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...
详细信息
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.
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....
详细信息
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.
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...
详细信息
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.
This paper presents an automatic segmentation procedure for MRI neuroimages, that overcomes part of the problems involved in multidimensional clustering techniques like partial volume effects (PVE), processing speed a...
详细信息
ISBN:
(纸本)0819440086
This paper presents an automatic segmentation procedure for MRI neuroimages, that overcomes part of the problems involved in multidimensional clustering techniques like partial volume effects (PVE), processing speed and difficulty of incorporating a priori knowledge. The method is a three-stage procedure: 1) Exclusion of background and skull voxels using threshold-based region growing techniques with fully automated seed selection. 2) Expectation Maximization algorithms are used to estimate the probability density function (PDF) of the remaining pixels, which are assumed to be mixtures of gaussians. These pixels can then be classified into cerebrospinal fluid (CSF), white matter and grey matter. Using this procedure, our method takes advantage of using the full covariance matrix (instead of the diagonal) for the joint PDF estimation. On the other hand, logistic discrimination techniques are more robust against violation of multi-gaussian assumptions. 3) A priori knowledge is added using Markov Random Field techniques. The algorithm has been tested with a dataset of 30 brain MRI studies (co-registered T1 and T2 MRI). Our method was compared with clustering techniques and with template-based statistical segmentation, using manual segmentation as a 'gold-standard'. Our results were more robust and closer to the gold-standard.
Since the detection of optical flow (two-dimensional motion field on an image) from image sequences is essentially an ill-posed problem, most of the conventional methods use a smoothness constraint for optical how heu...
详细信息
Since the detection of optical flow (two-dimensional motion field on an image) from image sequences is essentially an ill-posed problem, most of the conventional methods use a smoothness constraint for optical how heuristically and detect reasonable optical flow. However, little discussion exists regarding the degree of smoothness. Furthermore, to recover the relative three-dimensional motion and depth between a camera and a rigid object, in general at first, the optical flow is detected without a rigid motion constraint, and next, the motion and depth are: estimated using the detected optical flow. Rigorously speaking, the optical how should be detected with such a constraint, and consequently three-dimensional motion and depth should be determined. To solve these problems, in this paper, we apply a parametric model to an optical flow, and construct an estimation algorithm based on this model.
The problem of nonparametric estimation for the distribution function governing the time to occurrence of a recurrent event in the presence of censoring is considered. We derive Nelson-Aalen and Kaplan-Meier-type esti...
详细信息
The problem of nonparametric estimation for the distribution function governing the time to occurrence of a recurrent event in the presence of censoring is considered. We derive Nelson-Aalen and Kaplan-Meier-type estimators for the distribution function, and establish their respective finite-sample and asymptotic properties. We allow for random observation periods for each subject under study and explicitly account for the informative sum-quota nature of the data accrual scheme. These allowances complicate technical matters considerably and, in particular, invalidate the direct use of martingale methods. Consistency and weak convergence of our estimators are obtained by extending an approach due to Sellke, who considered a single renewal process (i.e., recurrent events on a single subject) observed over an infinite time period. A useful feature of the present analysis is that strong parallels are drawn to the usual "single-event'' setting, providing a natural route toward developing extensions that involve covariates weighted log-rank tests, Cox-type regression, and frailty models). Another advantage is that we obtain explicit, closed-form expressions for the asymptotic variances for these estimators. This enables, for instance, the characterization of the efficiency loss that results from employing only the first, possibly right-censored, observation per subject. An interesting feature of these results is the prominent role of the renewal function. Finally, we discuss the case of correlated interoccurrence times, propose an estimator in the case where the within-unit interoccurrence times follow a gamma frailty model, and compare the performance of our estimators to an estimator recently proposed by Wang and Chang.
The em algorithm is a popular and useful algorithm for finding the maximum likelihood estimator in incomplete data problems. Each iteration of the algorithm consists of two simple steps: an E-step, in which a conditio...
详细信息
The em algorithm is a popular and useful algorithm for finding the maximum likelihood estimator in incomplete data problems. Each iteration of the algorithm consists of two simple steps: an E-step, in which a conditional expectation is calculated, and an M-step, where the expectation is maximized. In some problems, however, the em algorithm cannot be applied since the conditional expectation required in the E-step cannot be calculated. Instead the expectation may be estimated by simulation. We call this a simulated em algorithm. The simulations can, at least in principle, be done in two ways. Either new independent random variables are drawn in each iteration, or the same uniforms are re-used in each iteration. In this paper the properties of these two versions of the simulated em algorithm an discussed and compared. (C) 2000 Elsevier Science S.A. All rights reserved.
The ubiquitous supermarket checkout scanner is indeed a well engineered and effective device, There is, nevertheless, demand for better devices. Existing scanners rely on simple and indeed low-cost signal processing t...
详细信息
The ubiquitous supermarket checkout scanner is indeed a well engineered and effective device, There is, nevertheless, demand for better devices. Existing scanners rely on simple and indeed low-cost signal processing to interpret bar code signals, These methods, nevertheless, fundamentally limit label reading and cannot be extended, A new method based on the deterministic em algorithm Is described here, First results show a substantial improvement in label reading depth of field, which is an important performance parameter for bar code readers.
Using a theory of list-mode maximum-likelihood (ML) source reconstruction presented recently by Barrett et al. [1], this paper formulates a corresponding expectation-maximization (em) algorithm, as well as a method fo...
详细信息
Using a theory of list-mode maximum-likelihood (ML) source reconstruction presented recently by Barrett et al. [1], this paper formulates a corresponding expectation-maximization (em) algorithm, as well as a method for estimating noise properties at the ML estimate, List-mode ML is of interest in cases where the dimensionality of the measurement space impedes a binning of the measurement data. It can be advantageous in cases where a better forward model can be obtained by including more measurement coordinates provided by a given detector. Different figures of merit for the detector performance can be computed from the Fisher information matrix (FIM). This paper uses the observed FIM, which requires a single data set, thus, avoiding costly ensemble statistics, The proposed techniques are demonstrated for an idealized two-dimensional (2-D) positron emission tomography (PET) [2-D PET] detector. We compute from simulation data the improved image quality obtained by including the time of flight of the coincident quanta.
This paper presents a deterministic annealing em (DAem) algorithm for maximum likelihood estimation problems to overcome a local maxima problem associated with the conventional em algorithm. In our approach, a new pos...
详细信息
This paper presents a deterministic annealing em (DAem) algorithm for maximum likelihood estimation problems to overcome a local maxima problem associated with the conventional em algorithm. In our approach, a new posterior parameterized by 'temperature' is derived by using the principle of maximum entropy and is used for controlling the annealing process. In the DAem algorithm, the em process is reformulated as the problem of minimizing the thermodynamic free energy by using a statistical mechanics analogy. Since this minimization is deterministically performed at each temperature, the total search is executed far more efficiently than in the simulated annealing. Moreover, the derived DAem algorithm, unlike the conventional em algorithm, can obtain better estimates free of the initial parameter values. We also apply the DAem algorithm to the training of probabilistic neural networks using mixture models to estimate the probability density and demonstrate the performance of the DAem algorithm. (C) 1998 Elsevier Science Ltd. All rights reserved.
暂无评论