Implicit Taylor methods for stiff stochastic differential equations

In this paper we discuss implicit Taylor methods for stiff Ito stochastic differential equations. Based on the relationship between Ito stochastic integrals and backward stochastic integrals, we introduce three implicit Taylor methods: the implicit Euler-Taylor method with strong order 0.5, the implicit Milstein-Taylor method with strong order 1.0 and the implicit Taylor method with strong order 1.5. The mean-square stability properties of the implicit Euler-Taylor and Milstein-Taylor methods are much better than those of the corresponding semi-implicit Euler and Milstein methods and these two implicit methods can be used to solve stochastic differential equations which are stiff in both the deterministic and the stochastic components. Numerical results are reported to show the convergence properties and the stability properties of these three implicit Taylor methods. The stability analysis and numerical results show that the implicit Euler-Taylor and Milstein-Taylor methods are very promising methods for stiff stochastic differential equations.

Topological methods for some boundary value problems

We establish existence of solutions for a finite difference approximation to y = f(x, y, y ') on [0, 1], subject to nonlinear two-point Sturm-Liouville boundary conditions of the form g(i)(y(i),y ' (i)) = 0, i = 0, 1, assuming S satisfies one-sided growth bounds with respect to y '. (C) 2001 Elsevier Science Ltd. All rights reserved.

Stiffly accurate Runge-Kutta methods for stiff stochastic differential equations

In this paper we discuss implicit methods based on stiffly accurate Runge-Kutta methods and splitting techniques for solving Stratonovich stochastic differential equations (SDEs). Two splitting techniques: the balanced splitting technique and the deterministic splitting technique, are used in this paper. We construct a two-stage implicit Runge-Kutta method with strong order 1.0 which is corrected twice and no update is needed. The stability properties and numerical results show that this approach is suitable for solving stiff SDEs. (C) 2001 Elsevier Science B.V. All rights reserved.

Genetic and environmental contributions to educational attainment in Australia

The genetic and environmental contributions to educational attainment in Australia are examined using a multiple regression model drawn from the medical research literature. Data from a large sample of Australian twins are analysed. The findings indicate that at least as much as 50 percent and perhaps as much as 65 percent of the variance in educational attainments can be attributed to genetic endowments. It is suggested that only around 25 percent of the variance in educational attainments may be due to environmental factors, though this contribution is shown to be around 40 percent when adjustments for measurement error and assortative mating are made. The high fraction of the observed variation in educational attainments due to genetic differences is consistent with results reported by Heath et al. (Heath, A.C., Berg, K., Eaves, L.J., Solaas, M.H., Corey, L.A., Sundet, J., Magnus, P., Nance, W.E., 1985. Education policy and the heritability of educational attainment. Nature 314(6013), 734-736.), Tambs et al. (Tambs, K., Sundet, J.M., Magnus, P., Berg, K., 1989. Genetic and environmental contributions to the covariance between occupational status, educational attainment and IQ: a study of twins. Behavior Genetics 19(2), 209-222.), Vogler and Fulker (Vogler, G.P., Fulker, D.W., 1983. Familial resemblance for educational attainment. Behavior Generics 13(4), 341-354.) and Behrman and Taubman (Behrman, J., Taubman, P., 1989. Is schooling mostly in the genes? Nature-nurture decomposition using data on relatives. Journal of Political Economy 97(6), 1425-1446.), suggesting that the finding is robust. (C) 2001 Elsevier Science Ltd. All rights reserved.

Memory awareness and schematization: Learning in the university context

Following the application of the remember/know paradigm to student learning by Conway et al. (1997), this study examined changes in learning and memory awareness of university students in a lecture course and a research methods course. The proposed shift from a dominance of 'remember' awareness in early learning to a dominance of 'know' awareness as learning progresses and schematization occurs was evident for the methods course but not for the lecture course. The patterns of remember and know awareness and proposed associated levels of schematization were supported by a separate measure of the quality of student learning using the SOLO (Structure of Observed Learning Outcomes) Taxonomy. As found by previous research, the remember-to-know shift and schematization of knowledge is dependent upon type of course and level of achievement. Findings are discussed in terms of the utility of the methodology used, the theoretical implications and the applications to educational practice. Copyright (C) 2001 John Wiley & Sons, Ltd.