Attachment and alcohol : testing a motivational model of problem drinking /

The purpose of this study was to replicate and extend a motivational model of problem drinking (Cooper, Frone, Russel, & Mudar, 1995; Read, Wood, Kahler, Maddock & Tibor, 2003), testing the notion that attachment is a common antecedent for both the affective and social paths to problem drinking. The model was tested with data from three samples, first-year university students (N=679), students about to graduate from university (N=206), and first-time clients at an addiction treatment facility (N=21 1). Participants completed a battery of questionnaires assessing alcohol use, alcohol-related consequences, drinking motives, peer models of alcohol use, positive and negative affect, attachment anxiety and attachment avoidance. Results underscored the importance of the affective path to problem drinking, while putting the social path to problem drinking into question. While drinking to cope was most prominent among the clinical sample, coping motives served as a risk factor for problem drinking for both individuals identified as problem drinkers and university students. Moreover, drinking for enhancement purposes appeared to be the strongest overall predictor of alcohol use. Results of the present study also supported the notion that attachment anxiety and avoidance are antecedents for the affective path to problem drinking, such that those with higher levels of attachment anxiety and avoidance were more vulnerable to experiencing adverse consequences related to their drinking, explained in terms of diminished affect regulation. Evidence that nonsecure attachment is a potent predictor of problem drinking was also demonstrated by the finding that attachment anxiety was directly related to alcohol-related consequences over and above its indirect relationship through affect regulation. However, results failed to show that attachment anxiety or attachment avoidance increased the risk of problem drinking via social influence.

Transmission of West Nile virus in the Niagara Region among a population at risk for exposure

Background. West Nile Virus (WNV), a mosquito-borne flavivirus, is one of an increasing number of infectious diseases that have been emerging or re-emerging in the last two decades. Since the arrival ofWNV to Canada to present date, the Niagara Region has only reported 30 clinical cases, a small number compared to the hundreds reported in other regions of similar conditions. Moreover, the last reported human case in Niagara was in 2006. As it has been demonstrated that the majority of WNV infections are asymptomatic, the question remains whether the lack of clinical cases in Niagara truly reflects the lack of transmission to humans or if infections are still occurring but are mostly asymptomatic. Objectives. The general objective of this study was to establish whether or not active WNV transmission could be detected in a human population residing in Niagara for the 2007 transmission season. To fullfil this objective, a cross-sectional seroprevalence study was designed to investigate for the presence of anti-WNV antibodies in a sample of Mexican migrant agricultural workers employed in farms registered with the Seasonal Agricultural Workers Program (SAWP). Due to the Mexican origin of the study participants, three specific research objectives were proposed: a) determine the seroprevalence ofanti-WNV antibodies as well as anti-Dengue virus antibodies (a closely related virus prevalent in Mexico and likely to confound WNV serology); b) analyze risk factors associated with WNV and Dengue virus seropositivity; and c) assess the awareness of study participants about WNV infection as well as their understanding of the mode of transmission and clinical importance of the infection. Methodology: After obtaining ethics clearance from Brock University, farms were visited and workers invited to participate. Due to time constraints, only a small number of farms were enrolled with a resulting convenience and non-randomized study sample. Workers' demographic and epidemiological data were collected using a standardized questionnaire and blood samples were drawn to determine serum anti-WNV and anti- Dengue antibodies with a commercial ELISA. All positive samples were sent to the National Microbiology Laboratory in Winnipeg, Manitoba for confirmation with the Plaque Reduction Neutralization Test (PRNT). Data was analyzed with Stata 10.0. Antibody determinations were reported as seroprevalence proportions for both WNV and Dengue. Logistic regression was used to analyze risk factors that may be associated with seropositivity and awareness was reported as a proportion of the number of individuals possessing awareness over the total number of participants. Results and Discussion. In total 92 participants working in 5 farms completed the study. Using the commercial ELISA, seropositivity was as follows: 2.2% for WNV IgM, 20.7% for WNV IgG, and 17.1 % for Dengue IgG. Possible cross-reactivity was demonstrated in 15/20 (75.0%) samples that were positive for both WNV IgG and Dengue IgG. Confirmatory testing with the PRNT demonstrated that none of the WNV ELISA positive samples had antibodies to WNV but 13 samples tested positive for anti-Dengue antibodies (14.1 % Dengue sereoprevalence). The findings showed that the ELISA performance was very poor for assessing anti-WNV antibodies in individuals previously exposed to Dengue virus. However, the ELISA had better sensitivity and specificity for assessing anti-Dengue antibodies. Whereas statistical analysis could not be done for WNV seropositivity, as all samples were PRNT negative, logistic regression demonstrated several risk factors for Dengue exposure_ The first year coming to Canada appeared to be significantly associated with increased exposure to Dengue while lower socio-economic housing and the presence of a water basin in the yard in Mexico appeared to be significantly associated with a decreased exposure to Dengue_ These seemingly contradictory results illustrate that in mobile populations such as migrant workers, risk factors for exposure to Dengue are not easily identified and more research is needed. Assessing the awareness of WNV and its clinical importance showed that only 23% of participants had some knowledge of WNV, of which 76% knew that the infection was mosquito-borne and 47% recognized fever as a symptom. The identified lack of understanding and awareness was not surprising since WNV is not a visible disease in Mexico. Since WNV persists in an enzootic cycle in Niagara and the occurrence of future outbreaks is unpredictable, the agricultural workers remain at risk for transmission. Therefore it important they receive sufficient health education regarding WNV before leaving Mexico and during their stay in Canada. Conclusions. Human transmission of WNV could not be proven among the study participants even when due to their occupation they are at high risk for mosquito bites. The limitations of the study sample do not permit generalizable conclusions, however, the study findings are consistent with the absence of clinical cases in the Niagara Region, so it is likely that human transmission is indeed neglible or absent. As evidenced by our WNV serology results, PRNT must be utilized as a confirmatory test since false positivity occurs frequently. This is especially true when previous exposure to Dengue virus is likely.

