In this paper, we analyze an M/PH/1 queuing system with optional re-service. In this queueing system the interarrival time distribution is exponential with mean 1/lambda, service time and re-service time distribution ...
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In this paper, we analyze an M/PH/1 queuing system with optional re-service. In this queueing system the interarrival time distribution is exponential with mean 1/lambda, service time and re-service time distribution follows continuous time phase-type distribution and each customer will be re-serviced with probability 'p'. We have obtained the expressions of various system performance measures and maximum likelihood method using E-M algorithm and Bayesian procedure based on MCMC method were proposed to estimate system parameters. Numerical illustration of various special cases of the queueing system was carried out using simulated data sets.
Genomic data arising from a genome-wide association study (GWAS) are often not only of large-scale, but also incomplete. A specific form of their incompleteness is missing val-ues with non-ignorable missingness mechan...
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Genomic data arising from a genome-wide association study (GWAS) are often not only of large-scale, but also incomplete. A specific form of their incompleteness is missing val-ues with non-ignorable missingness mechanism. The intrinsic complications of genomic data present significant challenges in developing an effective and efficient procedure of phenotype-genotype association analysis by a statistical variable selection approach. In this paper we develop a coherent procedure of categorical phenotype-genotype associa-tion analysis, in the presence of missing values with non-ignorable missingness mecha-nism in genomic data. It is developed by integrating the statistical learning methods of random forest for variable selection, joint weighted logistic ridge regression with em algo-rithm for missing data imputation, and linear statistical hypothesis testing for determining the missingness mechanism. Two simulated genomic datasets are used to undertake the phenotype-genotype association analysis by the proposed procedure, with the performance validated. The proposed procedure is then applied to analyze a real data set from breast cancer GWAS.& COPY;2023 The Author(s). Published by Elsevier Inc. This is an open access article under the CC BY-NC-ND license ( http://***/licenses/by-nc-nd/4.0/ )
Hashing has achieved great success in multimedia retrieval due to its high computing efficiency and low storage cost. Recently, contrastive-learning-based hashing methods have achieved decent retrieval performance in ...
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Hashing has achieved great success in multimedia retrieval due to its high computing efficiency and low storage cost. Recently, contrastive-learning-based hashing methods have achieved decent retrieval performance in label-free scenarios by learning distortion-invariant representations with Siamese networks. Their learning principle, i.e., instance discrimination , maximizes the correlation between self-augmented views and treats all others as negative samples. However, it may learn with false negative samples that are naturally similar, resulting in biased hash learning. To bridge this flaw, we reveal the between-instance similarity of naturally similar samples by exploring the latent structure of the training data. As a result, we propose the Deep Debiased Contrastive Hashing ( DDCH) algorithm, using the neighborhood discovery module to explore the intrinsic similarity relationship that can help contrastive hashing reduce false negatives for superior discriminatory ability. Furthermore, we elucidate the rationale for incorporating the module into the contrastive hashing framework and explain our hashing process from an Expectation-Maximization (em) perspective. Extensive experimental results on three benchmark image datasets demonstrate that DDCH significantly outperforms the state-of-the-art unsupervised hashing methods for image retrieval. (c) 2023 Elsevier Ltd. All rights reserved.
Audio segmentation is an essential problem in many audio signal processing tasks, which tries to segment an audio signal into homogeneous chunks. Rather than separately finding change points and computing similarities...
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ISBN:
(纸本)9781467369985
Audio segmentation is an essential problem in many audio signal processing tasks, which tries to segment an audio signal into homogeneous chunks. Rather than separately finding change points and computing similarities between segments, we focus on joint segmentation and clustering, using the framework of hidden Markov and semi-Markov models. We introduce a new incremental em algorithm for hidden Markov models (HMMs) and show that it compares favorably to existing online em algorithms for HMMs. We present results for real-time segmentation of musical notes and acoustic scenes.
We consider classical and Bayesian procedures for estimating the parameters of the power Lindley distribution based on hybrid censored data. By unifying the likelihood function under the hybrid censoring scheme, we co...
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We consider classical and Bayesian procedures for estimating the parameters of the power Lindley distribution based on hybrid censored data. By unifying the likelihood function under the hybrid censoring scheme, we consider the maximum likelihood estimators (MLEs) and develop an expectation-maximization algorithm to find the MLEs. We adopt the asymptotic distribution of the MLEs to construct approximate confidence intervals. We then study Bayesian estimators and the credible intervals of the parameters under appropriate choices of prior distributions. Lindley's approximation method and Markov chain Monte Carlo sampling algorithm are also provided to evaluate these Bayesian estimators. The finite sample performances of these estimation methods are investigated using simulation studies and a real data example.
