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The AIDS Epidemic: Estimating Survival After AIDS Diagnosis From Surveillance Data

Quarterly Publications of the American Statistical Association 1993年第421期88卷 26-36页

Survival analysis based on reported acquired immune deficiency syndrome (AIDS) cases from the surveillance data maintained by the Centers for Disease Control (CDC) leads to severe bias because of a sizable fraction of unreported deaths. One approach to this problem is to condition the analysis on reported deaths, but this approach presents several difficulties. First, only the individuals who die within a chronologic time interval defined by the analysis may be observed. Second, this right-truncated sampling process is complicated by the delay in reporting death to the surveillance system, and as a result, only a proportion of those who die within the defined time interval are reported. Third, the time each death is reported to the surveillance system, which is essential for the construction of a joint likelihood of survival and reporting delay, may not be available. Fourth, deaths that occurred before 1984 all had unknown times of death. As a consequence, the direct implementation of the joint likelihood approach appears to be complicated and difficult. On the other hand, this data set is worth analyzing; it is the only data base that comes close to covering the entire AIDS population and thus provides invaluable information to assess the current and predict the future AIDS epidemic. In this article we apply several missing–data techniques to deal with these difficulties. In particular, we discuss how to estimate the delay and survival distributions separately, and then combine the two sources of information to make valid inferences for the survival distributions using multiple imputation. The EM algorithm is used to facilitate computations. The methodology developed here can also be applied to other studies that share similar data structures, such as data from local and other national surveillance systems. Analysis of the CDC surveillance data as of March 1991, for some high-risk groups considered, indicates that there has been a steady and notable increase in sur

关键词： Acquired immune deficiency syndrome Censoring expectation-maximization algorithm Incomplete data Missing data Multiple imputation Proportional hazards model Supplemented-expectation-maximization algorithm Truncation

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Modeling Cyber Loss Severity Using a Spliced Regression Distribution with Mixture Components

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Open Journal of Statistics 2023年第4期13卷 425-452页

作者： Meng Sun Simon Fraser University Burnaby Canada

Cyber losses in terms of number of records breached under cyber incidents commonly feature a significant portion of zeros, specific characteristics of mid-range losses and large losses, which make it hard to model the whole range of the losses using a standard loss distribution. We tackle this modeling problem by proposing a three-component spliced regression model that can simultaneously model zeros, moderate and large losses and consider heterogeneous effects in mixture components. To apply our proposed model to Privacy Right Clearinghouse (PRC) data breach chronology, we segment geographical groups using unsupervised cluster analysis, and utilize a covariate-dependent probability to model zero losses, finite mixture distributions for moderate body and an extreme value distribution for large losses capturing the heavy-tailed nature of the loss data. Parameters and coefficients are estimated using the expectation-maximization (EM) algorithm. Combining with our frequency model (generalized linear mixed model) for data breaches, aggregate loss distributions are investigated and applications on cyber insurance pricing and risk management are discussed.

关键词： Cyber Risk Data Breach Spliced Regression Model Finite Mixture Distribu-tion Cluster Analysis expectation-maximization algorithm Extreme Value Theory

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Sparse Bayesian Learning for DOA Estimation in MIMO Radar with Unknown Nonuniform Noise

Sparse Bayesian Learning for DOA Estimation in MIMO Radar wi...

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CIE International Conference on Radar (RADAR)

作者： Wang, Xianpeng Huang, Mengxing Bi, Guoan Hainan Univ State Key Lab Marine Resource Utilizat South Chin Haikou 570228 Hainan Peoples R China Hainan Univ Coll Informat Sci & Technol Haikou 570228 Hainan Peoples R China Nanyang Technol Univ Sch Elect & Elect Engn Singapore 639798 Singapore

ISBN: (纸本)9781509048281

In this paper, a sparse Bayesian learning framework for DOA estimation in multiple input multiple output (MIMO) radar is proposed with unknown nonuniform noise. In the proposed method, the redundant elements of MIMO radar can be eliminated by using the reduced dimensional (RD) transformation. Then a sparse Bayesian model of covariance vector is formulated by assuming that the prior source power is independent zero-mean Gaussian distributed with hyperparameters for its unknown variance. The hyperparameters and nonuniform noise variances are estimated by utilizing the expectation-maximization (EM) algorithm and least squares (LS) criterion, respectively. Finally, the spectrum of hyperparameters is used to estimate the coarse DOA, and a high-precision DOA estimation is achieved by using a refined 1-D searching procedure based on the reconstruction result. Simulation results have demonstrated that the proposed method can work well with different nonuniform noise and achieve better performance.

