to be superior. empirically, the results indicate that there are two classes of firms. Class I starts with high hazard and decreases non-monotonically while Class II has a negligible hazard. These empirical results can be used to understand alternative models of firm dynamics. Copyright (c) 2017 John Wiley & Sons, Ltd.
This paper addresses the estimation for the unknown scale parameter of the half-logistic distribution based on a Type-I progressively hybrid censoring scheme. We evaluate the maximum likelihood estimate (MLE) via nume...
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This paper addresses the estimation for the unknown scale parameter of the half-logistic distribution based on a Type-I progressively hybrid censoring scheme. We evaluate the maximum likelihood estimate (MLE) via numerical method, and em algorithm, and also the approximate maximum likelihood estimate (AMLE). We use a modified acceptance rejection method to obtain the Bayes estimate and corresponding highest posterior confidence intervals. We perform Monte Carlo simulations to compare the performances of the different methods, and we analyze one dataset for illustrative purposes.
In this article, we propose mixtures of skew Laplace normal (SLN) distributions to model both skewness and heavy-tailedness in the neous data set as an alternative to mixtures of skew Student-t-normal (STN) distributi...
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In this article, we propose mixtures of skew Laplace normal (SLN) distributions to model both skewness and heavy-tailedness in the neous data set as an alternative to mixtures of skew Student-t-normal (STN) distributions. We give the expectation-maximization (em) algorithm to obtain the maximum likelihood (ML) estimators for the parameters of interest. We also analyze the mixture regression model based on the SLN distribution and provide the ML estimators of the parameters using the em algorithm. The performance of the proposed mixture model is illustrated by a simulation study and two real data examples.
In this article, the profile maximal likelihood estimate (PMLE) is proposed for non linear mixed models (NLMMs) with longitudinal data where the variance components are estimated by the expectation-maximization (em) a...
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In this article, the profile maximal likelihood estimate (PMLE) is proposed for non linear mixed models (NLMMs) with longitudinal data where the variance components are estimated by the expectation-maximization (em) algorithm. Strong consistency and the asymptotic normality of the estimators are derived. A simulation study is conducted where the performance of the PLME and the Fishing scoring estimate (FSE) in literatures are compared. Moreover, a real data is also analyzed to investigate the empirical performance of the procedure.
In this article, a new form of multivariate slash distribution is introduced and some statistical properties are derived. In order to illustrate the advantage of this distribution over the existing generalized multiva...
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In this article, a new form of multivariate slash distribution is introduced and some statistical properties are derived. In order to illustrate the advantage of this distribution over the existing generalized multivariate slash distribution in the literature, it is applied to a real data set.
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