This study deals with semi-blind (SB) channel estimation of multiple-input multiple-output orthogonal frequency-division multiplexing (MIMO-OFDM) system using maximum likelihood (ML) technique. For the ML cost optimis...
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This study deals with semi-blind (SB) channel estimation of multiple-input multiple-output orthogonal frequency-division multiplexing (MIMO-OFDM) system using maximum likelihood (ML) technique. For the ML cost optimisation function, new expectation maximisation (em) algorithms for the channel taps estimation are introduced. Different approximation/simplification approaches are proposed for the algorithm's computational cost reduction. The first approach consists of decomposing the MIMO-OFDM system into parallel multiple-input single-output OFDM systems. The em algorithm is then applied to estimate the MIMO channel in a parallel way. The second approach takes advantage of the SB context to reduce the em cost from exponential to linear complexity by reducing the size of the search space. Finally, the last proposed approach uses a parallel interference cancellation technique to decompose the MIMO-OFDM system into several single-input multiple-output OFDM systems. The latter are identified in a parallel scheme and with a reduced complexity. The performance of the proposed approaches are discussed, assessed through numerical experiments and compared with respect to the Cramer Rao Bound and to other em-based solutions reported in the literature.
Doubly truncated (DT) data occur when event times are observed only if they fall within subject-specific, possibly random, intervals. In this article, we consider conditional maximum likelihood estimation for the regr...
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Doubly truncated (DT) data occur when event times are observed only if they fall within subject-specific, possibly random, intervals. In this article, we consider conditional maximum likelihood estimation for the regression parameters of the Cox–Aalen model with DT data. Based on gradient projection method (GPM), we propose computational algorithms for obtaining the conditional maximum likelihood estimator (cMLE). The proposed cMLE is shown to be consistent and asymptotically normal. Simulation studies show that the cMLE performs well in finite samples.
In survival analysis, it often happens that a certain fraction of the subjects under study never experience the event of interest, that is, they are considered "cured." In the presence of covariates, a commo...
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In survival analysis, it often happens that a certain fraction of the subjects under study never experience the event of interest, that is, they are considered "cured." In the presence of covariates, a common model for this type of data is the mixture cure model, which assumes that the population consists of two subpopulations, namely the cured and the non-cured ones, and it writes the survival function of the whole population given a set of covariates as a mixture of the survival function of the cured subjects (which equals one), and the survival function of the non-cured ones. In the literature, one usually assumes that the mixing probabilities follow a logistic model. This is, however, a strong modeling assumption, which might not be met in practice. Therefore, in order to have a flexible model which at the same time does not suffer from curse-of-dimensionality problems, we propose in this paper a single-index model for the mixing probabilities. For the survival function of the non-cured subjects we assume a Cox proportional hazards model. We estimate this model using a maximum likelihood approach. We also carry out a simulation study, in which we compare the estimators under the single-index model and under the logistic model for various model settings, and we apply the new model and estimation method on a breast cancer data set.
A robust mixture regression based on the M regression estimation method has already been proposed in literature. However, since the M-estimators are only robust against the outliers in response variables, the resultin...
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A robust mixture regression based on the M regression estimation method has already been proposed in literature. However, since the M-estimators are only robust against the outliers in response variables, the resulting mixture regression methods will not be robust against the outliers in explanatory variables (leverage points). In this paper, we propose a robust mixture regression procedure to handle the outliers and the leverage points, simultaneously. Our proposed mixture regression procedure is based on the GM regression estimation method. We give an em-type algorithm to compute estimates for the parameters of interest. We provide a simulation study and a real data example to assess the robustness performance of the proposed method against the outliers and the leverage points.
Stochastic block models (SBMs) have been playing an important role in modeling clusters or community structures of network data. But, it is incapable of handling several complex features ubiquitously exhibited in real...
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Stochastic block models (SBMs) have been playing an important role in modeling clusters or community structures of network data. But, it is incapable of handling several complex features ubiquitously exhibited in real-world networks, one of which is the power-law degree characteristic. To this end, we propose a new variant of SBM, termed power-law degree SBM (PLD-SBM), by introducing degree decay variables to explicitly encode the varying degree distribution over all nodes. With an exponential prior, it is proved that PLD-SBM approximately preserves the scale-free feature in real networks. In addition, from the inference of variational E-Step, PLD-SBM is indeed to correct the bias inherited in SBM with the introduced degree decay factors. Furthermore, experiments conducted on both synthetic networks and two real-world datasets including Adolescent Health Data and the political blogs network verify the effectiveness of the proposed model in terms of cluster prediction accuracies.
