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15th Asia-Pacific Symposium on Internetware (Internetware)

作者： Han, Sheng Han, Qiang Qiao, Yixin Xue, Kehan Shi, Zhichao North Minzu Univ Yinchuan Ningxia Peoples R China

ISBN: (纸本)9798400707056

With the increasing complexity of software systems, the development of models that can accurately predict and enhance software reliability has become particularly important. This paper introduces an innovative method, applying the expectation conditional maximization (ECM) algorithm to the Burr-XII Software Reliability Growth Model (SRGM) based on the Non-Homogeneous Poisson Process (NHPP). The ECM algorithm, as a variant of the expectation maximization (EM) algorithm, is particularly suitable for the estimation of complex model parameters. We utilize the ECM algorithm to estimate the parameters of the Burr XII NHPP SRGM and compare its performance with traditional parameter estimation methods. The results of the experiment demonstrate superior goodness-of-fit and predictive ability on multiple datasets compared to traditional methods, which highlights the efficiency and accuracy of the ECM algorithm in parameter estimation.

关键词： Software reliability growth model non-homogeneous poison process Burr XII distribution expectation conditional maximization algorithm

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On Classical Inference of a Flexible Semi-Parametric Class of Distributions Under a Joint Balanced Progressive Censoring Scheme

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APPLIED STOCHASTIC MODELS IN BUSINESS AND INDUSTRY 2025年第1期41卷

作者： Bhattacharyya, Dhrubasish Kundu, Debasis Rajiv Gandhi Inst Petr Technol Dept Math Sci Amethi India Indian Inst Technol Kanpur Dept Math & Stat Kanpur India

The paper deals with the estimation procedures for the proportional hazard class of distributions under a two-sample balanced joint progressive censoring scheme. The baseline hazard function is assumed to be piecewise constant, instead of any specific form. This adds flexibility to the proposed model, and the shape of the underlying hazard function is completely data-driven. Since the complicated form of the likelihood function does not yield closed-form estimators, we propose a variant of the expectation-maximization algorithm, known as the expectation conditional maximization (ECM) algorithm, for obtaining maximum likelihood estimates of the model parameters. This leads to explicit expressions for the iterative constrained maximization steps of the algorithm. An extension to the case when the cut points are unknown has also been considered for dealing with problems involving real data. Simulation results and illustrations using real data have also been presented.

关键词： bootstrap confidence interval expectation conditional maximization algorithm expectation-maximization algorithm maximum likelihood estimators

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Lifetime prognostics for deteriorating systems with time-varying random jumps

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RELIABILITY ENGINEERING & SYSTEM SAFETY 2017年 167卷 338-350页

作者： Zhang, Jian-Xun Hu, Chang-Hua He, Xiao Si, Xiao-Sheng Liu, Yang Zhou, Dong-Hua Xian Res Inst High Tech Dept Automat Xian Shaanxi Peoples R China Shandong Univ Sci & Technol Coll Elect Engn & Automat Qingdao Peoples R China Tsinghua Univ Dept Automat Beijing Peoples R China

In this paper, we propose a jump diffusion process with non-homogeneous compound Poisson process to model the degradation process with randomly occurring jumps, which combines two stochastic processes, i.e., traditional diffusion process to describe the continuous degradation and non-homogeneous compound Poisson process to depict random jumps with a time-varying intensity. The approximated analytical lifetime under the concept of the first passage time (FPT) is obtained by a time-space transformation technique. To identify the model parameters, we first present a general method based on Maximum Likelihood Estimation (MLE) for the proposed model, and then specifically provide a two-step approach for linear jump diffusion process via combining MLE and expectation conditional maximization (ECM) algorithm. Finally, a numerical example and a study on the furnace wall are provided to illustrate the effectiveness of the proposed method. (C) 2017 Published by Elsevier Ltd.

关键词： Life prognostics Remaining useful life estimation expectation conditional maximization algorithm Jump diffusion process

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Non-negative matrix factorization by maximizing correntropy for cancer clustering

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BMC BIOINFORMATICS 2013年第1期14卷 1-11页

作者： Wang, Jim Jing-Yan Wang, Xiaolei Gao, Xin King Abdullah Univ Sci & Technol Comp Elect & Math Sci & Engn Div Thuwal 239556900 Saudi Arabia King Abdullah Univ Sci & Technol Computat Biosci Res Ctr Thuwal 239556900 Saudi Arabia

Background: Non-negative matrix factorization (NMF) has been shown to be a powerful tool for clustering gene expression data, which are widely used to classify cancers. NMF aims to find two non-negative matrices whose product closely approximates the original matrix. Traditional NMF methods minimize either the l(2) norm or the Kullback-Leibler distance between the product of the two matrices and the original matrix. Correntropy was recently shown to be an effective similarity measurement due to its stability to outliers or noise. Results: We propose a maximum correntropy criterion (MCC)-based NMF method (NMF-MCC) for gene expression data-based cancer clustering. Instead of minimizing the l(2) norm or the Kullback-Leibler distance, NMF-MCC maximizes the correntropy between the product of the two matrices and the original matrix. The optimization problem can be solved by an expectation conditional maximization algorithm. Conclusions: Extensive experiments on six cancer benchmark sets demonstrate that the