The exponential model is the simplest among all lifetime distribution models and it possesses a constant failure rate. Here we propose a new class of lifetime distribution having decreasing failure rate which we devel...
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The exponential model is the simplest among all lifetime distribution models and it possesses a constant failure rate. Here we propose a new class of lifetime distribution having decreasing failure rate which we developed through compounding exponential distribution with the positive hyper-Poisson distribution. We investigate some of its statistical properties and employed various methods of estimation for estimating the parameters of the distribution along with certain test procedures. All the procedures discussed in the paper are illustrated with the help of real-life data sets. Further, a brief simulation study is conducted for examining the performance of the estimators of the parameters of the distribution.
BackgroundFor detecting genotype-phenotype association from case-control single nucleotide polymorphism (SNP) data, one class of methods relies on testing each genomic variant site individually. However, this approach...
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BackgroundFor detecting genotype-phenotype association from case-control single nucleotide polymorphism (SNP) data, one class of methods relies on testing each genomic variant site individually. However, this approach ignores the tendency for associated variant sites to be spatially clustered instead of uniformly distributed along the genome. Therefore, a more recent class of methods looks for blocks of influential variant sites. Unfortunately, existing such methods either assume prior knowledge of the blocks, or rely on ad hoc moving windows. A principled method is needed to automatically detect genomic variant blocks which are associated with the *** this paper, we introduce an automatic block-wise Genome-Wide Association Study (GWAS) method based on Hidden Markov model. Using case-control SNP data as input, our method detects the number of blocks associated with the phenotype and the locations of the blocks. Correspondingly, the minor allele of each variate site will be classified as having negative influence, no influence or positive influence on the phenotype. We evaluated our method using both datasets simulated from our model and datasets from a block model different from ours, and compared the performance with other methods. These included both simple methods based on the Fisher's exact test, applied site-by-site, as well as more complex methods built into the recent Zoom-Focus algorithm. Across all simulations, our method consistently outperformed the *** its demonstrated better performance, we expect our algorithm for detecting influential variant sites may help find more accurate signals across a wide range of case-control GWAS.
The conventional Cox proportional hazards (PH) model typically assumes fully observed predictors and constant regression coefficients. However, some predictors are latent variables, each of which must be characterized...
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The conventional Cox proportional hazards (PH) model typically assumes fully observed predictors and constant regression coefficients. However, some predictors are latent variables, each of which must be characterized by multiple observed indicators from various perspectives. Moreover, the predictor effects may vary with time in practice. Accommodating such latent variables and identifying temporal covariate effects are frequently of primary interest. This study proposes a generalized structural equation model to investigate the temporal effects of observed and latent risk factors on the hazards of interest. The proposed model comprises a confirmatory factor analysis model as the measurement equation and a varying-coefficient PH model with observed and latent predictors as the structural equation. A hybrid procedure that combines the expectation-maximization (em) algorithm and the corrected estimating equation approach is developed to estimate unknown parameters and coefficient functions. Simulation studies demonstrate the satisfactory performance of the proposed method. An application to a health survey study reveals insights into risk factors for elders' life expectancy.
In this article, a variable selection method for the finite mixture of location regression (FMLR) and the finite mixture of mean regression (FMMR) models with a skew-normal error are discussed. The univariate skew-nor...
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In this article, a variable selection method for the finite mixture of location regression (FMLR) and the finite mixture of mean regression (FMMR) models with a skew-normal error are discussed. The univariate skew-normal distribution was introduced by Sahu et al. will be used in this work, which is attractive because estimation of the skewness parameter does not present the same degree of difficulty as in the case with Azzalini one and, moreover, it allows easy implementation of the em algorithm. A penalized likelihood approach for variable selection in FMLR and FMMR models was introduced in this article. With a data-adaptive method for selecting tuning parameters, we establish the theoretical properties of our procedure, including consistency in variable selection, the oracle property in estimation. The em algorithm facilitated by Gauss-Newton method for efficient numerical computations are developed. Simulation studies and a real data set are used to illustrate the proposed methodologies.
Event history data commonly occur in many areas and a great deal of literature on their analysis has been established. However, most of the existing methods apply only to a single type of event history data. Recently,...
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Event history data commonly occur in many areas and a great deal of literature on their analysis has been established. However, most of the existing methods apply only to a single type of event history data. Recently, several authors have discussed the analysis of mixed types of event history data and the existence of dependent observation processes is another issue that one often has to deal with in the analysis of event history data. This paper discusses regression analysis of mixed panel count data with dependent observation processes, which has not been addressed in the literature, and for the problem, an approximate likelihood estimation approach is proposed. For the implementation, an em algorithm is developed and the proposed estimators are shown to be consistent and asymptotically normal. An extensive simulation study is performed to assess the performance of the proposed approach and indicates that it works well in practical situations. An application to a set of real data is provided.
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