985 resultados para Matrix-Variate Statistical Distributions
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OBJECTIVE: To better understand the structure of the Patient Assessment of Chronic Illness Care (PACIC) instrument. More specifically to test all published validation models, using one single data set and appropriate statistical tools. DESIGN: Validation study using data from cross-sectional survey. PARTICIPANTS: A population-based sample of non-institutionalized adults with diabetes residing in Switzerland (canton of Vaud). MAIN OUTCOME MEASURE: French version of the 20-items PACIC instrument (5-point response scale). We conducted validation analyses using confirmatory factor analysis (CFA). The original five-dimension model and other published models were tested with three types of CFA: based on (i) a Pearson estimator of variance-covariance matrix, (ii) a polychoric correlation matrix and (iii) a likelihood estimation with a multinomial distribution for the manifest variables. All models were assessed using loadings and goodness-of-fit measures. RESULTS: The analytical sample included 406 patients. Mean age was 64.4 years and 59% were men. Median of item responses varied between 1 and 4 (range 1-5), and range of missing values was between 5.7 and 12.3%. Strong floor and ceiling effects were present. Even though loadings of the tested models were relatively high, the only model showing acceptable fit was the 11-item single-dimension model. PACIC was associated with the expected variables of the field. CONCLUSIONS: Our results showed that the model considering 11 items in a single dimension exhibited the best fit for our data. A single score, in complement to the consideration of single-item results, might be used instead of the five dimensions usually described.
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A-1 Monthly Public Assistance Statistical Report Family Investment Program, February 2006
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A-1 - March 2006 -Monthly Public Assistance Statistical Report Family Investment Program
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Monthly Public Assistance Statistical Report Family Investment Program, April 2006
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We study an adaptive statistical approach to analyze brain networks represented by brain connection matrices of interregional connectivity (connectomes). Our approach is at a middle level between a global analysis and single connections analysis by considering subnetworks of the global brain network. These subnetworks represent either the inter-connectivity between two brain anatomical regions or by the intra-connectivity within the same brain anatomical region. An appropriate summary statistic, that characterizes a meaningful feature of the subnetwork, is evaluated. Based on this summary statistic, a statistical test is performed to derive the corresponding p-value. The reformulation of the problem in this way reduces the number of statistical tests in an orderly fashion based on our understanding of the problem. Considering the global testing problem, the p-values are corrected to control the rate of false discoveries. Finally, the procedure is followed by a local investigation within the significant subnetworks. We contrast this strategy with the one based on the individual measures in terms of power. We show that this strategy has a great potential, in particular in cases where the subnetworks are well defined and the summary statistics are properly chosen. As an application example, we compare structural brain connection matrices of two groups of subjects with a 22q11.2 deletion syndrome, distinguished by their IQ scores.
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A-1 May 2006 - Monthly Public Assistance Statistical Report Family Investment Program
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A-1 Monthly Public Assistance Statistical Report - Family Investment Program - June 2006
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Diagnosis Related Groups (DRG) are frequently used to standardize the comparison of consumption variables, such as length of stay (LOS). In order to be reliable, this comparison must control for the presence of outliers, i.e. values far removed from the pattern set by the majority of the data. Indeed, outliers can distort the usual statistical summaries, such as means and variances. A common practice is to trim LOS values according to various empirical rules, but there is little theoretical support for choosing between alternative procedures. This pilot study explores the possibility of describing LOS distributions with parametric models which provide the necessary framework for the use of robust methods.
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Using both conventional fluorescence and confocal laser scanning microscopy we have investigated whether or not stabilization of isolated human erythroleukemic nuclei with sodium tetrathionate can maintain in the nuclear matrix the same spatial distribution of three polypeptides (M(r) 160 kDa and 125 kDa, previously shown to be components of the internal nuclear matrix plus the 180-kDa nucleolar isoform of DNA topoisomerase II) as seen in permeabilized cells. The incubation of isolated nuclei in the presence of 2 mM sodium tetrathionate was performed at 0 degrees C or 37 degrees C. The matrix fraction retained 20-40% of nuclear protein, depending on the temperature at which the chemical stabilization was executed. Western blot analysis revealed that the proteins studied were completely retained in the high-salt resistant matrix. Indirect immunofluorescence experiments showed that the distribution of the three antigens in the final matrix closely resembled that detected in permeabilized cells, particularly when the stabilization was performed at 37 degrees C. This conclusion was also strengthened by analysis of cells, isolated nuclei and the nuclear matrix by means of confocal laser scanning microscopy. We conclude that sodium tetrathionate stabilization of isolated nuclei does not alter the spatial distribution of some nuclear matrix proteins.
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A-1 July 2006 - Monthly Public Assistance Statistical Report Family Investment Program
Higher-order expansions for compound distributions and ruin probabilities with subexponential claims
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A-1 Monthly Public Assistance Statistical Report Family Investment Program August 2006
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Monthly Public Assistance Statistical Report: Family Investment Program produced by the Iowa Department of Human Services
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Monthly Public Assistance Statistical Report Family Investment Program
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Monthly Public Assistance Statistical Report Family Investment Program