Many approaches have been studied for the array processing problem when the additive noise is modeled with a Gaussian distribution, but these schemes typically perform poorly when the noise is non-Gaussian and/or impu...
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Many approaches have been studied for the array processing problem when the additive noise is modeled with a Gaussian distribution, but these schemes typically perform poorly when the noise is non-Gaussian and/or impulsive. This paper is concerned with maximum likelihood array processing in non-Gaussian noise. We present the Cramer-Rao bound on the variance of angle-of-arrival estimates for'arbitrary additive, independent, identically distributed (iid), symmetric, non-Gaussian noise. Then, we focus on non-Gaussian noise modeling with a finite Gaussian mixture distribution, which is capable of representing a broad class of non-Gaussian distributions that include heavy tailed, impulsive cases arising in wireless communications and other applications. Based on the Gaussian mixture model, we develop an expectation-maximization (em) algorithm for estimating the source locations, the signal waveforms, and the noise distribution parameters. The important problems of detecting the number of sources and obtaining initial parameter estimates for the iterative em algorithm are discussed in detail. The initialization procedure by itself is an effective algorithm for array processing in impulsive noise. Novel features of the em algorithm and the associated maximum likelihood formulation include a nonlinear beamformer that separates multiple source signals in non-Gaussian noise and a robust covariance matrix estimate that suppresses impulsive noise while also performing a model-based interpolation to restore the low-rank signal subspace. The em approach yields improvement over initial robust estimates and is valid for a nide SNR range. The results are also robust to pdf model mismatch and work well with infinite variance cases such as the symmetric stable distributions. Simulations confirm the optimality of the em estimation procedure in a variety of cases, including a multiuser communications scenario. We also compare with existing array processing algorithms for non-Gaussian no
Clustering about principal curves combines parametric modeling of noise with nonparametric modeling of feature shape. This is useful for detecting curvilinear features in spatial point patterns, with or without backgr...
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Clustering about principal curves combines parametric modeling of noise with nonparametric modeling of feature shape. This is useful for detecting curvilinear features in spatial point patterns, with or without background noise. Applications include the detection of curvilinear minefields from reconnaissance images, some of the points in which represent false detections, and the detection of seismic faults from earthquake catalogs. Our algorithm for principal curve clustering is in two steps: The first is hierarchical and agglomerative (HPCC) and the second consists of iterative relocation based on the Classification em algorithm (Cem-PCC). HPCC is used to combine potential feature clusters, while Cem-PCC refines the results and deals with background noise. It is important to have a good starting point for the algorithm: This can be found manually or automatically using, for example, nearest neighbor clutter removal or model-based clustering. We choose the number of features and the amount of smoothing simultaneously, using approximate Bayes factors.
We generalize the Gaussian mixture transition distribution (GMTD) model introduced by Le and co-workers to the mixture autoregressive (MAR) model for the modelling of non-linear time series. The models consist of a mi...
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We generalize the Gaussian mixture transition distribution (GMTD) model introduced by Le and co-workers to the mixture autoregressive (MAR) model for the modelling of non-linear time series. The models consist of a mixture of K stationary or non-stationary AR components. The advantages of the MAR model over the GMTD model include a more full range of shape changing predictive distributions and the ability to handle cycles and conditional heteroscedasticity in the time series. The stationarity conditions and autocorrelation function are derived. The estimation is easily done via a simple em algorithm and the model selection problem is addressed. The shape changing feature of the conditional distributions makes these models capable of modelling time series with multimodal conditional distributions and with heteroscedasticity. The models are applied to two real data sets and compared with other competing models. The MAR models appear to capture features of the data better than other competing models do.
Missing data can rarely be avoided in large scale studies in which subjects are requested to complete questionnaires with many items. Analyses of such surveys are often based on the records with no missing items, resu...
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Missing data can rarely be avoided in large scale studies in which subjects are requested to complete questionnaires with many items. Analyses of such surveys are often based on the records with no missing items, resulting in a loss of efficiency and, when data are missing not at random, in bias. This paper applies the method of multiple imputation to handle missing data in an analysis of alcohol consumption of the subjects in the Medical Research Council National Survey of Health and Development. The outcomes studied are derived from the entries in diaries of food and drink intake over seven designated days. Background Variables and other responses related to alcohol consumption and associated problems are used as collateral information. In conventional analyses, subpopulation means of quantities of alcohol consumed are compared. Since we are interested in the harmful effects of alcohol, we make inferences about the percentages of those who consume more than a given quantity of net alcohol. We assess the contribution to the analyses made by the incomplete records and outline a more integrated way of applying multiple imputation in large scale longitudinal surveys.
A problem that frequently occurs in biological experiments with laboratory animals is that some subjects are less susceptible to the treatment than others. A mixture model has traditionally been proposed to describe t...
