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A new cure rate model with discrete and multiple exposures

COMMUNICATIONS IN STATISTICS-SIMULATION AND COMPUTATION 2024年

作者： Pal, Suvra Univ Texas Arlington Dept Math 411 S Nedderman Dr Arlington TX 76019 USA Univ Texas Arlington Coll Sci Div Data Sci Arlington TX 76019 USA

Cure rate models are mostly used to study data arising from cancer clinical trials. Its use in the context of infectious diseases has not been explored well. In 2008, Tournoud and Ecochard first proposed a mechanistic formulation of cure rate model in the context of infectious diseases with multiple exposures to infection. However, they assumed a simple Poisson distribution to capture the unobserved pathogens at each exposure time. In this paper, we propose a new cure rate model to study infectious diseases with discrete multiple exposures to infection. Our formulation captures both over-dispersion and under-dispersion with respect to the count on pathogens at each time of exposure. We also propose a new estimation method based on the expectation maximization algorithm to calculate the maximum likelihood estimates of the model parameters. We carry out a detailed Monte Carlo simulation study to demonstrate the performance of the proposed model and estimation algorithm. The flexibility of our proposed model also allows us to carry out a model discrimination. For this purpose, we use both likelihood ratio test and information-based criteria. Finally, we illustrate our proposed model using a recently collected data on COVID-19.

关键词： COVID-19 expectation maximization algorithm HIV Multiple exposures Nosocomial infection

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Mixed Gaussian Models for Modeling Fluctuation Process Characteristics of Photovoltaic Outputs

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FRONTIERS IN ENERGY RESEARCH 2019年 7卷

作者： Wang, Zhenhao Kang, Jia Cheng, Long Pei, Zheyi Dong, Cun Liang, Zhifeng Northeast Elect Power Univ Sch Elect Engn Jilin Jilin Peoples R China Natl Elect Power Dispatching & Commun Ctr Beijing Peoples R China

In order to model fluctuation process characteristics of photovoltaic (PV) outputs, this paper proposes a novel mixed Gaussian model with the expectation maximization (EM) algorithm. Firstly, random components of PV outputs are obtained through computing the difference between the measured data of PV output and its theoretical outputs. Secondly, the EM algorithm is used to determine the weight of different Gaussian distribution functions. Finally, the mixed Gaussian model is obtained by linearly superimposing these Gaussian functions with the weight. Based on the simulation results on the measured data in Xichang City, China, the effectiveness of the proposed model is verified. Furthermore, this model has proven to be significantly better than other traditional models including t location-scale (TLS) distribution model.

关键词： PV output modeling fluctuation characteristics probability density distribution expectation maximization algorithm mixed Gaussian models

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On-line harmonic signal denoising from the measurement with non-stationary and non-Gaussian noise

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SIGNAL PROCESSING 2022年 201卷

作者： Yu, Liang Chen, Yanqi Zhang, Yongli Wang, Ran Zhang, Zhaodong Shanghai Jiao Tong Univ State Key Lab Mech Syst & Vibrat Inst Vibrat Shock & Noise Shanghai 200240 Peoples R China Hagong Intelligent Robot Co Ltd Shanghai 201100 Peoples R China Shanghai Maritime Univ Coll Logist Engn Shanghai 201306 Peoples R China

Harmonic denoising is one of the important preprocessing steps before extracting harmonic signal characteristics. Several signal processing techniques have been developed and applied for denoising the harmonics, which assume that the noise follows a Gaussian distribution and is stationary. However, the noise is often not so simple as a Gaussian distribution, and it could be non-Gaussian and non-stationary in most practical scenarios. A novel online denoising method for the harmonic signal with non-stationary complex noises based on the Bayesian Maximum a Posteriori (MAP) framework is proposed in this paper. The measured signal is divided equally into several frames. Then these frames are transformed into the time-frequency domain by the Short-Time Fourier transform (STFT) and are assumed to be the sum of a low-rank matrix and a noise matrix. The online model of the low-rank matrix and the noise matrix is then constructed between the frames. The online Gaussian mixture model (GMM) and low-rank matrix factorization are performed on the measurement matrix in the complex number domain to reconstruct the harmonic signal. The performance of the proposed method is validated in the simulations. The non-Gaussian and non-stationary noise can be removed more effectively, and the proposed algorithm can improve the frequency estimation accuracy. (C) 2022 Elsevier B.V. All rights reserved.

