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A semiparametric isotonic regression model for skewed distributions with application to DNA-RNA-protein analysis

BIOMETRICS 2022年第4期78卷 1464-1474页

作者： Wang, Chenguang Yuan, Ao Cope, Leslie Qin, Jing Johns Hopkins Univ Sidney Kimmel Comprehens Canc Ctr Baltimore MD USA Georgetown Univ Dept Biostat Bioinformat & Biomath Washington DC USA NIAID Bethesda MD 20852 USA

In this paper, we propose a semiparametric regression model that is built upon an isotonic regression model with the assumption that the random error follows a skewed distribution. We develop an expectation-maximization algorithm for obtaining the maximum likelihood estimates of the model parameters, examine the asymptotic properties of the estimators, conduct simulation studies to explore the performance of the proposed model, and apply the method to evaluate the DNA-RNA-protein relationship and identify genes that are key factors in tumor progression.

关键词： expectation-maximization algorithm isotonic regression maximum likelihood estimation skew normal

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Statistical inference for masked interval data with Weibull distribution under simple step-stress test and tampered failure rate model

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PROCEEDINGS OF THE INSTITUTION OF MECHANICAL ENGINEERS PART O-JOURNAL OF RISK AND RELIABILITY 2017年第6期231卷 654-665页

作者： Azizi, Fariba Haghighi, Firoozeh Ghadiri, Elahe Torabi, Leila Univ Tehran Dept Math Stat & Comp Sci Tehran Iran

In this article, we consider the planning of simple step-stress life test in the presence of competing risks with the possibility of masked causes of failure and interval censoring. It is assumed that the competing risks have independent Weibull distribution with the common shape parameter, but different scale parameters and a tampered failure rate model is hold. Maximum likelihood estimates of the model parameters are obtained for both cases, with and without masking. An expectation-maximization algorithm is used to estimate maximum likelihood estimates based on the masked data. For the case without masking, the Fisher information matrix is derived, and the problem of choosing the optimal test plan is considered using variance-optimality as well as determinant-optimality criteria. Through Monte Carlo simulations, the precision of the estimates is assessed and the optimal test plans are compared. Finally, the results are illustrated with an example.

关键词： Competing risks determinant optimality expectation-maximization algorithm interval censoring masked data optimal plan tampered failure rate model variance optimality Weibull distribution

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An improved lossless image compression based arithmetic coding using mixture of non-parametric distributions

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MULTIMEDIA TOOLS AND APPLICATIONS 2015年第23期74卷 10605-10619页

作者： Masmoudi, Atef Puech, William Masmoudi, Afif Univ Sfax Sfax Preparatory Engn Inst Sfax Tunisia Univ Montpellier 2 LIRMM UMR CNRS 5506 F-34392 Montpellier 05 France Univ Sfax Fac Sci Sfax Lab Stat & Probabil Sfax Tunisia

In this paper, we propose a new approach for a block-based lossless image compression using finite mixture models and adaptive arithmetic coding. Conventional arithmetic encoders encode and decode images sample-by-sample in raster scan order. In addition, conventional arithmetic coding models provide the probability distribution for whole source symbols to be compressed or transmitted, including static and adaptive models. However, in the proposed scheme, an image is divided into non-overlapping blocks and then each block is encoded separately by using arithmetic coding. The proposed model provides a probability distribution for each block which is modeled by a mixture of non-parametric distributions by exploiting the high correlation between neighboring blocks. The expectation-maximization algorithm is used to find the maximum likelihood mixture parameters in order to maximize the arithmetic coding compression efficiency. The results of comparative experiments show that we provide significant improvements over the state-of-the-art lossless image compression standards and algorithms. In addition, experimental results show that the proposed compression algorithm beats JPEG-LS by 9.7 % when switching between pixel and prediction error domains.

关键词： Arithmetic coding Lossless compression Image compression Finite mixture model expectation-maximization algorithm Kullback-Leibler distance

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Logistic model for pattern inference of subway passenger flows based on fare collection and vehicle location data

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APPLIED MATHEMATICAL MODELLING 2024年 130卷 472-495页

作者： Li, Chunya Xiong, Shifeng Xiong, Hui Sun, Xuan Qin, Yong Shanghai Univ Engn Sci Sch Math Phys & Stat Shanghai 201620 Peoples R China Chinese Acad Sci Acad Math & Syst Sci NCMIS KLSC Beijing 100190 Peoples R China Univ Chinese Acad Sci Sch Math Sci Beijing 100049 Peoples R China Hainan Univ Sch Mech & Elect Engn Haikou 570228 Peoples R China Beijing Jiaotong Univ Sch Traff & Transportat Beijing 100044 Peoples R China Beijing Jiaotong Univ State Key Lab Rail Traff Control & Safety Beijing 100044 Peoples R China

With large volume of passengers boarding and alighting through subway platforms, the stations are getting crowded, resulting in drops in the level of service and safety concerns, especially for subway systems operating at capacity during peak hours. Thus, it is crucial for subway agencies to sense changes in travel demand and adjust their management schemes accordingly. In this paper we propose a statistical approach to estimate dynamic passenger flows with automated data. First, we develop a dynamic logistic model for calculating passenger tap -out times, which can be employed to infer passenger flow characteristics at the aggregate level. In addition, a new passenger -to -train assignment model for any subway route is derived based on the dynamic model. Subsequently, we apply an expectation -maximization algorithm to estimate the model parameters with automated fare collection and automated vehicle location data. Finally, a crossvalidation method is employed to validate our approach with data obtained from several routes in Beijing subway system in China. Results of 95% prediction intervals indicate the effectiveness of the models and the proposed estimation methods.

