Serial dilution assays are widely employed for estimating substance concentrations and minimum inhibitory concentrations. The Poisson-Bernoulli model for such assays is appropriate for count data but not for continuou...
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Serial dilution assays are widely employed for estimating substance concentrations and minimum inhibitory concentrations. The Poisson-Bernoulli model for such assays is appropriate for count data but not for continuous measurements that are encountered in applications involving substance concentrations. This paper presents practical inference methods based on a log-normal model and illustrates these methods using a case application involving bacterial toxins.
Difficulties arise with the generalized likelihood ratio test (GLRT) in situations where one or more of the unknown signal parameters requires an enumeration that is computationally intractable. In the transient signa...
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Difficulties arise with the generalized likelihood ratio test (GLRT) in situations where one or more of the unknown signal parameters requires an enumeration that is computationally intractable. In the transient signal detection problem, the frequency characteristics of the signal are typically unknown;therefore, even if an aggregate signal bandwidth is assumed, the estimation problem intrinsic to the GLRT requires an enumeration of all possible sets of signal locations within the monitored band. In this paper, a prior distribution is imposed over those portions of the signal parameter space that traditionally require enumeration. By replacing intractable enumeration over possible signal characteristics with an a priori signal distribution and by estimating the "hyperparameters" (of the prior distribution) jointly with other signal parameters, it is possible to obtain a new formulation of the GLRT that avoids enumeration and is computationally feasible, The GLRT philosophy is not changed by this approach-what is different from the original GLRT is the underlying signal model. The performance of this new approach appears to be competitive with that of a scheme of emerging acceptance: the "power-law" detector.
Chain-of-events data are longitudinal observations on a succession of events that can only occur in a prescribed order. One goal in an analysis of this type of data is to determine the distribution of times between th...
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Chain-of-events data are longitudinal observations on a succession of events that can only occur in a prescribed order. One goal in an analysis of this type of data is to determine the distribution of times between the successive events. This is difficult when individuals are observed periodically rather than continuously because the event times are then interval censored. Chain-of-events data may also be subject to truncation when individuals can only be observed if a certain event in the chain (e.g., the final event) has occurred. We provide a nonparametric approach to estimate the distributions of times between successive events in discrete time for data such as these under the semi-Markov assumption that the times between events are independent. This method uses a self-consistency algorithm that extends Turnbull's algorithm (1976, Journal of the Royal Statistical Society, Series B 38, 290-295). The quantities required to carry out the algorithm can be calculated recursively for improved computational efficiency. Two examples using data from studies involving HIV disease are used to illustrate our methods.
The purpose of this paper is to present and evaluate a heuristic algorithm for learning Bayesian networks for clustering. Our approach is based upon improving the Naive-Bayes model by means of constructive induction. ...
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The purpose of this paper is to present and evaluate a heuristic algorithm for learning Bayesian networks for clustering. Our approach is based upon improving the Naive-Bayes model by means of constructive induction. A key idea in this approach is to treat expected data as real data. This allows us to complete the database and to take advantage of factorable closed forms for the marginal likelihood. In order to get such an advantage, we search for parameter values using the em algorithm or another alternative approach that we have developed: a hybridization of the Bound and Collapse method and the em algorithm, which results in a method that exhibits a faster convergence rate and a more effective behaviour than the em algorithm. Also, we consider the possibility of interleaving runnings of these two methods after each structural change. We evaluate our approach on synthetic and real-world databases. (C) 1999 Elsevier Science B.V. All rights reserved.
In this paper we examine the problem of estimating a stochastic signal from noise corrupted linearly distorted samples of the original. Due to the ill-posedness caused by the blurring function, we are motivated to exa...
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In this paper we examine the problem of estimating a stochastic signal from noise corrupted linearly distorted samples of the original. Due to the ill-posedness caused by the blurring function, we are motivated to examine an inversion method in which the statistics of the underlying process are modeled as a 1/f type fractal process. In particular, we explore two issues with the use of such a model: the effects of model mismatch and parameter estimation. Our analysis demonstrates that the mean-square-error performance of the estimator is quite insensitive to the choice of prior model parameters used in the recovery of the signal. Such robustness is shown to hold even when the underlying process is not of the 1/f variety. We then introduce an expectation-maximization technique for jointly extracting the best parameters for use in an inversion along with the reconstructed signal. Here, Monte Carlo and Cramer-Rao bound results demonstrate that we are able to determine accurate model parameters exactly in those situations where the model mismatch analysis shows that such fidelity is required to ensure low mean square error in the recovery of the underlying signal. (C) 1999 Elsevier Science B.V. All rights reserved.