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A problem that frequently occurs in biological experiments with laboratory animals is that some subjects are less susceptible to the treatment than others. A mixture model has traditionally been proposed to describe the distribution of responses in treatment groups for such experiments. Using a mixture dose-response model, Re derive an upper confidence limit on additional risk, defined as the excess risk over the background risk due to an added dose. Our focus will be on experiments with continuous responses for which risk is the probability of an adverse effect defined as an event that is extremely rare in controls. The asymptotic distribution of the likelihood ratio statistic is used to obtain the upper confidence limit on additional risk. The method can also be used to derive a benchmark dose corresponding to a specified level of increased risk. The em algorithm is utilized to find the maximum likelihood estimates of model parameters and an extension of the algorithm is proposed to derive the estimates when the model is subject to a specified level of added risk. An example is used to demonstrate the results, and it is shown that by using the mixture model a more accurate measure of added risk is obtained.
This paper is concerned with maximum likelihood (ML) parameter estimation of continuous-time nonlinear partially observed stochastic systems, via the expectation maximization (em) algorithm. It is shown that the em al...
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This paper is concerned with maximum likelihood (ML) parameter estimation of continuous-time nonlinear partially observed stochastic systems, via the expectation maximization (em) algorithm. It is shown that the em algorithm can be executed efficiently, provided the unnormalized conditional density of nonlinear filtering is either explicitly solvable or numerically implemented. The methodology exploits the relationships between incomplete and complete data, log likelihood and its gradient.
Maximum likelihood (ML) estimates of K-distribution parameters are derived using the expectation maximization (em) approach, This approach demonstrates computational advantages compared with 2-D numerical maximization...
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Maximum likelihood (ML) estimates of K-distribution parameters are derived using the expectation maximization (em) approach, This approach demonstrates computational advantages compared with 2-D numerical maximization of the likelihood function using a Nelder-Mead approach. For large datasets, the em approach yields more accurate estimates than those of a non-ML estimation technique.
The determination of toxicokinetic parameters is an essential component in the risk assessment of potential harmful chemicals. It is a key step to analyse the processes involved in the formation of DNA adducts which a...
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The determination of toxicokinetic parameters is an essential component in the risk assessment of potential harmful chemicals. It is a key step to analyse the processes involved in the formation of DNA adducts which are connected with the development of chemical-induced cancer. A general problem is the extrapolation of toxicological data from experimental animals to the human organism. The basis of a toxicokinetic species extrapolation are physiologically-based pharmacokinetic models, which require detailed information about physiological parameters as well as about the kinetic processes involved. Fundamental in the extrapolation from one species to another is the characterisation of processes valid for the whole species, i.e. of population mean parameters instead of sets of parameters for different individuals. These, again, may vary between repeated experiments at the same or at different administered doses. Nevertheless, these differences are of great importance in obtaining a more precise insight into the variability structure of process investigated within the test animal population, so that a valid basis for further research is the final result. The theory of hierarchical models provides a procedure which incorporates both modelling of the variability structure and estimation of population mean parameter vectors. The present study was designed to elucidate interindividual and interoccasion variabilities of toxicokinetic parameters relevant for the biological transformation of one of the basic petrochemical industrial compounds, ethylene (ethene), which is also a physiological body constituent, to its metabolite, ethylene oxide, which is a proven carcinogen. In particular, this aspect has a potential impact for legal regulations of weak genotoxins in general. Copyright (C) 2000 John Wiley & Sons, Ltd.
An adaptive receiver that uses multiple antennas to provide diversity against fading is developed for operation in an impulsive noise environment. The noise components at each sensor are assumed to be correlated. A mi...
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An adaptive receiver that uses multiple antennas to provide diversity against fading is developed for operation in an impulsive noise environment. The noise components at each sensor are assumed to be correlated. A mixture of multivariate Gaussian distributions is used to model the noise. Using a training sequence, model parameters are estimated by iterative procedures derived from the expectation-maximization (em) algorithm. These estimated parameters are then used in a likelihood ratio test to recover the transmitted signals. Simulations show that the proposed adaptive receiver is robust, and near-optimum performance can be achieved when sufficient training data is available.
作者：
McClean, SDevine, CUniv Ulster
Sch Informat & Softwar Engn Fac Informat Coleraine BT52 1SA Londonderry North Ireland
In manpower planning it is commonly the case that employees withdraw from active service for a period of time before returning to take up post at a later date. Such periods of absence are frequently of major concern t...
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In manpower planning it is commonly the case that employees withdraw from active service for a period of time before returning to take up post at a later date. Such periods of absence are frequently of major concern to employers who are anxious to ensure that employees return as soon as possible. The distribution of duration of such periods of absence are therefore of considerable interest as is the probability that such employees will ever return to active service. In this paper we derive a nonparametric estimator for such a lifetime distribution based on renewal data which are subject to various forms of incompleteness, namely right censoring, left and right truncation, and forward recurrence. Artificial truncation is used to ensure that the data are time homogeneous. A nonparametric maximum likelihood estimator for the lifetime distribution is derived using the em algorithm. The data analysed concern the Northern Ireland nursing profession.
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