We propose a method for estimating parameters in the generalised linear mixed model with nonignorable missing response data and with nonmonotone patterns of missing data in the response variable. We develop a Monte Ca...
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We propose a method for estimating parameters in the generalised linear mixed model with nonignorable missing response data and with nonmonotone patterns of missing data in the response variable. We develop a Monte Carlo em algorithm for estimating the parameters in the model via the Gibbs sampler. For the normal random effects model, we derive a novel analytical form for the E- and M-steps, which is facilitated by integrating out the random effects. This form leads to a computationally feasible and extremely efficient Monte Carlo em algorithm for computing maximum likelihood estimates and standard errors. In addition, we propose a very general joint multinomial model for the missing data indicators, which can be specified via a sequence of one-dimensional conditional distributions. This multinomial model allows for an arbitrary correlation structure between the missing data indicators, and has the potential of reducing the number of nuisance parameters. Real datasets from the International Breast Cancer Study Group and an environmental study involving dyspnoea in cotton workers are presented to illustrate the proposed methods.
The expectation-maximization (em) algorithm isa powerful computational technique for locating maxima of functions. It is widely used in statistics for maximum likelihood or maximum a posteriori estimation in incomplet...
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The expectation-maximization (em) algorithm isa powerful computational technique for locating maxima of functions. It is widely used in statistics for maximum likelihood or maximum a posteriori estimation in incomplete data models. In certain situations, however, this method is not applicable because the expectation step cannot be performed in closed form. To deal with these problems, a novel method is introduced, the stochastic approximation em (SAem), which replaces the expectation step of the em algorithm by one iteration of a stochastic approximation procedure. The convergence of the SAem algorithm is established under conditions that are applicable to many practical situations. Moreover,it is proved that, under mild additional conditions, the attractive stationary points of the SAem algorithm correspond to the local maxima of the function. presented to support our findings.
An application of a latent class vector model to preference data is presented. Analysing hedonic ratings, this technique realises simultaneously the clustering of consumers in homogeneous classes on the basis of their...
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An application of a latent class vector model to preference data is presented. Analysing hedonic ratings, this technique realises simultaneously the clustering of consumers in homogeneous classes on the basis of their preferences and the joint representation of products and classes using a vector model. A probabilistic assumption allows performance of significance tests on the number of clusters and provides a useful tool for interpreting results of preference tests. (C) 2001 Elsevier Science Ltd. All rights reserved.
This work presents an application of the em-algorithm to two problems of estimation and testing in a multivariate normal distribution with missing data. The assumptions are that the observations are multivariate norma...
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This work presents an application of the em-algorithm to two problems of estimation and testing in a multivariate normal distribution with missing data. The assumptions are that the observations are multivariate normally distributed and that the missing values are missing at random. The two models are tested applying the log-likelihood ratio test;for deriving the maximum likelihood estimates and evaluating the corresponding log-likelihood functions the em algorithm is used. The problem of different and non-monotone patterns of missing data is solved introducing suitable transformations and partitions of the data matrix. The algorithm is proposed for general constraints on the mean vector;the topic of exchangeability of random vectors is also presented.
We consider a generalized linear model with canonical link when some of the covariates are subject to measurement error. In this article we propose a likelihood based method of adjusting the estimate of the regression...
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The paper proposes a hidden semi-Markov model for breakpoint rainfall data that consist of both the times at which rain-rate changes and the steady rates between such changes. The model builds on and extends the semin...
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The Probabilistic Multiple Hypothesis Tracker (PMHT) has previously been augmented and modified to deal with target maneuver. Unfortunately, although the resulting procedure tracks maneuvering targets reasonably well,...
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ISBN:
(纸本)0819441872
The Probabilistic Multiple Hypothesis Tracker (PMHT) has previously been augmented and modified to deal with target maneuver. Unfortunately, although the resulting procedure tracks maneuvering targets reasonably well, estimation of the maneuver process (i.e. the hidden Markov Model (HMM)) is not particularly reactive. In this paper, the PMHT is further investigated and several PMHT variants for maneuvering targets are discussed. These include: the ideas from Logothetis et al. and from Pulford and La Scala;the incorporation of the Interacting Multiple Mode (IMM) formalism to the PMHT;the extension of the "turbo" PMHT. We finally compare these em-based tracking schemes and provide the simulation results on the second benchmark problem from Blair et al.
The U.S. component of the International Reading Literacy Study provides a data set where nonresponses to the background questionnaire items were filled in using imputation methods (mainly hot-deck). This study uses th...
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The U.S. component of the International Reading Literacy Study provides a data set where nonresponses to the background questionnaire items were filled in using imputation methods (mainly hot-deck). This study uses the completed data set for analyses and compares the results with those from other methods of handling missing data. Analyses conducted include regression and hierarchical linear models. The imputed data set yields results similar to those produced by available case analyses (pairwise deletion) and by the estimation and maximization algorithm analyses. The results, however, are different from those produced by complete case analyses (casewise deletion). For most analyses of the Reading Literacy Study, the data set completed by imputation is a convenient option.
In functional magnetic resonance images (fMRI), to accurately detect functional activation areas, it is necessary to eliminate the physiological movements of subject, which cause false activation areas, from fMRI time...
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ISBN:
(纸本)0780372115
In functional magnetic resonance images (fMRI), to accurately detect functional activation areas, it is necessary to eliminate the physiological movements of subject, which cause false activation areas, from fMRI time series data. This paper proposes a method for estimation of not only rigid-body motion such as gross head motion, but also non-rigid-body motion like pulsatile blood and cerebrospinal fluid (CSF) flow. Our method estimates these types of movements by using optical flow on a pixel-by-pixel basis. We extend generalized gradient schemes in order to compute probability distributions of optical flow. The crux of our method is to compute optical flow on a pixel-by-pixel basis. Although many other methods assume that the subject movement is rigid-body motion, our method does not require this assumption. We demonstrate that the detection of brain activation areas can be improved by motion correction based on our method.
The possibility of using surrogate variables (e.g., school grades, other test scores, examinee background information) as replacements for common items predicting sample-selection bias between groups was investigated....
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The possibility of using surrogate variables (e.g., school grades, other test scores, examinee background information) as replacements for common items predicting sample-selection bias between groups was investigated. The problem was specified as an incomplete data problem of comparability studies and was addressed using nonequivalent groups. A general model for estimating complete data (fitted) distributions through covariates is proposed (including common-item scores and surrogate variables as special cases). Model parameters are estimated using the em algorithm. Standard errors of comparable scores are derived under the proposed model. Data from an empirical example examined the use of surrogate variables for establishing score comparability.
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