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.

Estimating the basic reproductive number from viral sequence data.

Epidemiological processes leave a fingerprint in the pattern of genetic structure of virus populations. Here, we provide a new method to infer epidemiological parameters directly from viral sequence data. The method is based on phylogenetic analysis using a birth-death model (BDM) rather than the commonly used coalescent as the model for the epidemiological transmission of the pathogen. Using the BDM has the advantage that transmission and death rates are estimated independently and therefore enables for the first time the estimation of the basic reproductive number of the pathogen using only sequence data, without further assumptions like the average duration of infection. We apply the method to genetic data of the HIV-1 epidemic in Switzerland.

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

Adaptation and the genetics of social behaviour.

In recent years much progress has been made towards understanding the selective forces involved in the evolution of social behaviour including conflicts over reproduction among group members. Here, I argue that an important additional step necessary for advancing our understanding of the resolution of potential conflicts within insect societies is to consider the genetics of the behaviours involved. First, I discuss how epigenetic modifications of behaviour may affect conflict resolution within groups. Second, I review known natural polymorphisms of social organization to demonstrate that a lack of consideration of the genetic mechanisms involved may lead to erroneous explanations of the adaptive significance of behaviour. Third, I suggest that, on the basis of recent genetic studies of sexual conflict in Drosophila, it is necessary to reconsider the possibility of within-group manipulation by means of chemical substances (i.e. pheromones). Fourth, I address the issue of direct versus indirect genetic effects, which is of particular importance for the study of behaviour in social groups. Fifth, I discuss the issue of how a genetic influence on dominance hierarchies and reproductive division of labour can have secondary effects, for example in the evolution of promiscuity. Finally, because the same sets of genes (e.g. those implicated in chemical signalling and the responses that are triggered) may be used even in species as divergent as ants, cooperative breeding birds and primates, an integration of genetic mechanisms into the field of social evolution may also provide unifying ideas.