Delsterol in edible powder; Du Pont vitamin D ...

Mode of access: Internet.

A lecture on the mixture of civil and ecclesiastical power : in the governments of the Middle Ages /

"Delivered at the tabernacle, on Monday evening, December 18, 1843, by request of the Irish Emigrant Society."

The hierarchical despotism : lectures on the mixture of civil and ecclesiastical power in the governments of the Middle Ages : in illustration of the nature and progress of despotism in the Romish Church /

A reply to Bishop John Hughes' "A lecture on the mixture of civil and ecclesiastical power, in the governments of the Middle Ages."

Powder and propellants : energetic materials at Indian Head, Maryland, 1890-1990 /

Shipping list no.: 90-601-P.

A revision of the New World species of powder-post beetles belonging to the family Lyctidae /

Selected references: p. 46-55.

North African powder keg.

Mode of access: Internet.

Powder metallurgy; principles and methods.

Mode of access: Internet.

Resource management plan/environmental impact statement for the Powder River Resource Area : Miles City district, Montana : draft /

Eight maps on folded sheets in 2 pockets.

The powder of sympathy,

Mode of access: Internet.

Finite mixture regression model with random effects: application to neonatal hospital length of stay

A two-component mixture regression model that allows simultaneously for heterogeneity and dependency among observations is proposed. By specifying random effects explicitly in the linear predictor of the mixture probability and the mixture components, parameter estimation is achieved by maximising the corresponding best linear unbiased prediction type log-likelihood. Approximate residual maximum likelihood estimates are obtained via an EM algorithm in the manner of generalised linear mixed model (GLMM). The method can be extended to a g-component mixture regression model with the component density from the exponential family, leading to the development of the class of finite mixture GLMM. For illustration, the method is applied to analyse neonatal length of stay (LOS). It is shown that identification of pertinent factors that influence hospital LOS can provide important information for health care planning and resource allocation. (C) 2002 Elsevier Science B.V. All rights reserved.

Modelling inpatient length of stay by a hierarchical mixture regression via the EM algorithm

The modelling of inpatient length of stay (LOS) has important implications in health care studies. Finite mixture distributions are usually used to model the heterogeneous LOS distribution, due to a certain proportion of patients sustaining-a longer stay. However, the morbidity data are collected from hospitals, observations clustered within the same hospital are often correlated. The generalized linear mixed model approach is adopted to accommodate the inherent correlation via unobservable random effects. An EM algorithm is developed to obtain residual maximum quasi-likelihood estimation. The proposed hierarchical mixture regression approach enables the identification and assessment of factors influencing the long-stay proportion and the LOS for the long-stay patient subgroup. A neonatal LOS data set is used for illustration, (C) 2003 Elsevier Science Ltd. All rights reserved.

On the sintering of uncompacted, pre-alloyed Al powder alloys

The effect of the addition of elemental Mg, Sri and Pb on the sintering of impressed prealloyed 2124 and 6061 powder has been investigated. Despite being sintered at a temperature that resulted in similar to20 vol.% liquid phase, high density was possible only upon the addition of similar to1% elemental Mg as well as trace amounts of Sri or Pb. Additions of Mg facilitated oxide disruption, while Sri and Pb segregated to the liquid phase, where they reduced the liquid's surface tension, thus improving sintering. (C) 2002 Elsevier Science B.V. All rights reserved.

On a resampling approach for tests on the number of clusters with mixture model-based clustering of tissue samples

We consider the problem of assessing the number of clusters in a limited number of tissue samples containing gene expressions for possibly several thousands of genes. It is proposed to use a normal mixture model-based approach to the clustering of the tissue samples. One advantage of this approach is that the question on the number of clusters in the data can be formulated in terms of a test on the smallest number of components in the mixture model compatible with the data. This test can be carried out on the basis of the likelihood ratio test statistic, using resampling to assess its null distribution. The effectiveness of this approach is demonstrated on simulated data and on some microarray datasets, as considered previously in the bioinformatics literature. (C) 2004 Elsevier Inc. All rights reserved.

Speeding up the EM algorithm for mixture model-based segmentation of magnetic resonance images

Mixture models implemented via the expectation-maximization (EM) algorithm are being increasingly used in a wide range of problems in pattern recognition such as image segmentation. However, the EM algorithm requires considerable computational time in its application to huge data sets such as a three-dimensional magnetic resonance (MR) image of over 10 million voxels. Recently, it was shown that a sparse, incremental version of the EM algorithm could improve its rate of convergence. In this paper, we show how this modified EM algorithm can be speeded up further by adopting a multiresolution kd-tree structure in performing the E-step. The proposed algorithm outperforms some other variants of the EM algorithm for segmenting MR images of the human brain. (C) 2004 Pattern Recognition Society. Published by Elsevier Ltd. All rights reserved.