In this paper, we propose an image completion algorithm based on dense correspondence between the input image and an exemplar image retrieved from the Internet. Contrary to traditional methods which register two image...
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In this paper, we propose an image completion algorithm based on dense correspondence between the input image and an exemplar image retrieved from the Internet. Contrary to traditional methods which register two images according to sparse correspondence, in this paper, we propose a hierarchical PatchMatch method that progressively estimates a dense correspondence, which is able to capture small deformations between images. The estimated dense correspondence has usually large occlusion areas that correspond to the regions to be completed. A nearest neighbor field (NNF) interpolation algorithm interpolates a smooth and accurate NNF over the occluded region. Given the calculated NNF, the correct image content from the exemplar image is transferred to the input image. Finally, as there could be a color difference between the completed content and the input image, a color correction algorithm is applied to remove the visual artifacts. Numerical results show that our proposed image completion method can achieve photo realistic image completion results.
Jammalamadaka and Mangalam introduced middle censoring which refers to data arising in situations, where the exact lifetime becomes unobservable if it falls within a random censoring interval. In the present article, ...
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Jammalamadaka and Mangalam introduced middle censoring which refers to data arising in situations, where the exact lifetime becomes unobservable if it falls within a random censoring interval. In the present article, we propose an additive risks regression model for a lifetime data subject to middle censoring, where the lifetimes are assumed to follow exponentiated exponential distribution. The regression parameters are estimated using the Expectation-Maximization algorithm. Asymptotic normality of the estimator is proposed. We report a simulation study to assess the finite sample properties of the estimator. We then analyze a real-life data on survival times of larynx cancer patients studied by Karduan.
In this article, by using the constant and random selection matrices, several properties of the maximum likelihood (ML) estimates and the ML estimator of a normal distribution with missing data are derived. The consta...
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In this article, by using the constant and random selection matrices, several properties of the maximum likelihood (ML) estimates and the ML estimator of a normal distribution with missing data are derived. The constant selection matrix allows us to obtain an explicit form of the ML estimates and the exact relationship between the em algorithm and the score function. The random selection matrix allows us to clarify how the missing-data mechanism works in the proof of the consistency of the ML estimator, to derive the asymptotic properties of the sequence by the em algorithm, and to derive the information matrix.
In complex indoor environments, the censoring, dropping, and multi-component problems may present in the observable data. This is due to the attenuation of signals, the unexpected operation of equipments, and the chan...
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In complex indoor environments, the censoring, dropping, and multi-component problems may present in the observable data. This is due to the attenuation of signals, the unexpected operation of equipments, and the changing surrounding environment. Censoring refers to the fact that sensors on portable devices are unable to measure Received Signal Strength Index (RSSI) values below a certain threshold, for example, −100 dBm with typical smart phones. Dropping means that, occasionally, RSSI measurements of Wifi access points are not available, although their value is clearly above the censoring threshold. The multi-component problem occurs when the measured data varies due to obstacles as well as user directions; doors closed or open; and so forth. Taking these problems into consideration, this paper proposes a novel approach to enhance the performance of the Wifi Fingerprinting based Indoor Positioning System (WF-IPS). The proposed method is verified through simulated data and real field data. The experimental results show that our proposal outperforms the other state-of-the-art WF-IPS approach both in positioning accuracy and computational cost.
In this article, we propose and study a new class of semiparametric mixture of regression models, where the mixing proportions and variances are constants, but the component regression functions are smooth functions o...
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In this article, we propose and study a new class of semiparametric mixture of regression models, where the mixing proportions and variances are constants, but the component regression functions are smooth functions of a covariate. A one-step backfitting estimate and two em-type algorithms have been proposed to achieve the optimal convergence rate for both the global parameters and the nonparametric regression functions. We derive the asymptotic property of the proposed estimates and show that both the proposed em-type algorithms preserve the asymptotic ascent property. A generalized likelihood ratio test is proposed for semiparametric inferences. We prove that the test follows an asymptotic chi(2) distribution under the null hypothesis, which is independent of the nuisance parameters. A simulation study and two real data examples have been conducted to demonstrate the finite sample performance of the proposed model.
作者：
Bashir, FarajWei, Hua-LiangUniv Sheffield
Dept Automat Control & Syst Engn Mapping St Sheffield S1 4DT S Yorkshire England Univ Sheffield
Dept Automat Control & Syst Engn Mapping St Sheffield S1 3JD S Yorkshire England Univ Sheffield
INSIGNEO Inst Silico Med Mapping St Sheffield S1 3JD S Yorkshire England
Imputing missing data from a multivariate time series dataset remains a challenging problem. There is an abundance of research on using various techniques to impute missing, biased, or corrupted values to a dataset. W...
