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Penal block

Kuangnan Fang, Shuangge Ma
Data with a large p (number of covariates) and/or a large n (sample size) are now commonly encountered. For many problems, regularization especially penalization is adopted for estimation and variable selection. The straightforward application of penalization to large datasets demands a "big computer" with high computational power. To improve computational feasibility, we develop bootstrap penalization, which dissects a big penalized estimation into a set of small ones, which can be executed in a highly parallel manner and each only demands a "small computer"...
November 21, 2016: Biometrical Journal. Biometrische Zeitschrift
Bert-Ram Sah, Paul Stolzmann, Gaspar Delso, Scott D Wollenweber, Martin Hüllner, Yahya A Hakami, Marcelo A Queiroz, Felipe de Galiza Barbosa, Gustav K von Schulthess, Carsten Pietsch, Patrick Veit-Haibach
PURPOSE: To investigate the clinical performance of a block sequential regularized expectation maximization (BSREM) penalized likelihood reconstruction algorithm in oncologic PET/computed tomography (CT) studies. METHODS: A total of 410 reconstructions of 41 fluorine-18 fluorodeoxyglucose-PET/CT studies of 41 patients with a total of 2010 lesions were analyzed by two experienced nuclear medicine physicians. Images were reconstructed with BSREM (with four different β values) or ordered subset expectation maximization (OSEM) algorithm with/without time-of-flight (TOF/non-TOF) corrections...
January 2017: Nuclear Medicine Communications
Dan Nguyen, Qihui Lyu, Dan Ruan, Daniel O'Connor, Daniel A Low, Ke Sheng
PURPOSE: Volumetric modulated arc therapy (VMAT) is a widely employed radiation therapy technique, showing comparable dosimetry to static beam intensity modulated radiation therapy (IMRT) with reduced monitor units and treatment time. However, the current VMAT optimization has various greedy heuristics employed for an empirical solution, which jeopardizes plan consistency and quality. The authors introduce a novel direct aperture optimization method for VMAT to overcome these limitations...
July 2016: Medical Physics
Andriani Asproudi, Maurizio Petrozziello, Silvia Cavalletto, Silvia Guidoni
The influence exerted by bunch microclimate on some C13-norisoprenoid precursors content was investigated for the first time in Nebbiolo grapes during ripening. Samples were collected, during two consecutive seasons, from two vineyards, which are characterized by different microclimatic conditions caused by vine vigour heterogeneity and different vineyard aspects. Enzymatic hydrolysis of the glycosides extracted from the grapes, and subsequent GC-MS determination of the aglycones, highlighted that the majority of norisoprenoid glycosides accumulated in Nebbiolo berries from pre-veraison until 3-4weeks post-veraison...
November 15, 2016: Food Chemistry
Sara Viotti
BACKGROUND: Correctional officers (COs) are exposed to various factors likely to jeopardize their health and safety. Even if numerous studies have been focused on work-related stress among COs, few studies have been carried out in Italy. OBJECTIVE: Indentify the work-related factors and comprehend how they negatively affect the COs' psychological health in the Italian penal system. METHODS: A qualitative approach was employed. Twenty-eight COs employed in a detention block of an Italian jail were interviewed face-to-face...
January 25, 2016: Work: a Journal of Prevention, Assessment, and Rehabilitation
Koen Michielsen, Johan Nuyts
PURPOSE: The authors wish to evaluate the possible advantages of using a multigrid approach to maximum-a-posteriori reconstruction in digital breast tomosynthesis together with block-iterative updates in the form of either plane-by-plane updates or ordered subsets. METHODS: The authors previously developed a penalized maximum likelihood reconstruction algorithm with resolution model dedicated to breast tomosynthesis [K. Michielsen et al., "Patchwork reconstruction with resolution modeling for digital breast tomosynthesis," Med...
