How to build a bioinformatics service in a year

Introduction: Biomedical scientists need to choose among hundreds of publicly available bioinformatics applications, tools, and databases. Librarian challenges include raising awareness to valuable resources, as well as providing support in finding and evaluating specific resources. Our objective is to implement an education program in bioinformatics similar to those offered in other North American academic libraries. Description: Our initial target clientele included four research departments of the Faculty of Medicine at Universite´ de Montréal. In January 2010, I attended two departmental meetings and interviewed a few stakeholders in order to propose a basic bioinformatics service: one-to-one consultations and a workshop on NCBI databases. The response was favourable. The workshop was thus offered once a month during the Winter and Fall semesters, and participants were invited to evaluate the workshop via an online survey. In addition, a bioinformatics subject guide was launched on the library’s website in December 2010. Outcomes: One hundred and two participants attended one of the nine NCBI workshops offered in 2010; most were graduate students (74%). The survey’s response rate was 54%. A majority of respondents thought that the bioinformatics resources featured in the workshop were relevant (95%) and that the difficulty level of exercises was appropriate (84%). Respondents also thought that their future information searches would be more efficient (93%) and that the workshop should be integrated in a course (78%). Furthermore, five bioinformatics-related reference questions were answered and two one-to-one consultations with students were performed. Discussion: The success of our bioinformatics service is growing. Future directions include extending the service to other biomedical departments, integrating the workshop in an undergraduate course, promoting the subject guide to other francophone universities, and creating a bioinformatics blog that would feature specific databases, news, and library resources.

AnaBench: a Web/CORBA-based workbench for biomolecular sequence analysis

Affiliation: Département de biochimie, Faculté de médecine, Université de Montréal

Le modèle statistique d'équilibre du comportement dans les jeux expérimentaux: applications aux contributions volontaires aux biens publics.

Rapport de recherche

Some Additional Specification Tests for Generalized Method of Moments Estimators with Macro-Economic Applications Part I : Theory

In This Paper Several Additional Gmm Specification Tests Are Studied. a First Test Is a Chow-Type Test for Structural Parameter Stability of Gmm Estimates. the Test Is Inspired by the Fact That \"Taste and Technology\" Parameters Are Uncovered. the Second Set of Specification Tests Are Var Encompassing Tests. It Is Assumed That the Dgp Has a Finite Var Representation. the Moment Restrictions Which Are Suggested by Economic Theory and Exploited in the Gmm Procedure Represent One Possible Characterization of the Dgp. the Var Is a Different But Compatible Characterization of the Same Dgp. the Idea of the Var Encompassing Tests Is to Compare Parameter Estimates of the Euler Conditions and Var Representations of the Dgp Obtained Separately with Parameter Estimates of the Euler Conditions and Var Representations Obtained Jointly. There Are Several Ways to Construct Joint Systems Which Are Discussed in the Paper. Several Applications Are Also Discussed.

Finite-Sample Diagnostics for Multivariate Regressions with Applications to Linear Asset Pricing Models

In this paper, we propose several finite-sample specification tests for multivariate linear regressions (MLR) with applications to asset pricing models. We focus on departures from the assumption of i.i.d. errors assumption, at univariate and multivariate levels, with Gaussian and non-Gaussian (including Student t) errors. The univariate tests studied extend existing exact procedures by allowing for unspecified parameters in the error distributions (e.g., the degrees of freedom in the case of the Student t distribution). The multivariate tests are based on properly standardized multivariate residuals to ensure invariance to MLR coefficients and error covariances. We consider tests for serial correlation, tests for multivariate GARCH and sign-type tests against general dependencies and asymmetries. The procedures proposed provide exact versions of those applied in Shanken (1990) which consist in combining univariate specification tests. Specifically, we combine tests across equations using the MC test procedure to avoid Bonferroni-type bounds. Since non-Gaussian based tests are not pivotal, we apply the “maximized MC” (MMC) test method [Dufour (2002)], where the MC p-value for the tested hypothesis (which depends on nuisance parameters) is maximized (with respect to these nuisance parameters) to control the test’s significance level. The tests proposed are applied to an asset pricing model with observable risk-free rates, using monthly returns on New York Stock Exchange (NYSE) portfolios over five-year subperiods from 1926-1995. Our empirical results reveal the following. Whereas univariate exact tests indicate significant serial correlation, asymmetries and GARCH in some equations, such effects are much less prevalent once error cross-equation covariances are accounted for. In addition, significant departures from the i.i.d. hypothesis are less evident once we allow for non-Gaussian errors.

Finite-Sample Simulation-Based Inference in VAR Models with Applications to Order Selection and Causality Testing

Statistical tests in vector autoregressive (VAR) models are typically based on large-sample approximations, involving the use of asymptotic distributions or bootstrap techniques. After documenting that such methods can be very misleading even with fairly large samples, especially when the number of lags or the number of equations is not small, we propose a general simulation-based technique that allows one to control completely the level of tests in parametric VAR models. In particular, we show that maximized Monte Carlo tests [Dufour (2002)] can provide provably exact tests for such models, whether they are stationary or integrated. Applications to order selection and causality testing are considered as special cases. The technique developed is applied to quarterly and monthly VAR models of the U.S. economy, comprising income, money, interest rates and prices, over the period 1965-1996.

Etude de deux applications d'evaluation contingente mesurant les benefices lies au potentiel de recreation ou la preservation des forets publiques.

Rapport de recherche