关键词： Boarding probability Travel time prediction Congestion evaluation Available safe egress time expectation-maximization algorithm Dynamic logistics model

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An algorithm for maximum likelihood estimation of hidden Markov models with unknown state-tying

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IEEE TRANSACTIONS ON SPEECH AND AUDIO PROCESSING 1998年第1期6卷 61-70页

作者： Cappe, O Mokbel, CE Jouvet, D Moulines, E ENST Dept Signal CNRS URA 820 F-75634 Paris 13 France

For speech recognition based on hidden Markov modeling, parameter-tying, which consists in constraining some of the parameters of the model to share the same value, has emerged as a standard practice. In this paper, an original algorithm is proposed that makes it possible to jointly estimate both the shared model parameters and the tying characteristics;using the maximum likelihood criterion, The proposed algorithm is based on a recently introduced extension of the classic expectation-maximization (EM) framework. The convergence properties of this class of algorithms are analyzed in detail. The method is evaluated on an isolated word recognition task using hidden Markov models (HMM's) with Gaussian observation densities and tying at the state level. Finally, the extension of this method to the case of mixture observation densities with tying at the mixture component level is discussed.

关键词： expectation-maximization algorithm hidden Markov models maximum likelihood estimation speech recognition

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τ-Inflated Beta Regression Model for Estimating τ-Restricted Means and Event-Free Probabilities for Censored Time-to-Event Data

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BIOMETRICAL JOURNAL 2024年第8期66卷

作者： Wang, Yizhuo Murray, Susan Univ Michigan Dept Biostat Ann Arbor MI 48109 USA

In this research, we propose analysis of tau-restricted censored time-to-event data via a tau-inflated beta regression (tau-IBR) model. The outcome of interest is min(tau,T), where T and tau are the time-to-event and follow-up duration, respectively. Our analysis goals include estimation and inference related to tau-restricted mean survival time (tau-RMST) values and event-free probabilities at tau that address the censored nature of the data. In this setting, it is common to observe many individuals with min(tau,T)=tau, a point mass that is typically overlooked in tau-restricted event-time analyses. Our proposed tau-IBR model is based on a decomposition of min(tau,T) into tau[I(T >=tau)+(T/tau)I(Texpectation-maximization algorithm. An alternative multiple imputation (MI) algorithm for fitting the tau-IBR model has the additional advantage of producing uncensored datasets for analysis. Simulations indicate excellent performance of the tau-IBR model(s), and corresponding tau-RMST estimates, in independent and dependent censoring settings. We apply our method to the Azithromycin for Prevention of Chronic Obstructive Pulmonary Disease (COPD) Exacerbations Trial. In addition to tau-IBR model results providing a nuanced understanding of the treatment effect, visually appealing heatmaps of the tau-restricted event times based on our MI datasets are given, a visualization not typically available for censored time-to-event data.

关键词： expectation-maximization algorithm inflated beta regression multiple imputation of censored times-to-event restricted mean survival time

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Semiparametric time-to-event modeling in the presence of a latent progression event

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BIOMETRICS 2017年第2期73卷 463-472页

作者： Rice, John D. Tsodikov, Alex Univ Michigan Dept Biostat Ann Arbor MI 48109 USA

In cancer research, interest frequently centers on factors influencing a latent event that must precede a terminal event. In practice it is often impossible to observe the latent event precisely, making inference about this process difficult. To address this problem, we propose a joint model for the unobserved time to the latent and terminal events, with the two events linked by the baseline hazard. Covariates enter the model parametrically as linear combinations that multiply, respectively, the hazard for the latent event and the hazard for the terminal event conditional on the latent one. We derive the partial likelihood estimators for this problem assuming the latent event is observed, and propose a profile likelihood-based method for estimation when the latent event is unobserved. The baseline hazard in this case is estimated nonparametrically using the EM algorithm, which allows for closed-form Breslow-type estimators at each iteration, bringing improved computational efficiency and stability compared with maximizing the marginal likelihood directly. We present simulation studies to illustrate the finite-sample properties of the method;its use in practice is demonstrated in the analysis of a prostate cancer data set.