关键词： Harmonic denoising Gaussian mixture model Low-rank modeling Online algorithm expectation maximization algorithm

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Iterative reconstruction techniques in emission computed tomography

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PHYSICS IN MEDICINE AND BIOLOGY 2006年第15期51卷 R541-R578页

作者： Qi, Jinyi Leahy, Richard M. Univ Calif Davis Dept Biomed Engn Davis CA 95616 USA Univ So Calif Dept Elect Engn Signal & Image Proc Inst Los Angeles CA 90089 USA

In emission tomography statistically based iterative methods can improve image quality relative to analytic image reconstruction through more accurate physical and statistical modelling of high-energy photon production and detection processes. Continued exponential improvements in computing power, coupled with the development of fast algorithms, have made routine use of iterative techniques practical, resulting in their increasing popularity in both clinical and research environments. Here we review recent progress in developing statistically based iterative techniques for emission computed tomography. We describe the different formulations of the emission image reconstruction problem and their properties. We then describe the numerical algorithms that are used for optimizing these functions and illustrate their behaviour using small scale simulations.

关键词： BAYESIAN IMAGE-RECONSTRUCTION expectation maximization algorithm MAXIMUM-LIKELIHOOD RECONSTRUCTION LIST-MODE LIKELIHOOD MEDIAN ROOT PRIOR EM algorithm LEAST-SQUARES PET DATA 3D PET SCATTER CORRECTION

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Association mapping of blood pressure levels in a longitudinal framework using binomial regression

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BMC proceedings 2014年第SUPPL 1 GENETIC ANALYSIS WORKSHOP 18VANESSA OLMO期8卷 S74页

作者： Arunabha Majumdar Indranil Mukhopadhyay Saurabh Ghosh Human Genetics Unit Indian Statistical Institute 203 B.T. Road Kolkata 700108 India.

Heritable quantitative characters underline complex genetic traits. However, a single quantitative phenotype may not be a suitably good surrogate for a clinical end point trait. It may be more optimal to use a multivariate phenotype vector correlated with the end point trait to carry out an association analysis. Existing methods, such as variance components and principal components, suffer from inherent limitations, such as lack of robustness or difficulty in biological interpretation of association findings. In an effort to circumvent these limitations, we propose a novel regression approach based on a conditional binomial model to detect association between a single-nucleotide polymorphism and a multivariate phenotype vector. We use our proposed method to analyze data on systolic and diastolic blood pressure levels provided in Genetic Analysis Workshop 18. We find that the bivariate analysis of the two phenotypes yields more promising results in terms of lower p-values compared to univariate analyses.

关键词： Association Analysis Blood Pressure Level expectation maximization algorithm Genetic Analysis Workshop Systolic Blood Pressure Level

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Reconstruction of viral population structure from next-generation sequencing data using multicommodity flows

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BMC BIOINFORMATICS 2013年第Sup9期14卷 S2-S2页

作者： Skums, Pavel Mancuso, Nicholas Artyomenko, Alexander Tork, Bassam Mandoiu, Ion Khudyakov, Yury Zelikovsky, Alex Ctr Dis Control & Prevent Lab Mol Epidemiol & Bioinformat Div Viral Hepatitis Atlanta GA 30333 USA Georgia State Univ Dept Comp Sci Atlanta GA 30303 USA Univ Connecticut Dept Comp Sci & Engn Storrs CT 06269 USA