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Imputing missing data from a multivariate time series dataset remains a challenging problem. There is an abundance of research on using various techniques to impute missing, biased, or corrupted values to a dataset. While a great amount of work has been done in this field, most imputing methodologies are centered about a specific application, typically involving static data analysis and simple time series modelling. However, these approaches fall short of desired goals when the data originates from a multivariate time series. The objective of this paper is to introduce a new algorithm for handling missing data from multivariate time series datasets. This new approach is based on a vector autoregressive (VAR) model by combining an expectation and minimization (em) algorithm with the prediction error minimization (Pem) method. The new algorithm is called a vector autoregressive imputation method (VAR-IM). A description of the algorithm is presented and a case study was accomplished using the VAR-IM. The case study was applied to a real-world data set involving electrocardiogram (ECG) data. The VAR-IM method was compared with both traditional methods list wise deletion and linear regression substitution;and modern methods Multivariate Auto-Regressive State-Space (MARSS) and expectation maximization algorithm (em). Generally, the VAR-IM method achieved significant improvement of the imputation tasks as compared with the other two methods. Although an improvement, a summary of the limitations and restrictions when using VAR-IM is presented. (c) 2017 Elsevier B.V. All rights reserved.
This paper is concerned with learning of mixture regression models for individuals that are measured repeatedly. The adjective "unsupervised" implies that the number of mixing components is unknown and has t...
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This paper is concerned with learning of mixture regression models for individuals that are measured repeatedly. The adjective "unsupervised" implies that the number of mixing components is unknown and has to be determined, ideally by data driven tools. For this purpose, a novel penalized method is proposed to simultaneously select the number of mixing components and to estimate the mixture proportions and unknown parameters in the models. The proposed method is capable of handling both continuous and discrete responses by only requiring the first two moment conditions of the model distribution. It is shown to be consistent in both selecting the number of components and estimating the mixture proportions and unknown regression parameters. Further, a modified em algorithm is developed to seamlessly integrate model selection and estimation. Simulation studies are conducted to evaluate the finite sample performance of the proposed procedure. And it is further illustrated via an analysis of a primary biliary cirrhosis data set. (C) 2018 Elsevier B.V. All rights reserved.
In applications of multivariate finite mixture models, estimating the number of unknown components is often difficult. We propose a bootstrap information criterion, whereby we calculate the expected log-likelihood at ...
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In applications of multivariate finite mixture models, estimating the number of unknown components is often difficult. We propose a bootstrap information criterion, whereby we calculate the expected log-likelihood at maximum a posteriori estimates for model selection. Accurate estimation using the bootstrap requires a large number of bootstrap replicates. We accelerate this computation by employing parallel processing with graphics processing units (GPUs) on the Compute Unified Device Architecture (CUDA) platform. We conducted a runtime comparison of CUDA algorithms between implementation on the GPU and that on a CPU. The results showed significant performance gains in the proposed CUDA algorithms over multithread CPUs.
In this article, we consider the problem of estimation and prediction on unknown parameters of a Lomax distribution when the lifetime data are observed in the presence of progressively type-I hybrid censoring scheme. ...
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In this article, we consider the problem of estimation and prediction on unknown parameters of a Lomax distribution when the lifetime data are observed in the presence of progressively type-I hybrid censoring scheme. In the classical scenario, the Expectation Maximization (em) algorithm is utilized to derive the maximum likelihood estimates (MLEs) for the unknown parameters and associated confidence intervals. Under the Bayesian framework, the point estimates of unknown parameters with respect to different symmetric, asymmetric and balanced loss functions are obtained using Tierney-Kadane's approximation and Markov Chain Monte Carlo (MCMC) technique. Also, the highest posterior density (HPD) credible intervals for the parameters are reckoned using importance sampling procedure. Simulation experiments are performed to compare the different proposed methods. Further, the predictive estimates of censored observations and the corresponding prediction intervals are also provided. One real-life data example is presented to illustrate the derived results. (C) 2018 Elsevier B.V. All rights reserved.
In this paper we consider estimation of unknown parameters of an inverted exponentiated Rayleigh distribution when it is known that data are hybrid Type I censored. The maximum likelihood and Bayes estimates are deriv...
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In this paper we consider estimation of unknown parameters of an inverted exponentiated Rayleigh distribution when it is known that data are hybrid Type I censored. The maximum likelihood and Bayes estimates are derived. In sequel interval estimates are also constructed. We further consider one- and two-sample prediction of future observations and also obtain prediction intervals. The performance of proposed methods of estimation and prediction is studied using simulations and an illustrative example is discussed in support of the suggested methods.
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