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A novel transmission-based test of association for multivariate phenotypes: an application to systolic and diastolic blood pressure levels

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BMC proceedings 2014年第SUPPL 1期8卷 S71页

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

Unlike case-control studies, family-based tests for association are protected against population stratification. Complex genetic traits are often governed by quantitative precursors and it has been argued that it may be a more powerful strategy to analyze these quantitative precursors instead of the clinical end point trait. Although methods have been developed for family-based association tests for single quantitative traits, it is of interest to develop such methods for multivariate phenotypes. We propose a novel transmission-based approach based on a trio design using a simple logistic regression to test for association with a multivariate phenotype. We use our proposed method to analyze data on systolic and diastolic blood pressure levels provided in Genetic Analysis Workshop 18. However, we find that the bivariate analysis of the two phenotypes did not provide more promising results compared to univariate analyses, suggesting a possibility of a different set of major genetic variants modulating the two phenotypes.

关键词： expectation maximization algorithm Genetic Analysis Workshop Transmission Disequilibrium Heterozygous Parent Systolic Blood Pressure Level

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A Signal Processing Technique for Compensating Random Drift of MEMS Gyros

A Signal Processing Technique for Compensating Random Drift ...

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2014中国制导、导航与控制学术会议

作者： Jieyu Liu Qiang Shen Weiwei Qin Department of Automatic Control Xi''an Research Institute of High-techXi''anShannxi Province710025China

ISBN: (纸本)9781479946983

In this paper,we present a prediction and compensation method for Micro-Electro-Mechanical System (MEMS) gyroscope random drift,which is based on relevance vector *** relevance vector machine (RVM) model is established based on the feature of MEMS gyroscope random drift and the parameters are trained by the expectation maximization (EM) *** phase space reconstruction,the time sequence of random drift is accessed in the *** final experimental results indicate that our proposed methodology can achieve both the least complexity of structure and goodness of fit to data,and also can predict the gyroscope random drift ***,by compensating random drift using the predicting result,the precision of gyroscopes application could be improved well.

关键词： compensation expectation-maximisation algorithm gyroscopes learning (artificial intelligence) microsensors signal processing EM algorithm MEMS gyroscope RVM model compensation method expectation maximization algorithm microelectromechanical systems phase space reconstruction prediction method random drift compensation relevance vector machine signal processing technique Bayes methods Gyroscopes Kernel Micromechanical devices Prediction algorithms Support vector machines Vectors

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A New Method for Maximum Likelihood Parameter Estimation of Gamma-Gamma Distribution

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JOURNAL OF LIGHTWAVE TECHNOLOGY 2013年第9期31卷 1347-1353页

作者： Kazeminia, Mahdi Mehrjoo, Mehri Univ Sistan & Baluchestan Dept Telecommun Zahedan 98155987 Iran

We propose a method to obtain the maximum likelihood (ML) parameter estimation of the Gamma-Gamma (Gamma - Gamma) distribution representing the free space optical (FSO) channel irradiance fluctuations. The proposed method is based on the expectation maximization (EM) algorithm and the generalized Newton method using a non-quadratic approximation. The numerical results show that, for all turbulence conditions, the proposed ML method is more accurate than the fractional moments (FMOM) method and the numerical ML method (two dimensional numerical maximization of log-likelihood function using the Nelder-Mead algorithm). Moreover, the proposed ML is a fast and stable iterative method, because the iterations always converge to the global optimum with high convergence rate.

关键词： expectation maximization algorithm free space optical communications generalized Newton method maximum likelihood parameter estimation

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Partitioning the distribution function of radar reflectivity in convective storms using maximum likelihood method

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ATMOSPHERIC RESEARCH 2013年 124卷 123-136页

作者： Diop, Cheikh Abdoulahat Sauvageot, Henri Mesnard, Frederic Univ Toulouse Observ Midi Pyrenees Lab Aerol Toulouse France Univ Toulouse GEII Inst Univ Technol Tarbes Tarbes France

A method to estimate the parameters of radar reflectivity distribution functions of convective storm systems is presented. To carry out this estimation, the probability density distribution of the radar reflectivity, P(Z), is computed using data collected on continental convective storm systems with the radar of Little Rock in central Arkansas. We show that P(Z) can be modeled as a mixture of Gaussian components, each of them corresponding to a type of precipitation. The EM (expectation maximization) algorithm is used to decompose P(Z) in these merged components. In the precipitation associated with intense continental convective storms, four main populations are considered: shallow precipitation, stratiform precipitation, convective precipitation, and hail. Each component is described by the fraction of area occupied inside P(Z) and by the Gaussian parameters, mean and variance. The retrieval of the mixed distribution by a linear combination of the Gaussian components gives a very satisfactory P(Z) fitting. It is shown that this method enables to follow the evolution with time of the various precipitation components of a convective system crossing the radar observed area. (C) 2013 Elsevier B.V. All rights reserved.

