Comparison as a Universal Learning Action

This article explores ‘comparison’ as a universal metasubject learning action, a key curricular element envisaged by the Russian Federal State Educational Standards. Representing the modern learner’s fundamental pragmatic skill embedding such core capacities as information processing, critical thinking, robust decision-making, and effective problem solving, the universal learning action of ‘comparison’ has become a new professional challenge for educators, in terms of both understanding the underlying theoretical framework and ensuring that appropriate instructional models are deployed. The author seeks to clarify either of these aspects by providing a plain discussion of how the basic mechanisms of comparison as a cognitive device can be incorporated into clear and simple classroom aids and algorithms supporting goal-directed learning.     

Transient detailed balance and product form for reaction networks

This paper explores the boundary of the set of reaction networks that have an exact transient (truncated) multidimensional Poisson or product-form distribution for the number of particles of different types. Motivated by the birth–death process, we introduce the notions of transient detailed balance and delay functions, and use these notions to obtain the novel transient product-form distribution in a coagulation-fragmentation process for polymers with a tree-like structure from that of the pure coagulation process.     

Multilevel maximum likelihood estimation with application to covariance matrices

The asymptotic variance of the maximum likelihood estimate is proved to decrease when the maximization is restricted to a subspace that contains the true parameter value. Maximum likelihood estimation allows a systematic fitting of covariance models to the sample, which is important in data assimilation. The hierarchical maximum likelihood approach is applied to the spectral diagonal covariance model with different parameterizations of eigenvalue decay, and to the sparse inverse covariance model with specified parameter values on different sets of nonzero entries. It is shown computationally that using smaller sets of parameters can decrease the sampling noise in high dimension substantially.     

Ambulance allocation for maximal survival with heterogeneous outcome measures

This paper proposes new models for locating emergency medical services (EMS) by incorporating survival functions for capturing multiple-classes of heterogeneous patients. The Maximal Expected Survival Location Model for Heterogeneous Patients (MESLMHP) aims to maximize the overall expected survival probability of multiple-classes of patients, whereby different classes could be defined according to agreed patient categories based on response time targets, or by capturing differing medical conditions each with a corresponding survival function. Furthermore, we propose and demonstrate an approximation approach to solving the extended stochastic version of MESLMHP, which utilizes queuing theory to permit the modeling of congestion and utilization at each ambulance station, and does not require assumptions to be made on the utilization of ambulances. Both models are demonstrated using data from the ambulance service in Wales. We show that our multiple outcome measures and survival-maximizing approach, rather than one based on average response time targets alone or a single patient class provides more effective EMS ambulance allocations.     

Trend smoothness achieved by penalized least squares with the smoothing parameter chosen by optimality criteria

This work presents a study about the smoothness attained by the methods more frequently used to choose the smoothing parameter in the context of splines: Cross Validation, Generalized Cross Validation, and corrected Akaike and Bayesian Information Criteria, implemented with Penalized Least Squares. It is concluded that the amount of smoothness strongly depends on the length of the series and on the type of underlying trend, while the presence of seasonality even though statistically significant is less relevant. The intrinsic variability of the series is not statistically significant and its effect is taken into account only through the smoothing parameter.     

Trauma and health risk behaviors in people with severe mental illness

The current study tests the association between a composite measure of unsafe sex and sharing syringes for drug use with six of the more common lifetime traumatic/stressful events in 421 community mental health clients with severe mental illness (SMI) while controlling for psychiatric symptoms and related problems. A small but significant proportion of respondents said they had injected drugs with a shared needle in their lifetime (30, 7.2%), and a much larger proportion of respondents had engaged in unprotected sex (165, 39.2%). Unprotected sex and needle sharing were significantly correlated (Spearman’s rho = .20, p < .01). Frequency of lifetime traumatic events that occurred at least once was reported by one third to three quarters of clients depending on type of trauma. Regression analysis revealed that substance abuse and lifetime homelessness were significantly correlated with health risk behaviors. Practitioners need to be continuously vigilant to comorbid substance use and the housing needs of people with SMI. Limitations of the study include its cross-sectional design.     

The effect of fast and slow decisions on risk taking

We experimentally compare fast and slow decisions in a series of experiments on financial risk taking in three countries involving over 1700 subjects. To manipulate fast and slow decisions, subjects were randomly allocated to responding within 7 seconds (time pressure) or waiting for at least 7 or 20 seconds (time delay) before responding. To control for different effects of time pressure and time delay on measurement noise, we estimate separate parameters for noise and risk preferences within a random utility framework. We find that time pressure increases risk aversion for gains and risk taking for losses compared to time delay, implying that time pressure increases the reflection effect of Prospect Theory. The results for gains are weaker and less robust than the results for losses. We find no significant difference between time pressure and time delay for loss aversion (tested in only one of the experiments). Time delay also leads to less measurement noise than time pressure and unconstrained decisions, and appears to be an effective way of decreasing noise in experiments.     

Semi-polynomial copulas

In this paper, we characterise a family of bivariate copulas whose sections between the main diagonal and the border of the unit square are polynomial, generalising several families of copulas, including those with quadratic and cubic sections. We also study a measure of association and the tail dependence for this class, illustrating our results with several examples.     

Latin hypercube sampling with multidimensional uniformity

Complex models can only be realized a limited number of times due to large computational requirements. Methods exist for generating input parameters for model realizations including Monte Carlo simulation (MCS) and Latin hypercube sampling (LHS). Recent algorithms such as maximinLHS seek to maximize the minimum distance between model inputs in the multivariate space. A novel extension of Latin hypercube sampling (LHSMDU) for multivariate models is developed here that increases the multidimensional uniformity of the input parameters through sequential realization elimination. Correlations are considered in the LHSMDU sampling matrix using a Cholesky decomposition of the correlation matrix. Computer code implementing the proposed algorithm supplements this article. A simulation study comparing MCS, LHS, maximinLHS and LHSMDU demonstrates that increased multidimensional uniformity can significantly improve realization efficiency and that LHSMDU is effective for large multivariate problems.