November 2015: Medical Physics
Amala Rahmah, James Blogg, Nurlan Silitonga, Muqowimul Aman, Robert Michael Power
PURPOSE: Indonesian law provides prisoners with basic rights, including access to education, health care and nutrition. Yet, structural and institutional limitations, notably overcrowding and under-resourcing, prohibits penal institutions from fulfilling these commitments for female prisoners. The purpose of this paper is to explore their health concerns. DESIGN/METHODOLOGY/APPROACH: Six prisons and one detention centre were researched, comprising: female prisoners (n=69); clinical officers (six); clinic heads (seven); wardens (seven); heads of prisons (seven); and a Directorate representative...
2014: International Journal of Prisoner Health
Yunzhang Zhu, Xiaotong Shen, Wei Pan
Gaussian graphical models are useful to analyze and visualize conditional dependence relationships between interacting units. Motivated from network analysis under di erent experimental conditions, such as gene networks for disparate cancer subtypes, we model structural changes over multiple networks with possible heterogeneities. In particular, we estimate multiple precision matrices describing dependencies among interacting units through maximum penalized likelihood. Of particular interest are homogeneous groups of similar entries across and zero-entries of these matrices, referred to as clustering and sparseness structures, respectively...
October 2014: Journal of the American Statistical Association
Yu Han, Huiqian Du, Wenbo Mei, Liping Fang
This work aims to develop a novel magnetic resonance (MR) image reconstruction approach motivated by the recently proposed sampling framework with union-of-subspaces model (SUoS). Based on SUoS, we propose a mathematical formalism that effectively integrates a block sparsity constraint and support information which is estimated in an iterative fashion. The resulting optimization problem consists of a data fidelity term and a support detection based block sparsity (SDBS) promoting term penalizing entries within the complement of the estimated support...
June 2015: Magnetic Resonance Imaging
SeyyedMajid Valiollahzadeh, John W Clark, Osama Mawlawi
PURPOSE: Most positron emission tomography/computed tomography (PET/CT) scanners consist of tightly packed discrete detector rings to improve scanner efficiency. The authors' aim was to use compressive sensing (CS) techniques in PET imaging to investigate the possibility of decreasing the number of detector elements per ring (introducing gaps) while maintaining image quality. METHODS: A CS model based on a combination of gradient magnitude and wavelet domains (wavelet-TV) was developed to recover missing observations in PET data acquisition...
January 2015: Medical Physics
Rahul Mazumder, Trevor Hastie
The graphical lasso [5] is an algorithm for learning the structure in an undirected Gaussian graphical model, using ℓ1 regularization to control the number of zeros in the precision matrix Θ = Σ (-1) [2, 11]. The R package GLASSO [5] is popular, fast, and allows one to efficiently build a path of models for different values of the tuning parameter. Convergence of GLASSO can be tricky; the converged precision matrix might not be the inverse of the estimated covariance, and occasionally it fails to converge with warm starts...
November 9, 2012: Electronic Journal of Statistics
B Clarke, J-H Chu
Consider a regression problem in which there are many more explanatory variables than data points, i.e., p ≫ n. Essentially, without reducing the number of variables inference is impossible. So, we group the p explanatory variables into blocks by clustering, evaluate statistics on the blocks and then regress the response on these statistics under a penalized error criterion to obtain estimates of the regression coefficients. We examine the performance of this approach for a variety of choices of n, p, classes of statistics, clustering algorithms, penalty terms, and data types...
2014: Journal of the Indian Society of Agricultural Statistics
Jenna Dixon, Isaac N Luginaah, Paul Mkandawire
This article addresses the implications of the mandatory delay in coverage for individuals residing in the Upper West Region (UWR) of Ghana who have dropped out of the National Health Insurance Scheme (NHIS) but later attempt to reenroll. Using data collected in 2011 in Ghana's UWR, we use a negative log-log model (n=1,584) to compare those who remain enrolled in the scheme with those who have dropped out. Women with unreliable incomes, who reported being food-insecure and those living with young children were more likely to drop out (OR range: 1...