Background: Highly mutable RNA viruses exist in infected hosts as heterogeneous populations of genetically close variants known as quasispecies. Next-generation sequencing (NGS) allows for analysing a large number of viral sequences from infected patients, presenting a novel opportunity for studying the structure of a viral population and understanding virus evolution, drug resistance and immune escape. Accurate reconstruction of genetic composition of intra-host viral populations involves assembling the NGS short reads into whole-genome sequences and estimating frequencies of individual viral variants. Although a few approaches were developed for this task, accurate reconstruction of quasispecies populations remains greatly unresolved. Results: Two new methods, AmpMCF and ShotMCF, for reconstruction of the whole-genome intra-host viral variants and estimation of their frequencies were developed, based on Multicommodity Flows (MCFs). AmpMCF was designed for NGS reads obtained from individual PCR amplicons and ShotMCF for NGS shotgun reads. While AmpMCF, based on covering formulation, identifies a minimal set of quasispecies explaining all observed reads, ShotMCS, based on packing formulation, engages the maximal number of reads to generate the most probable set of quasispecies. Both methods were evaluated on simulated data in comparison to Maximum Bandwidth and ViSpA, previously developed state-of-the-art algorithms for estimating quasispecies spectra from the NGS amplicon and shotgun reads, respectively. Both algorithms were accurate in estimation of quasispecies frequencies, especially from large datasets. Conclusions: The problem of viral population reconstruction from amplicon or shotgun NGS reads was solved using the MCF formulation. The two methods, ShotMCF and AmpMCF, developed here afford accurate reconstruction of the structure of intra-host viral population from NGS reads. The implementations of the algorithms are available at http://***/vira.h

关键词： Root Mean Square Error Positive Predict Value expectation maximization expectation maximization algorithm Viral Population

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Time series prediction with input noise based on the ESN and the EM and its industrial applications

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EXPERT SYSTEMS WITH APPLICATIONS 2023年第1期217卷

作者： Liu, Ying Chen, Long Li, Yunchong Zhao, Jun Wang, Wei Dalian Univ Technol Key Lab Intelligent Control & Optimizat Ind Equipm Minist Educ Dalian 116024 Peoples R China Dalian Univ Technol Sch Control Sci & Engn Dalian 116024 Peoples R China Dalian Univ Technol Artificial Intelligence Inst Dalian Peoples R China

Industrial time series data usually have a high noise. In this paper, an echo state network (ESN) model with input noise is proposed to address the problem of predicting time series with noise. In the ESN, the introduction of the input noise makes it difficult to accurately estimate the non-linear states of the dynamical reservoir, therefore, in this study, the states are approximated by linearizing it through an extended Kalman filter (EKF). For the learning of the model parameters, the expectation maximization algorithm (EM) is used to iteratively update all the uncertain parameters to construct the prediction intervals, where the state estimation is performed using a forward back algorithm. To verify the effectiveness of the proposed method, two benchmark data sets and three real gas data sets from steel enterprises are used in this paper. Experimental results show that the prediction accuracy of the proposed method is better than that of the existing methods.

关键词： Industrial data Input noise Echo state network expectation maximization algorithm

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Genomic selection and complex trait prediction using a fast EM algorithm applied to genome-wide markers

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BMC BIOINFORMATICS 2010年第1期11卷 1-12页

作者： Shepherd, Ross K. Meuwissen, Theo H. E. Woolliams, John A. Cent Queensland Univ Sch Informat & Commun Technol Rockhampton Qld 4702 Australia Norwegian Univ Life Sci Inst Anim & Aquacultural Sci N-1432 As Norway Univ Edinburgh Royal Dick Sch Vet Studies Roslin EH25 9PS Midlothian Scotland Univ Edinburgh Roslin Inst Roslin EH25 9PS Midlothian Scotland