关键词： Nonnegative Matrix Factorization Cancer Cluster expectation conditional maximization Convex Conjugate Function expectation conditional maximization algorithm

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Automatic Modulation Classification for Low SNR Digital Signal in Frequency-Selective Fading Environments

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WIRELESS PERSONAL COMMUNICATIONS 2015年第3期84卷 1891-1906页

作者： Wallayt, Waqas Younis, Muhammad S. Imran, Muhammad Shoaib, Muhammad Guizani, Mohsen Natl Univ Sci & Technol Islamabad 44000 Pakistan King Saud Univ Coll Comp & Informat Sci Riyadh Saudi Arabia Qatar Univ Doha 2713 Qatar

In this research, a classifier is proposed for automatic modulation classification of some common modulation schemes, i.e., BPSK, QPSK, 8-PSK and 16-QAM. Our proposed classifier considers multipath fading effects on the received signal in a non-Gaussian noise environment. Automatic modulation classification is very challenging in real-world scenarios due to fading effects and additive Gaussian mixture noise on modulation schemes. Most of the available modulation classifiers do not consider the fading effects which results in degradation of classification in a blind channel environment. In our work, the channel is supposed to be suffering from excessive additive Gaussian mixture noise and frequency selective fading resulting in low signal SNR. The estimation of the unknown channel along with noise parameters is performed using ECM algorithm and then used in maximum-likelihood classifier for the classification of modulation schemes. Simulation results are presented that show 2 dB improvement in performance than classifier which considers only Gaussian noise in the received signal.

关键词： Automatic modulation classification expectation conditional maximization algorithm Maximum likelihood classification Feature based classification Gaussian mixture model Blind channel estimation Frequency selective fading

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Censored linear regression models for irregularly observed longitudinal data using the multivariate-t distribution

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STATISTICAL METHODS IN MEDICAL RESEARCH 2017年第2期26卷 542-566页

作者： Garay, Aldo M. Castro, Luis M. Leskow, Jacek Lachos, Victor H. Univ Estadual Campinas Dept Estat Campinas SP Brazil Univ Concepcion Dept Estadist Concepcion Chile Krakow Tech Univ Inst Math Krakow Poland

In acquired immunodeficiency syndrome (AIDS) studies it is quite common to observe viral load measurements collected irregularly over time. Moreover, these measurements can be subjected to some upper and/or lower detection limits depending on the quantification assays. A complication arises when these continuous repeated measures have a heavy-tailed behavior. For such data structures, we propose a robust structure for a censored linear model based on the multivariate Student's t-distribution. To compensate for the autocorrelation existing among irregularly observed measures, a damped exponential correlation structure is employed. An efficient expectation maximization type algorithm is developed for computing the maximum likelihood estimates, obtaining as a by-product the standard errors of the fixed effects and the log-likelihood function. The proposed algorithm uses closed-form expressions at the E-step that rely on formulas for the mean and variance of a truncated multivariate Student's t-distribution. The methodology is illustrated through an application to an Human Immunodeficiency Virus-AIDS (HIV-AIDS) study and several simulation studies.

关键词： censored data expectation conditional maximization algorithm longitudinal data HIV viral load outliers

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A CLASS OF MIXTURE OF EXPERTS MODELS FOR GENERAL INSURANCE: APPLICATION TO CORRELATED CLAIM FREQUENCIES

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ASTIN BULLETIN 2019年第3期49卷 647-688页

作者： Fung, Tsz Chai Badescu, Andrei L. Lin, X. Sheldon Univ Toronto Dept Stat Sci 100 St George St Toronto ON M5S 3G3 Canada

This paper focuses on the estimation and application aspects of the Erlang count logit-weighted reduced mixture of experts model (EC-LRMoE), which is a fully flexible multivariate insurance claim frequency regression model. We first prove the identifiability property of the proposed model to ensure that it is a suitable candidate for statistical inference. An expectation conditional maximization (ECM) algorithm is developed for efficient model calibrations. Three simulation studies are performed to examine the effectiveness of the proposed ECM algorithm and the versatility of the proposed model. The applicability of the EC-LRMoE is shown through fitting an European automobile insurance data set. Since the data set contains several complex features, we find it necessary to adopt such a flexible model. Apart from showing excellent fitting results, we are able to interpret the fitted model in an insurance perspective and to visualize the relationship between policyholders' information and their risk level. Finally, we demonstrate how the fitted model may be useful for insurance ratemaking.