关键词： MIMO radar direction of arrival estimation sparse Bayesian learning expectation-maximization algorithm nonuniform noise

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Lognormal and log-logistic distribution mixture analysis and application

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MODERN PHYSICS LETTERS B 2022年第5期36卷

作者： Kocovic, Jelena Mitic, Vojislav V. Koprivica, Marija Rajic, Vesna Lazovic, Goran Univ Belgrade Fac Econ Dept Stat & Math Belgrade Serbia SASA Inst Tech Sci Belgrade Serbia Univ Nis Fac Elect Engn Nish Serbia Univ Belgrade Fac Mech Engn Dept Math Belgrade Serbia

In this paper, we analyze a mixture of Lognormal and Log-Logistic distribution. We estimate the parameters of the introduced distribution by using the expectation-maximization (EM) algorithm. Various phenomena in the field of medicine and economy could be modeled by this mixture. In this paper, it is used to construct new mortality model for determining the unisex premium rates in life insurance. The application of the model is illustrated in the case of Serbian population and its advantages are presented in the context of life insurance premium calculation.

关键词： Mixture distribution expectation-maximization algorithm mortality model unisex premium rate

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A Combined Method for MEMS Gyroscope Error Compensation Using a Long Short-Term Memory Network and Kalman Filter in Random Vibration Environments

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SENSORS 2021年第4期21卷 1181-1181页

作者： Zhu, Chenhao Cai, Sheng Yang, Yifan Xu, Wei Shen, Honghai Chu, Hairong Chinese Acad Sci Changchun Inst Opt Fine Mech & Phys Changchun 130033 Peoples R China Univ Chinese Acad Sci Beijing 100049 Peoples R China Chinese Acad Sci Changchun Inst Opt Fine Mech & Phys Key Lab Airborne Opt Imaging & Measurement Changchun 130033 Peoples R China

In applications such as carrier attitude control and mobile device navigation, a micro-electro-mechanical-system (MEMS) gyroscope will inevitably be affected by random vibration, which significantly affects the performance of the MEMS gyroscope. In order to solve the degradation of MEMS gyroscope performance in random vibration environments, in this paper, a combined method of a long short-term memory (LSTM) network and Kalman filter (KF) is proposed for error compensation, where Kalman filter parameters are iteratively optimized using the Kalman smoother and expectation-maximization (EM) algorithm. In order to verify the effectiveness of the proposed method, we performed a linear random vibration test to acquire MEMS gyroscope data. Subsequently, an analysis of the effects of input data step size and network topology on gyroscope error compensation performance is presented. Furthermore, the autoregressive moving average-Kalman filter (ARMA-KF) model, which is commonly used in gyroscope error compensation, was also combined with the LSTM network as a comparison method. The results show that, for the x-axis data, the proposed combined method reduces the standard deviation (STD) by 51.58% and 31.92% compared to the bidirectional LSTM (BiLSTM) network, and EM-KF method, respectively. For the z-axis data, the proposed combined method reduces the standard deviation by 29.19% and 12.75% compared to the BiLSTM network and EM-KF method, respectively. Furthermore, for x-axis data and z-axis data, the proposed combined method reduces the standard deviation by 46.54% and 22.30% compared to the BiLSTM-ARMA-KF method, respectively, and the output is smoother, proving the effectiveness of the proposed method.

关键词： MEMS gyroscope random vibration environments long short-term memory network Kalman filter expectation-maximization algorithm

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Maximum likelihood estimation for discrete latent variable models via evolutionary algorithms

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STATISTICS AND COMPUTING 2024年第2期34卷 62-62页

作者： Brusa, Luca Pennoni, Fulvia Bartolucci, Francesco Univ Milano Bicocca Dept Stat & Quantitat Methods Via Bicocca Arcimboldi 8 I-20126 Milan Italy Univ Perugia Dept Econ Via Alessandro Pascoli 20 I-06123 Perugia Italy

We propose an evolutionary optimization method for maximum likelihood and approximate maximum likelihood estimation of discrete latent variable models. The proposal is based on modified versions of the expectation-maximization (EM) and variational EM (VEM) algorithms, which are based on the genetic approach and allow us to accurately explore the parameter space, reducing the chance to be trapped into one of the multiple local maxima of the log-likelihood function. Their performance is examined through an extensive Monte Carlo simulation study where they are employed to estimate latent class, hidden Markov, and stochastic block models and compared with the standard EM and VEM algorithms. We observe a significant increase in the chance to reach global maximum of the target function and a high accuracy of the estimated parameters for each model. Applications focused on the analysis of cross-sectional, longitudinal, and network data are proposed to illustrate and compare the algorithms.

关键词： expectation-maximization algorithm Local maxima Maximum-likelihood estimation Variational expectation-maximization algorithm

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Variable selection and prediction in biased samples with censored outcomes

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LIFETIME DATA ANALYSIS 2018年第1期24卷 72-93页

作者： Wu, Ying Cook, Richard J. Nankai Univ Inst Stat Tianjin Peoples R China Univ Waterloo Dept Stat & Actuarial Sci Waterloo ON N2L 3G1 Canada

With the increasing availability of large prospective disease registries, scientists studying the course of chronic conditions often have access to multiple data sources, with each source generated based on its own entry conditions. The different entry conditions of the various registries may be explicitly based on the response process of interest, in which case the statistical analysis must recognize the unique truncation schemes. Moreover, intermittent assessment of individuals in the registries can lead to interval-censored times of interest. We consider the problem of selecting important prognostic biomarkers from a large set of candidates when the event times of interest are truncated and right- or interval-censored. Methods for penalized regression are adapted to handle truncation via a Turnbull-type complete data likelihood. An expectation-maximization algorithm is described which is empirically shown to perform well. Inverse probability weights are used to adjust for the selection bias when assessing predictive accuracy based on individuals whose event status is known at a time of interest. Application to the motivating study of the development of psoriatic arthritis in patients with psoriasis in both the psoriasis cohort and the psoriatic arthritis cohort illustrates the procedure.