Background: The information provided by dense genome-wide markers using high throughput technology is of considerable potential in human disease studies and livestock breeding programs. Genome-wide association studies relate individual single nucleotide polymorphisms (SNP) from dense SNP panels to individual measurements of complex traits, with the underlying assumption being that any association is caused by linkage disequilibrium (LD) between SNP and quantitative trait loci (QTL) affecting the trait. Often SNP are in genomic regions of no trait variation. Whole genome Bayesian models are an effective way of incorporating this and other important prior information into modelling. However a full Bayesian analysis is often not feasible due to the large computational time involved. Results: This article proposes an expectation-maximization (EM) algorithm called emBayesB which allows only a proportion of SNP to be in LD with QTL and incorporates prior information about the distribution of SNP effects. The posterior probability of being in LD with at least one QTL is calculated for each SNP along with estimates of the hyperparameters for the mixture prior. A simulated example of genomic selection from an international workshop is used to demonstrate the features of the EM algorithm. The accuracy of prediction is comparable to a full Bayesian analysis but the EM algorithm is considerably faster. The EM algorithm was accurate in locating QTL which explained more than 1% of the total genetic variation. A computational algorithm for very large SNP panels is described. Conclusions: emBayesB is a fast and accurate EM algorithm for implementing genomic selection and predicting complex traits by mapping QTL in genome-wide dense SNP marker data. Its accuracy is similar to Bayesian methods but it takes only a fraction of the time.

关键词： Quantitative Trait Locus Single Nucleotide Polymorphism Lasso Genomic Selection expectation maximization algorithm

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A data-driven soft sensor based on weighted probabilistic slow feature analysis for nonlinear dynamic chemical processes

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JOURNAL OF CHEMOMETRICS 2023年第4期37卷 e3471-e3471页

作者： Zhang, Miao Xu, Beike Zhou, Le Zheng, Hui Jie, Jing Zhejiang Univ Sci & Technol Sch Automation & Elect Engn Hangzhou Peoples R China Zhejiang Univ Sci & Technol Sch Automation & Elect Engn Hangzhou 310023 Peoples R China

Modeling high-dimensional dynamic processes is a challenging task. In this regard, probabilistic slow feature analysis (PSFA) is revealed to be advantageous for dynamic soft sensor modeling, which can extract slowly varying intrinsic features from high-dimensional data. However, nonlinearities prevalent in industrial processes are not considered, which could lead to unsatisfactory prediction performance. In this paper, a weighted PSFA (WPSFA)-based soft sensor model is proposed for nonlinear dynamic chemical process. In WPSFA, a weighted log-likelihood function of complete data is constructed to linearize the nonlinear state emission equation. Then, the expectation maximization algorithm is applied to estimate the model parameters and a locally weighted regression model is established for quality prediction. Finally, the feasibility and effectiveness of the proposed approach are well illustrated through a numerical example and a real industrial process.

关键词： expectation maximization algorithm nonlinear dynamic processes soft sensor weighted probabilistic slow feature analysis

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Autoregressive double latent variables probabilistic model for higher-order dynamic process monitoring

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JOURNAL OF PROCESS CONTROL 2024年 141卷

作者： Ying, Ze Chang, Yuqing He, Yuchen Wang, Fuli Northeastern Univ Coll Informat Sci & Engn Shenyang 110819 Peoples R China Northeastern Univ State Key Lab Synthet Automat Proc Ind Shenyang 110819 Peoples R China China Jiliang Univ Key Lab Intelligent Mfg Qual Big Data Tracing & An Hangzhou 310018 Peoples R China

The application of multivariate statistical analysis in process monitoring has emerged as a significant research topic, with a focus on consideration of data correlations. The present study investigates an anomaly detection method based on autoregressive double latent variables probabilistic (ADLVP) model for industrial dynamic processes. Specifically, the ADLVP model incorporates two distinct types of latent variables (LVs) to capture the internal relationships within the data from both quality-correlated and uncorrelated perspectives. Moreover, the model employs autoregressive modeling to characterize the double latent variables with time-dependence, enabling them to unveil more intricate higher-order autocorrelations between industrial measurements. The model parameters and the double latent variables can be iteratively determined using the expectation maximization (EM) algorithm, upon which the statistics for process monitoring are devised. Finally, the proposed method is validated in two industrial studies, and experimental results demonstrate that the ADLVP model outperforms its counterparts in dynamic processes monitoring.

关键词： Multivariate statistical analysis Dynamic process monitoring Probabilistic latent variable structure expectation maximization algorithm

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