关键词： Precipitation Radar reflectivity of precipitation Hail detection by radar Probability density function of precipitation NEXRAD radar network Hailstorm observation by radar expectation maximization algorithm

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Multiple model approach to nonlinear system identification with an uncertain scheduling variable using EM algorithm

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JOURNAL OF PROCESS CONTROL 2013年第10期23卷 1480-1496页

作者： Chen, Lei Tulsyan, Aditya Huang, Biao Liu, Fei Jiangnan Univ Inst Automat Key Lab Adv Proc Control Light Ind Minist Educ Wuxi 214122 Peoples R China Univ Alberta Dept Chem & Mat Engn Edmonton AB T6G 2G6 Canada

This paper deals with system identification of general nonlinear dynamical systems with an uncertain scheduling variable. A multi model approach is developed;wherein, a set of local auto regressive exogenous (ARX) models are first identified at different process operating points, and are then combined to describe the complete dynamics of a nonlinear system. An expectation-maximization (EM) algorithm is used for simultaneous identification of local ARX models, and for computing the probability associated with each of the local ARX models taking effect. A smoothing algorithm is used to estimate the distribution of the hidden scheduling variables in the EM algorithm. If the dynamics of the scheduling variables are linear, Kalman smoother is used;whereas, if the dynamics are nonlinear, sequential Monte-Carlo (SMC) method is used. Several simulation examples, including a continuous stirred tank reactor (CSTR) and a distillation column, are considered to illustrate the efficacy of the proposed method. Furthermore, to highlight the practical utility of the developed identification method, an experimental study on a pilot-scale hybrid tank system is also provided. (C) 2013 Elsevier Ltd. All rights reserved.

关键词： System identification Nonlinear process Multiple models expectation maximization algorithm Kalman smoother Particle smoother

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Likelihood Inference Based on Left Truncated and Right Censored Data From a Gamma Distribution

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IEEE TRANSACTIONS ON RELIABILITY 2013年第3期62卷 679-688页

作者： Balakrishnan, Narayanaswamy Mitra, Debanjan McMaster Univ Dept Math & Stat Hamilton ON L8S 4K1 Canada King Abdulaziz Univ Dept Stat Jeddah 21413 Saudi Arabia Indian Inst Technol Guwahati Dept Math Gauhati 781039 Assam India

The gamma distribution is used as a lifetime distribution widely in reliability analysis. Lifetime data are often left truncated, and right censored. The EM algorithm is developed here for the estimation of the scale and shape parameters of the gamma distribution based on left truncated and right censored data. The Newton-Raphson method is also used for the same purpose, and then these two methods of estimation are compared through an extensive Monte Carlo simulation study. The asymptotic variance-covariance matrix of the MLEs under the EM framework is obtained by using the missing information principle (Louis, 1982). Then, the asymptotic confidence intervals for the parameters are constructed. The confidence intervals based on the EM algorithm and the Newton-Raphson method are then compared empirically in terms of coverage probabilities. Finally, all the methods of inference discussed here are illustrated through a numerical example.

关键词： Asymptotic variance-covariance matrix coverage probability expectation maximization algorithm gamma distribution information matrix left truncation lifetime data maximum likelihood estimates missing information principle Monte Carlo simulation right censoring

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Identification of Hammerstein-Wiener models

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AUTOMATICA 2013年第1期49卷 70-81页

作者： Wills, Adrian Schon, Thomas B. Ljung, Lennart Ninness, Brett Univ Newcastle Sch Elect Engn & Comp Sci Callaghan NSW 2308 Australia Linkoping Univ Div Automat Control SE-58183 Linkoping Sweden

This paper develops and illustrates a new maximum-likelihood based method for the identification of Hammerstein-Wiener model structures. A central aspect is that a very general situation is considered wherein multivariable data, non-invertible Hammerstein and Wiener nonlinearities, and colored stochastic disturbances both before and after the Wiener nonlinearity are all catered for. The method developed here addresses the blind Wiener estimation problem as a special case. (C) 2012 Elsevier Ltd. All rights reserved.

