Towards a user-friendly webservice architecture for statistical machine translation in the PANACEA project

This paper presents a webservice architecture for Statistical Machine Translation aimed at non-technical users. A workfloweditor allows a user to combine different webservices using a graphical user interface. In the current state of this project,the webservices have been implemented for a range of sentential and sub-sententialaligners. The advantage of a common interface and a common data format allows the user to build workflows exchanging different aligners.

A-1 Monthly Public Assistance Statistical Report Family Investment Program, September 2005

Monthly Public Assistance Statistical Report Family Investment Program

Swain: Stata module to correct the SEM chi-square overidentification test in small sample sizes or complex models. Statistical Software Components S457617, Boston College Department of Economics.

Swain corrects the chi-square overidentification test (i.e., likelihood ratio test of fit) for structural equation models whethr with or without latent variables. The chi-square statistic is asymptotically correct; however, it does not behave as expected in small samples and/or when the model is complex (cf. Herzog, Boomsma, & Reinecke, 2007). Thus, particularly in situations where the ratio of sample size (n) to the number of parameters estimated (p) is relatively small (i.e., the p to n ratio is large), the chi-square test will tend to overreject correctly specified models. To obtain a closer approximation to the distribution of the chi-square statistic, Swain (1975) developed a correction; this scaling factor, which converges to 1 asymptotically, is multiplied with the chi-square statistic. The correction better approximates the chi-square distribution resulting in more appropriate Type 1 reject error rates (see Herzog & Boomsma, 2009; Herzog, et al., 2007).

A-1 Monthly Public Assistance Statistical Report Family Investment Program, October 2005

A-1 Monthly Public Assistance Statistical Report Family Investment Program, October 2005

A-1 Monthly Public Assistance Statistical Report Family Investment Program, November 2005

A-1 - Monthly Public Assistance Statistical Report Family Investment Program - November 2005

A-1 Monthly Public Assistance Statistical Report-Family Investment Program, December 2005

A-1 December 2005 - Monthly Public Assistance Statistical Report - Family Investment Program

A-1 Monthly Public Assistance Statistical Report Family Investment Program, January 2006

Monthly Public Assistance Statistical Report Family Investment Program, January 2006

A-1 Monthly Public Assistance Statistical Report-Family Investment Program, February 2006

A-1 Monthly Public Assistance Statistical Report Family Investment Program, February 2006

A-1 Monthly Public Assistance Statistical Report Family Investment Program, March 2006

A-1 - March 2006 -Monthly Public Assistance Statistical Report Family Investment Program

A-1 Monthly Public Assistance Statistical Report Family Investment Program, April 2006

Monthly Public Assistance Statistical Report Family Investment Program, April 2006

Adaptive strategy for the statistical analysis of connectomes.

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.

A-1, Monthly Public Assistance Statistical Report Family Investment Program, May 2006

A-1 May 2006 - Monthly Public Assistance Statistical Report Family Investment Program

A-1, Monthly Public Assistance Statistical Report - Family Investment Program, June 2006

A-1 Monthly Public Assistance Statistical Report - Family Investment Program - June 2006

A-1 Monthly Public Assistance Statistical Report Family Investment Program, July 2006

A-1 July 2006 - Monthly Public Assistance Statistical Report Family Investment Program

A-1 Monthly Public Assistance Statistical Report Family Investment Program, August 2006

A-1 Monthly Public Assistance Statistical Report Family Investment Program August 2006