In this paper a method to automatically generate a Gaussian mixture classifier is presented. The growing process is based on the iterative addition of Gaussian nodes. Each iteration takes place in two sequential steps...
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In this paper a method to automatically generate a Gaussian mixture classifier is presented. The growing process is based on the iterative addition of Gaussian nodes. Each iteration takes place in two sequential steps: first, using the em algorithm, we maximize the likelihood of the data under the current configuration of the classifier;then, a new Gaussian node is added to the class which most improves the discriminant capabilities of the network. Growth control is imposed by means of a complexity penalizing term and a discriminant MMI condition. The classical em algorithm for Gaussian mixtures is also extended to jointly include labeled and unlabeled data. We report some artificial experiments that show the utility of this extension and the reliability of the proposed growing technique. We also report results of the Growing Gaussian Mixtures Network on terrain classification over a Landsat-TM image using different restrictions on the covariance matrix of the Gaussian mixtures. Comparisons in classification performance with a set of MLP neural networks are provided. (C) 1999 Elsevier Science B.V. All rights reserved.
Mixed effects models are often used for estimating fixed effects and variance components in longitudinal studies of continuous data. When the outcome being modelled is a laboratory measurement, however, it may be subj...
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Mixed effects models are often used for estimating fixed effects and variance components in longitudinal studies of continuous data. When the outcome being modelled is a laboratory measurement, however, it may be subject to lower and upper detection limits (i.e., censoring). In this paper, the usual em estimation procedure for mixed effects models is modified to account for left and/or right censoring.
The occurrence of different forms of asymmetry complicates the analysis and interpretation of patterns in asymmetry. Furthermore, between-individual heterogeneity in developmental stability (DS) and thus fluctuating a...
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The occurrence of different forms of asymmetry complicates the analysis and interpretation of patterns in asymmetry. Furthermore, between-individual heterogeneity in developmental stability (DS) and thus fluctuating asymmetry (FA), is required to find relationships between DS and other factors. Separating directional asymmetry (DA) and antisymmetry (AS) from real FA and understanding between-individual heterogeneity in FA is therefore crucial in the analysis and interpretation of patterns in asymmetry. In this paper we introduce and explore mixture analysis to (i) identify FA, DA and AS from the distribution of the signed asymmetry, and (ii) to model and quantify between-individual heterogeneity in developmental stability and FA. In addition, we expand mixtures to the estimation of the proportion of variation in the unsigned FA that can be attributed to between-individual heterogeneity in the presumed underlying developmental stability (the so-called hypothetical repeatability). Finally, we construct weighted normal probability plots to investigate the assumption of underlying normality of the different components. We specifically show that (i) model selection based on the likelihood ratio test has the potential to yield models that incorporate nearly all heterogeneity in FA;(ii) mixtures appear to be a powerful and sensitive statistical technique to identify the different forms of asymmetry;(iii) restricted measurement accuracy and the occurrence of many zero observations results in an overestimation of the hypothetical repeatability on the basis of the model parameters;and (iv) as judged from the high correlation coefficients of the normal probability plots, the underlying normality assumption appears to hold for the empirical data we analysed. In conclusion, mixtures provide a useful statistical tool to study patterns in asymmetry.
作者：
Marchette, DJPoston, WLUSN
Computat Stat Grp Ctr Surface Warfare Dahlgren VA 22448 USA USN
Adv Processors Grp Ctr Surface Warfare Dahlgren VA 22448 USA
In automatic pattern recognition applications, numerous features that describe the classes are obtained in an attempt to ensure accurate classification of unknown observations. These features or dimensions must be red...
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In automatic pattern recognition applications, numerous features that describe the classes are obtained in an attempt to ensure accurate classification of unknown observations. These features or dimensions must be reduced to a smaller number before classification schemes can be applied, because classifiers become computationally and analytically unmanageable in high dimensions;Principal components and Fisher's Linear Discriminant offer global dimensionality reduction within the framework of linear algebra applied to covariance matrices. This report describes local methods that use both mixture-models and nearest neighbor calculations to construct local versions of these methods. These new versions for local dimensionality reduction will provide increased classification accuracy in lower dimensions.
We describe a semiparametric mixture model for human fertility studies. The probability of conception is a product of two components. The mixing distribution, the component that introduces the heterogeneity among the ...
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We describe a semiparametric mixture model for human fertility studies. The probability of conception is a product of two components. The mixing distribution, the component that introduces the heterogeneity among the menstrual cycles that come from different couples, is characterized nonparametrically by a finite number of moments. The second component, the intercourse-related probability is modeled parametrically to assess the possible exposure effects. We discuss an em algorithm-based estimating procedure that incorporates the natural order in the moments. (C) 1999 Elsevier Science B.V. All rights reserved.
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