关键词： System identification Hammerstein Wiener Block-oriented models Nonlinear models Dynamic systems Monte Carlo method Smoothing expectation maximization algorithm Particle methods Maximum Likelihood

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Pricing fraud detection in online shopping malls using a finite mixture model

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ELECTRONIC COMMERCE RESEARCH AND APPLICATIONS 2013年第3期12卷 195-207页

作者： Kim, Kwanho Choi, Yerim Park, Jonghun Seoul Natl Univ Dept Ind Engn Seoul 151744 South Korea

Although pricing fraud is an important issue for improving service quality of online shopping malls, research on automatic fraud detection has been limited. In this paper, we propose an unsupervised learning method based on a finite mixture model to identify pricing frauds. We consider two states, normal and fraud, for each item according to whether an item description is relevant to its price by utilizing the known number of item clusters. Two states of an observed item are modeled as hidden variables, and the proposed models estimate the state by using an expectation maximization (EM) algorithm. Subsequently, we suggest a special case of the proposed model, which is applicable when the number of item clusters is unknown. The experiment results show that the proposed models are more effective in identifying pricing frauds than the existing outlier detection methods. Furthermore, it is presented that utilizing the number of clusters is helpful in facilitating the improvement of pricing fraud detection performances. (C) 2013 Elsevier B.V. All rights reserved.

关键词： Pricing fraud Fraud detection Online shopping e-Commerce expectation maximization algorithm Finite mixture model

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Application of Bayesian Model Averaging Approach to Multimodel Ensemble Hydrologic Forecasting

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JOURNAL OF HYDROLOGIC ENGINEERING 2013年第11期18卷 1426-1436页

作者： Liang, Zhongmin Wang, Dong Guo, Yan Zhang, Yu Dai, Rong Hohai Univ Coll Hydrol & Water Resources Nanjing 210098 Jiangsu Peoples R China Northwest Hydro Consulting Engn CHECC Xian 710065 Peoples R China

Bayesian model averaging (BMA) is a statistical method that can synthesize the advantages of different models or methods. The objective of this research is to explore the use of BMA to forecast combinations among several hydrological models. BMA is a statistical scheme that infers the posterior distribution of forecasting variables by weighing individual posterior distributions based on their probabilistic likelihood measures, with the better performing predictions receiving higher weights than the worse predictions. The Topographic Kinematic Approximation and Integration and Xin'anjiang models were applied to the Dongwan basin, Yellow River, China, for flood simulation. Observed and simulated discharge time series were transformed into normally distributed variables through the normal quantile transform. The Gaussian mixture model was constructed by weighing the posterior distribution of individual hydrological models in the transformed space. The posterior probability measuring samples belonging to each specific hydrological model were treated as the weights. The parameters of the Gaussian mixture model and the weight of each hydrological model were estimated by the expectation maximization algorithm. Thus, the forecast combination in the catchment was obtained from the two hydrological models. For flood forecasting, the results provided not only the mean discharge values, but also quantitative evaluation of forecasting uncertainties (e.g.,standard deviation and confidence interval), because the BMA approach calculated the estimation of the probability distribution of forecasted variables. (C) 2013 American Society of Civil Engineers.

关键词： Bayesian analysis Hydrology Forecasting algorithms Uncertainty principles Bayesian model averaging Multimodel ensemble hydrologic forecasting Hydrological model Normal quantile transform Gaussian mixture model expectation maximization algorithm Forecasting uncertainty

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Probabilistic typology of management decision making in private forest properties

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FOREST POLICY AND ECONOMICS 2013年 27卷 34-43页

作者： Ficko, Andrej Boncina, Andrej Univ Ljubljana Biotech Fac Dept Forestry & Renewable Forest Resources Ljubljana 1000 Slovenia

We conducted a quantitative study of private forest owner management behavior based on face-to-face interviews with 380 randomly selected private forest owners in Slovenia. Forest owners were asked to rate the relevance of nineteen factors representing information related to the social, ecological, and economic aspects of decision making based on a five-point Likert scale. This information was consolidated into major categories with Principal Component Analysis. expectation maximization (EM) clustering was used to build a probabilistic private forest owner derision making typology. Six major categories of information determined 64% of the variability in decision making: non-wood goods and services, forest economics, property administration, optimization of wood production, forest protection, and minimum cutting restrictions. EM clustering revealed two decision making types differing in their attitude towards the total economic value of forests: Materialists, whose decisions are mainly related to the extractive value of forests and Non-materialists, who manage for non-extractive vale. Full-time farmers, owners living within 2 km of their holdings, and owners who permanently cooperated with the public forest service were much more likely to be Materialists. The uncertainty in private forest owner typology building and the applicability of probabilistic models of private forest owners to end-users is discussed. (C) 2012 Elsevier B.V. All rights reserved.

关键词： Private forest owners Value of information Management decision Management behavior expectation maximization algorithm Slovenia

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