The Effect of Trainer Muscularity and Expertise on Self-Presentational Concerns, Body Image, and Performance in College Men during One-Repetition Maximum Testing

This study attempted to manipulate self-presentational efficacy to examine the effect on social anxiety, social physique anxiety, drive for muscularity, and maximal strength performance during a one-repetition maximum (1-RM) chest press and leg press test. Ninety-nine college men with a minimum of six months of previous weight training experience were randomly assigned to complete a 1-RM protocol with either a muscular male trainer described as an expert or a lean male trainer described as a novice. Participants completed measures of self-presentation and body image prior to meeting their respective trainer, and following the completion of the 1-RM tests. Although the self-presentational efficacy manipulation was not successful, the trainers were perceived significantly differently on musculature and expertise. The group with the muscular, expert trainer reported higher social anxiety and attained higher 1-RM scores for the chest and leg press. Thus, trainer characteristics can affect strength performance and self-presentational concerns in this population.

Optimal and Robust Designs of Step-stress Accelerated Life Testing Experiments for Proportional Hazards Models

Accelerated life testing (ALT) is widely used to obtain reliability information about a product within a limited time frame. The Cox s proportional hazards (PH) model is often utilized for reliability prediction. My master thesis research focuses on designing accelerated life testing experiments for reliability estimation. We consider multiple step-stress ALT plans with censoring. The optimal stress levels and times of changing the stress levels are investigated. We discuss the optimal designs under three optimality criteria. They are D-, A- and Q-optimal designs. We note that the classical designs are optimal only if the model assumed is correct. Due to the nature of prediction made from ALT experimental data, attained under the stress levels higher than the normal condition, extrapolation is encountered. In such case, the assumed model cannot be tested. Therefore, for possible imprecision in the assumed PH model, the method of construction for robust designs is also explored.

Stress testing a complement of value-a-risk.

Rapport de stage (maîtrise en finance mathématique et computationnelle)

The Seemingly Unrelated Dynamic Cointegration Regression Model and Testing for Purching Power Parity.