In this work, we study a class ofp-order non-negative integer-valued autoregressive (INAR(p)) processes, with innovations following zero-inflated (ZI) distributions called ZI-INAR(p) processes. Based on the em algorit...
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In this work, we study a class ofp-order non-negative integer-valued autoregressive (INAR(p)) processes, with innovations following zero-inflated (ZI) distributions called ZI-INAR(p) processes. Based on the em algorithm, we present an estimation procedure of parameters model. We also develop a regenerative bootstrap method to construct confidence intervals for the parameters as well as to estimate the forecasting distributions for future values. We discuss asymptotic properties of the regenerative bootstrap method. The performance of the proposed methods is evaluated considering the analysis of two simulation studies and a real dataset.
Joint estimation and identification to the linear system with unknown input(s) (UI, UIs) is critical in the control community as well as signal processing. In this paper we present the solution to the problem based on...
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Joint estimation and identification to the linear system with unknown input(s) (UI, UIs) is critical in the control community as well as signal processing. In this paper we present the solution to the problem based on the expectation-maximization (em) method to alternately estimate system states and identify the UIs. The dominant advantage of the proposed method is that we could handle the UI(s) in not only the system dynamics model but also the measurement model. Specifically we make the following contributions: (1) providing the rigorous mathematical definitions of the problem, (2) theoretically proving the existence and uniqueness of the solution to the joint estimation and identification problem, (3) presenting the theoretical proof of convergence and effectiveness of the em-based algorithm, and (4) supplying with sufficiently insightful explanations for the mathematical derivation. (C) 2019 Elsevier B.V. All rights reserved.
The cumulative exposure model (Cem) is a commonly used statistical model utilized to analyze data from a step-stress accelerated life testing which is a special class of accelerated life testing (ALT). In practice, re...
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The cumulative exposure model (Cem) is a commonly used statistical model utilized to analyze data from a step-stress accelerated life testing which is a special class of accelerated life testing (ALT). In practice, researchers conduct ALT to: (1) determine the effects of extreme levels of stress factors (e.g., temperature) on the life distribution, and (2) to gain information on the parameters of the life distribution more rapidly than under normal operating (or environmental) conditions. In literature, researchers assume that the Cem is from well-known distributions, such as the Weibull family. This study, on the other hand, considers a p-step-stress model with q stress factors from the two-parameter Birnbaum-Saunders distribution when there is a time constraint on the duration of the experiment. In this comparison paper, we consider different frameworks to numerically compute the point estimation for the unknown parameters of the Cem using the maximum likelihood theory. Each framework implements at least one optimization method;therefore, numerical examples and extensive Monte Carlo simulations are considered to compare and numerically examine the performance of the considered estimation frameworks.
Variable selection in finite mixture of regression (FMR) models is frequently used in statistical modeling. The majority of applications of variable selection in FMR models use a normal distribution for regression err...
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Variable selection in finite mixture of regression (FMR) models is frequently used in statistical modeling. The majority of applications of variable selection in FMR models use a normal distribution for regression error. Such assumptions are unsuitable for a set of data containing a group or groups of observations with heavy tails and outliers. In this paper, we introduce a robust variable selection procedure for FMR models using the t distribution. With appropriate selection of the tuning parameters, the consistency and the oracle property of the regularized estimators are established. To estimate the parameters of the model, we develop an em algorithm for numerical computations and a method for selecting tuning parameters adaptively. The parameter estimation performance of the proposed model is evaluated through simulation studies. The application of the proposed model is illustrated by analyzing a real data set.
In this paper, an attempt has been made to improve information of an image by fusion technique, so it may be more effectively used. Full polarimetric images of ALOS PALSAR are fused by exploiting the nice properties o...
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ISBN:
(纸本)9781479915736;9781479921751
In this paper, an attempt has been made to improve information of an image by fusion technique, so it may be more effectively used. Full polarimetric images of ALOS PALSAR are fused by exploiting the nice properties of Expectation Maximization (em) algorithm. Maximum likelihood classifier is applied on the composite unfused singlet and fused doublet images and results are compared on the basis of producer, user, overall accuracies and Kappa Coefficient.
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