关键词： Erlang count models expectation conditional maximization algorithm logitweighted gating functions mixture of experts models multivariate count regression

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Spatial modeling of individual-level infectious disease transmission: Tuberculosis data in Manitoba, Canada

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STATISTICS IN MEDICINE 2021年第7期40卷 1678-1704页

作者： Amiri, Leila Torabi, Mahmoud Deardon, Rob Pickles, Michael Univ Manitoba Rady Fac Hlth Sci Dept Community Hlth Sci Winnipeg MB Canada Univ Manitoba Fac Sci Dept Stat Winnipeg MB Canada Univ Calgary Fac Vet Med Dept Prod Anim Hlth Calgary AB Canada Univ Calgary Fac Sci Dept Math & Stat Calgary AB Canada

Geographically dependent individual level models (GD-ILMs) are a class of statistical models that can be used to study the spread of infectious disease through a population in discrete-time in which covariates can be measured both at individual and area levels. The typical ILMs to illustrate spatial data are based on the distance between susceptible and infectious individuals. A key feature of GD-ILMs is that they take into account the spatial location of the individuals in addition to the distance between susceptible and infectious individuals. As a motivation of this article, we consider tuberculosis (TB) data which is an infectious disease which can be transmitted through individuals. It is also known that certain areas/demographics/communities have higher prevalent of TB (see Section 4 for more details). It is also of interest of policy makers to identify those areas with higher infectivity rate of TB for possible preventions. Therefore, we need to analyze this data properly to address those concerns. In this article, the expectation conditional maximization algorithm is proposed for estimating the parameters of GD-ILMs to be able to predict the areas with the highest average infectivity rates of TB. We also evaluate the performance of our proposed approach through some simulations. Our simulation results indicate that the proposed method provides reliable estimates of parameters which confirms accuracy of the infectivity rates.

关键词： conditional autoregressive model expectation conditional maximization algorithm individual-level models susceptible-infected-removed model

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The EM algorithm - An old folk-song sung to a fast new tune

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JOURNAL OF THE ROYAL STATISTICAL SOCIETY SERIES B-METHODOLOGICAL 1997年第3期59卷 511-540页

作者： Meng, XL vanDyk, D HARVARD UNIV CAMBRIDGEMA 02138

Celebrating the 20th anniversary of the presentation of the paper by Dempster, Laird and Rubin which popularized the EM algorithm, we investigate, after a brief historical account, strategies that aim to make the EM algorithm converge faster while maintaining its simplicity and stability (e.g. automatic monotone convergence in likelihood). First we introduce the idea of a 'working parameter' to facilitate the search for efficient data augmentation schemes and thus fast EM implementations. Second, summarizing various recent extensions of the EM algorithm, we formulate a general alternating expectation-conditional maximization algorithm AECM that couples flexible data augmentation schemes with model reduction schemes to achieve efficient computations. We illustrate these methods using multivariate t-models with known or unknown degrees of freedom and Poisson models for image reconstruction. We show, through both empirical and theoretical evidence, the potential for a dramatic reduction in computational time with little increase in human effort. We also discuss the intrinsic connection between EM-type algorithms and the Gibbs sampler, and the possibility of using the techniques presented here to speed up the latter. The main conclusion of the pager is that, with the help of statistical considerations, it is possible to construct algorithms that are simple, stable and fast.

关键词： data augmentation expectation conditional maximization algorithm expectation conditional maximization either algorithm Gibbs sampler incomplete data Markov chain Monte Carlo method missing data model reduction multivariate t-distributions Poisson model positron emission tomography rate of convergence sage algorithm

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Joint Channel Estimation and Data Detection for MIMO-OFDM Two-Way Relay Networks

Joint Channel Estimation and Data Detection for MIMO-OFDM Tw...

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IEEE Global Telecommunications Conference (GLOBECOM 2010)

作者： Pham, The-Hanh Liang, Ying-Chang Garg, Hari Krishna Nallanathan, Arumugam Natl Univ Singapore Singapore 119260 Singapore Inst Infocomm Res Singapore 138632 Singapore Kings Coll London Dept Elect Engn London WC2R 2LS England

ISBN: (纸本)9781424456383

In this paper, we consider multi-input multi-output (MIMO) two-way relay networks with orthogonal frequency-division multiplexing (OFDM) modulation scheme. The networks consist of two end users exchanging their information via a relay. One information exchange between two end users is divided into two Phases. In Phase 1, two users send their information to the relay. The relay then amplifies and broadcasts its received signals to the two users in Phase 2, i.e., the relay works in the amplify-and-forward (AF) mode. By doing so, two-way relay networks require half as many time slots to accomplish one information exchange as the traditional one-way relay networks. We propose at each end user an iterative algorithm to jointly estimate the channel information and detect the data transmitted from the other user. The channel information consists of the composite channels which are the combinations of individual channels in the networks and noise covariance matrices resulting from the AF working mode of the networks. We apply the expectation conditional maximization (ECM) algorithm to estimate the necessary channel information for detection. Simulation results show that the performance of our proposed iterative algorithm is close to that given by the perfect channel information.

关键词： MIMO communication MIMO-OFDM two-way relay networks OFDM modulation amplify-and-forward mode channel estimation channel estimation channel information composite channels covariance matrices data detection expectation conditional maximization algorithm exp

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