August 2014: Journal of Health Care for the Poor and Underserved
Nicholas N Nagle, Barbara P Buttenfield, Stefan Leyk, Seth Speilman
Dasymetric models increase the spatial resolution of population data by incorporating related ancillary data layers. The role of uncertainty in dasymetric modeling has not been fully addressed as of yet. Uncertainty is usually present because most population data are themselves uncertain, and/or the geographic processes that connect population and the ancillary data layers are not precisely known. A new dasymetric methodology - the Penalized Maximum Entropy Dasymetric Model (P-MEDM) - is presented that enables these sources of uncertainty to be represented and modeled...
January 1, 2014: Annals of the Association of American Geographers
Mladen Kolar, Eric P Xing
We study the problem of estimating a temporally varying coefficient and varying structure (VCVS) graphical model underlying data collected over a period of time, such as social states of interacting individuals or microarray expression profiles of gene networks, as opposed to i.i.d. data from an invariant model widely considered in current literature of structural estimation. In particular, we consider the scenario in which the model evolves in a piece-wise constant fashion. We propose a procedure that estimates the structure of a graphical model by minimizing the temporally smoothed L1 penalized regression, which allows jointly estimating the partition boundaries of the VCVS model and the coefficient of the sparse precision matrix on each block of the partition...
2012: Electronic Journal of Statistics
Ming Li, Mario A Cleves, Himel Mallick, Stephen W Erickson, Xinyu Tang, Todd G Nick, Stewart L Macleod, Charlotte A Hobbs
The development of congenital heart defects (CHDs) involves a complex interplay between genetic variants, epigenetic variants, and environmental exposures. Previous studies have suggested that susceptibility to CHDs is associated with maternal genotypes, fetal genotypes, and maternal-fetal genotype (MFG) interactions. We conducted a haplotype-based genetic association study of obstructive heart defects (OHDs), aiming to detect the genetic effects of 877 SNPs involved in the homocysteine, folate, and transsulfuration pathways...
September 2014: Human Genetics
H A Prentice, N M Pajewski, D He, K Zhang, E E Brown, W Kilembe, S Allen, E Hunter, R A Kaslow, J Tang
Multiple major histocompatibility complex (MHC) loci encoding human leukocyte antigens (HLA) have allelic variants unequivocally associated with differential immune control of HIV-1 infection. Fine mapping based on single nucleotide polymorphisms (SNPs) in the extended MHC (xMHC) region is expected to reveal causal or novel factors and to justify a search for functional mechanisms. We have tested the utility of a custom fine-mapping platform (the ImmunoChip) for 172 HIV-1 seroconverters (SCs) and 449 seroprevalent individuals (SPs) from Lusaka, Zambia, with a focus on more than 6400 informative xMHC SNPs...
July 2014: Genes and Immunity
Nathan W Churchill, Grigori Yourganov, Stephen C Strother
In recent years, a variety of multivariate classifier models have been applied to fMRI, with different modeling assumptions. When classifying high-dimensional fMRI data, we must also regularize to improve model stability, and the interactions between classifier and regularization techniques are still being investigated. Classifiers are usually compared on large, multisubject fMRI datasets. However, it is unclear how classifier/regularizer models perform for within-subject analyses, as a function of signal strength and sample size...
September 2014: Human Brain Mapping
Theodore P Gerber, Jonas Radl
Russia provides an interesting context for studying the labor market experiences of the elderly because of its experience with market transition, its looming growth in the elderly dependency ratio, and its unusual pension policies that do not penalize pensioners for working. We use data from twenty surveys of the Russian population conducted from February 1991 to November 2007 to analyze the labor market participation and earnings of elderly Russians following market transition. Economic desperation, exacerbated by low pension levels, pushed some elderly to seek employment for income on the labor market...
May 2014: Social Science Research
Mengjun Wang, Anand Mehta, Timothy M Block, Jorge Marrero, Adrian M Di Bisceglie, Karthik Devarajan
BACKGROUND: Currently, a surgical approach is the best curative treatment for those with hepatocellular carcinoma (HCC). However, this requires HCC detection and removal of the lesion at an early stage. Unfortunately, most cases of HCC are detected at an advanced stage because of the lack of accurate biomarkers that can be used in the surveillance of those at risk. It is believed that biomarkers that could detect HCC early will play an important role in the successful treatment of HCC...
2013: BMC Medical Genomics
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