关键词： expectation-maximization algorithm Semiparametric methods Survival analysis

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Stomped-t: A novel probability distribution for rough-probabilistic clustering

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INFORMATION SCIENCES 2017年 421卷 104-125页

作者： Banerjee, Abhirup Maji, Pradipta Indian Stat Inst Machine Intelligence Unit Biomed Imaging & Bioinformat Lab Kolkata India

Rough clustering is one of the principal research areas in data mining, machine learning, pattern recognition, and bioinformatics. Among different variants of rough clustering, rough-probabilistic clustering is a new concept introduced recently. In rough-probabilistic clustering, a class is defined as the union of two disjoint regions, namely, a crisp lower approximation region and a probabilistic boundary region. In this regard, stomped normal (SN) distribution provides a statistical modeling of the data set in rough-probabilistic clustering framework. The SN distribution models the central tendency, dispersion, and width of the lower approximation region of each class using its mean, variance, and width parameter, respectively. However, it does not take into consideration the property of kurtosis of the class distribution, which controls the concentration of values around mean and shape of the tail of data distribution. In this background, a novel probability distribution, named stomped-t (St) distribution, is introduced in the paper for rough-probabilistic clustering. The proposed probability distribution incorporates the property of kurtosis into the SN framework. The proposed St probability distribution is then integrated within the rough-probabilistic clustering framework for precise and robust clustering of the data. The efficacy of the proposed clustering algorithm is demonstrated for unsupervised data clustering and image segmentation problems, along with a comparative performance analysis with related algorithms. (C) 2017 Elsevier Inc. All rights reserved.

关键词： Rough-probabilistic clustering Stomped normal distribution Image segmentation Hidden Markov random field model expectation-maximization algorithm

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ExomeHMM: A Hidden Markov Model for Detecting Copy Number Variation Using Whole-Exome Sequencing Data

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CURRENT BIOINFORMATICS 2017年第2期12卷 147-155页

作者： Guo, Cheng Yu, Zhenhua Wang, Minghui Li, Ao Univ Sci & Technol China Sch Informat Sci & Technol Hefei 230027 Anhui Peoples R China Univ Sci & Technol China Res Ctr Biomed Engn Hefei Anhui Peoples R China

Background: Copy number variations (CNVs), including amplification and deletion, are alterations of DNA copy number compared to a reference genome. CNVs play a crucial role in tumourigenesis and progression, including amplification of oncogenes and deletion of tumor suppressor genes that may significantly increase the risk of cancer. CNVs are also reported to be closely related with non-cancer diseases, such as Down syndrome, Parkinson disease, and Alzheimer disease. Objective: Whole-exome sequencing (WES) has been successfully applied to the discovery of gene mutations as well as clinical diagnosis. But it is quite challenging to evaluate the copy number using WES data due to read depth bias, exons' distribution pattern and normal cell contamination. Our aim is develop an efficient method to overcome these challenges and detect CNVs using WES data. Method: In this study, we present ExomeHMM, a hidden Markov model (HMM) based CNV detecting algorithm. ExomeHMM exploits relative read depth, a ratio based signal, to mitigate read depth distortion and employs exponential attenuated transition matrix to handle sparsely and non-uniformly distributed exons. expectation-maximization algorithm is used to optimize parameters for the proposed model. Finally, we use standard Viterbi algorithm to infer the copy number of exons. Results: Using previously identified CNVs in 1000 Genome Project data as golden standard, ExomeHMM achieves the highest F-score among the four methods compared in this study. When applied to triple-negative breast cancer data, ExomeHMM is capable to find abnormal genes that are significantly associated with breast cancer. Conclusion: In conclusion, ExomeHMM is a suitable tool for CNV detections in both healthy samples as well as clinic tumor samples on whole-exome sequencing data.

关键词： Copy number variation expectation-maximization algorithm hidden Markov model next generation sequencing viterbi algorithm whole-exome sequencing

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Network anomaly detection with incomplete audit data

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COMPUTER NETWORKS 2007年第13期51卷 3935-3955页

作者： Patcha, Animesh Park, Jung-Min Virginia Polytech Inst & State Univ Bradley Dept Elect & Comp Engn Blacksburg VA 24061 USA

With the ever increasing deployment and usage of gigabit networks, traditional network anomaly detection based Intrusion Detection Systems (IDS) have not scaled accordingly. Most, if not all IDS assume the availability of complete and clean audit data. We contend that this assumption is not valid. Factors like noise, mobility of the nodes and the large amount of network traffic make it difficult to build a traffic profile of the network that is complete and immaculate for the purpose of anomaly detection. In this paper, we attempt to address these issues by presenting an anomaly detection scheme. called SCAN (Stochastic Clustering algorithm for Network Anomaly Detection), that has the capability to detect intrusions with high accuracy even with incomplete audit data. To address the threats posed by network-based denial-of-service attacks in high speed networks, SCAN consists of two modules: an anomaly detection module that is at the core of the design and an adaptive packet sampling scheme that intelligently samples packets to aid the anomaly detection module. The noteworthy features of SCAN include: (a) it intelligently samples the incoming network traffic to decrease the amount of audit data being sampled while retaining the intrinsic characteristics of the network traffic itself, (b) it computes the missing elements of the sampled audit data by utilizing an improved expectation-maximization (EM) algorithm-based clustering algorithm;and (c) it improves the speed of convergence of the clustering process by employing Bloom filters and data summaries. (c) 2007 Elsevier B.V. All rights reserved.

关键词： network anomaly detection expectation-maximization algorithm sampling

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