关键词： expectation-maximization algorithm Inverse probability weighted estimator Truncation Penalized regression Prediction error ROC curve

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Ehapp2: Estimate haplotype frequencies from pooled sequencing data with prior database information

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JOURNAL OF BIOINFORMATICS AND COMPUTATIONAL BIOLOGY 2016年第4期14卷 1650017-1650017页

作者： Cao, Chang-Chang Sun, Xiao Southeast Univ Sch Biol Sci & Med Engn State Key Lab Bioelect Nanjing 210096 Jiangsu Peoples R China

To reduce the cost of large-scale re-sequencing, multiple individuals are pooled together and sequenced called pooled sequencing. Pooled sequencing could provide a cost-effective alternative to sequencing individuals separately. To facilitate the application of pooled sequencing in haplotype-based diseases association analysis, the critical procedure is to accurately estimate haplotype frequencies from pooled samples. Here we present Ehapp2 for estimating haplotype frequencies from pooled sequencing data by utilizing a database which provides prior information of known haplotypes. We first translate the problem of estimating frequency for each haplotype into finding a sparse solution for a system of linear equations, where the NNREG algorithm is employed to achieve the solution. Simulation experiments reveal that Ehapp2 is robust to sequencing errors and able to estimate the frequencies of haplotypes with less than 3% average relative difference for pooled sequencing of mixture of real Drosophila haplotypes with 50 x total coverage even when the sequencing error rate is as high as 0.05. Owing to the strategy that proportions for local haplotypes spanning multiple SNPs are accurately calculated first, Ehapp2 retains excellent estimation for recombinant haplotypes resulting from chromosomal crossover. Comparisons with present methods reveal that Ehapp2 is state-of-the-art for many sequencing study designs and more suitable for current massive parallel sequencing.

关键词： Haplotype frequency estimation pooled sequencing expectation-maximization algorithm

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Lip Localization and Performance Evaluation

Lip Localization and Performance Evaluation

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International Conference on Machine Vision (ICMV 2007)

作者： Shafiq Ur Rehman Li Liu Haibo Li Digital Media Laboratory Tillämpad Fysik och Elektronik Umeå Universitet Umea Sweden

Although impressive achievement has been made in the field of lip localization, there is no clear picture showing that which algorithms or methods have technical advantages over others. It is partly due to the fact that the commonly agreed evaluation methodology has not been established yet in the field. The common practice is using manual lip labelling as the ground truth for evaluation marked by human labellers. Our empirical results using the expectation-maximization procedure show that human labeller introduce subjective noises while labelling the lip-localization and often the subjective noises added can be too significant to be directly used for evaluation as ground truth. We argue in the paper that to train and evaluate a lip analysis system one has to measure the quality of human operators and infer the "ground truth" from the manual labelling, simultaneously. We demonstrate using BioID database how the expectation-maximization technology can be used for this purpose.

关键词： Lip Localization Lip Analysis expectation-maximization algorithm Performance Evaluation Ground Truth Computer Vision

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Expandable factor analysis

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BIOMETRIKA 2017年第3期104卷 649-663页

作者： Srivastava, Sanvesh Engelhardt, Barbara E. Dunson, David B. Univ Iowa Dept Stat & Actuarial Sci 241 Schaeffer Hall20 East Washington St Iowa City IA 52242 USA Princeton Univ Dept Comp Sci Ctr Stat & Machine Learning 35 Olden St Princeton NJ 08540 USA Duke Univ Dept Stat Sci Box 90251 Durham NC 27708 USA

Bayesian sparse factor models have proven useful for characterizing dependence in multivariate data, but scaling computation to large numbers of samples and dimensions is problematic. We propose expandable factor analysis for scalable inference in factor models when the number of factors is unknown. The method relies on a continuous shrinkage prior for efficient maximum a posteriori estimation of a low-rank and sparse loadings matrix. The structure of the prior leads to an estimation algorithm that accommodates uncertainty in the number of factors. We propose an information criterion to select the hyperparameters of the prior. Expandable factor analysis has better false discovery rates and true positive rates than its competitors across diverse simulation settings. We apply the proposed approach to a gene expression study of ageing in mice, demonstrating superior results relative to four competing methods.

关键词： expectation-maximization algorithm Factor analysis Shrinkage prior Sparsity Variable selection

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