This paper studies seemingly unrelated linear models with integrated regressors and stationary errors. By adding leads and lags of the first differences of the regressors and estimating this augmented dynamic regression model by feasible generalized least squares using the long-run covariance matrix, we obtain an efficient estimator of the cointegrating vector that has a limiting mixed normal distribution. Simulation results suggest that this new estimator compares favorably with others already proposed in the literature. We apply these new estimators to the testing of purchasing power parity (PPP) among the G-7 countries. The test based on the efficient estimates rejects the PPP hypothesis for most countries.

Testing Normality : A GMM Approach

In this paper, we consider testing marginal normal distributional assumptions. More precisely, we propose tests based on moment conditions implied by normality. These moment conditions are known as the Stein (1972) equations. They coincide with the first class of moment conditions derived by Hansen and Scheinkman (1995) when the random variable of interest is a scalar diffusion. Among other examples, Stein equation implies that the mean of Hermite polynomials is zero. The GMM approach we adopted is well suited for two reasons. It allows us to study in detail the parameter uncertainty problem, i.e., when the tests depend on unknown parameters that have to be estimated. In particular, we characterize the moment conditions that are robust against parameter uncertainty and show that Hermite polynomials are special examples. This is the main contribution of the paper. The second reason for using GMM is that our tests are also valid for time series. In this case, we adopt a Heteroskedastic-Autocorrelation-Consistent approach to estimate the weighting matrix when the dependence of the data is unspecified. We also make a theoretical comparison of our tests with Jarque and Bera (1980) and OPG regression tests of Davidson and MacKinnon (1993). Finite sample properties of our tests are derived through a comprehensive Monte Carlo study. Finally, three applications to GARCH and realized volatility models are presented.

Testing Mean-Variance Efficiency in CAPM with Possibly Non-Gaussian Errors : An Exact Simulation-Based Approach

In this paper we propose exact likelihood-based mean-variance efficiency tests of the market portfolio in the context of Capital Asset Pricing Model (CAPM), allowing for a wide class of error distributions which include normality as a special case. These tests are developed in the frame-work of multivariate linear regressions (MLR). It is well known however that despite their simple statistical structure, standard asymptotically justified MLR-based tests are unreliable. In financial econometrics, exact tests have been proposed for a few specific hypotheses [Jobson and Korkie (Journal of Financial Economics, 1982), MacKinlay (Journal of Financial Economics, 1987), Gib-bons, Ross and Shanken (Econometrica, 1989), Zhou (Journal of Finance 1993)], most of which depend on normality. For the gaussian model, our tests correspond to Gibbons, Ross and Shanken’s mean-variance efficiency tests. In non-gaussian contexts, we reconsider mean-variance efficiency tests allowing for multivariate Student-t and gaussian mixture errors. Our framework allows to cast more evidence on whether the normality assumption is too restrictive when testing the CAPM. We also propose exact multivariate diagnostic checks (including tests for multivariate GARCH and mul-tivariate generalization of the well known variance ratio tests) and goodness of fit tests as well as a set estimate for the intervening nuisance parameters. Our results [over five-year subperiods] show the following: (i) multivariate normality is rejected in most subperiods, (ii) residual checks reveal no significant departures from the multivariate i.i.d. assumption, and (iii) mean-variance efficiency tests of the market portfolio is not rejected as frequently once it is allowed for the possibility of non-normal errors.

Testing for a Unit Root in Panels with Dynamic Factors

This paper studies testing for a unit root for large n and T panels in which the cross-sectional units are correlated. To model this cross-sectional correlation, we assume that the data is generated by an unknown number of unobservable common factors. We propose unit root tests in this environment and derive their (Gaussian) asymptotic distribution under the null hypothesis of a unit root and local alternatives. We show that these tests have significant asymptotic power when the model has no incidental trends. However, when there are incidental trends in the model and it is necessary to remove heterogeneous deterministic components, we show that these tests have no power against the same local alternatives. Through Monte Carlo simulations, we provide evidence on the finite sample properties of these new tests.

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.