A Numerical Solution Using an Adaptively Preconditioned Lanczos Method for a Class of Linear Systems Related with the Fractional Poisson Equation

This study considers the solution of a class of linear systems related with the fractional Poisson equation (FPE) (−∇2)α/2φ=g(x,y) with nonhomogeneous boundary conditions on a bounded domain. A numerical approximation to FPE is derived using a matrix representation of the Laplacian to generate a linear system of equations with its matrix A raised to the fractional power α/2. The solution of the linear system then requires the action of the matrix function f(A)=A−α/2 on a vector b. For large, sparse, and symmetric positive definite matrices, the Lanczos approximation generates f(A)b≈β0Vmf(Tm)e1. This method works well when both the analytic grade of A with respect to b and the residual for the linear system are sufficiently small. Memory constraints often require restarting the Lanczos decomposition; however this is not straightforward in the context of matrix function approximation. In this paper, we use the idea of thick-restart and adaptive preconditioning for solving linear systems to improve convergence of the Lanczos approximation. We give an error bound for the new method and illustrate its role in solving FPE. Numerical results are provided to gauge the performance of the proposed method relative to exact analytic solutions.

Poisson, Poisson-gamma and zero-inflated regression models of motor vehicle crashes: balancing statistical fit and theory

There has been considerable research conducted over the last 20 years focused on predicting motor vehicle crashes on transportation facilities. The range of statistical models commonly applied includes binomial, Poisson, Poisson-gamma (or negative binomial), zero-inflated Poisson and negative binomial models (ZIP and ZINB), and multinomial probability models. Given the range of possible modeling approaches and the host of assumptions with each modeling approach, making an intelligent choice for modeling motor vehicle crash data is difficult. There is little discussion in the literature comparing different statistical modeling approaches, identifying which statistical models are most appropriate for modeling crash data, and providing a strong justification from basic crash principles. In the recent literature, it has been suggested that the motor vehicle crash process can successfully be modeled by assuming a dual-state data-generating process, which implies that entities (e.g., intersections, road segments, pedestrian crossings, etc.) exist in one of two states—perfectly safe and unsafe. As a result, the ZIP and ZINB are two models that have been applied to account for the preponderance of “excess” zeros frequently observed in crash count data. The objective of this study is to provide defensible guidance on how to appropriate model crash data. We first examine the motor vehicle crash process using theoretical principles and a basic understanding of the crash process. It is shown that the fundamental crash process follows a Bernoulli trial with unequal probability of independent events, also known as Poisson trials. We examine the evolution of statistical models as they apply to the motor vehicle crash process, and indicate how well they statistically approximate the crash process. We also present the theory behind dual-state process count models, and note why they have become popular for modeling crash data. A simulation experiment is then conducted to demonstrate how crash data give rise to “excess” zeros frequently observed in crash data. It is shown that the Poisson and other mixed probabilistic structures are approximations assumed for modeling the motor vehicle crash process. Furthermore, it is demonstrated that under certain (fairly common) circumstances excess zeros are observed—and that these circumstances arise from low exposure and/or inappropriate selection of time/space scales and not an underlying dual state process. In conclusion, carefully selecting the time/space scales for analysis, including an improved set of explanatory variables and/or unobserved heterogeneity effects in count regression models, or applying small-area statistical methods (observations with low exposure) represent the most defensible modeling approaches for datasets with a preponderance of zeros

Design of experiments for bivariate binary responses modelled by Copula functions

Optimal design for generalized linear models has primarily focused on univariate data. Often experiments are performed that have multiple dependent responses described by regression type models, and it is of interest and of value to design the experiment for all these responses. This requires a multivariate distribution underlying a pre-chosen model for the data. Here, we consider the design of experiments for bivariate binary data which are dependent. We explore Copula functions which provide a rich and flexible class of structures to derive joint distributions for bivariate binary data. We present methods for deriving optimal experimental designs for dependent bivariate binary data using Copulas, and demonstrate that, by including the dependence between responses in the design process, more efficient parameter estimates are obtained than by the usual practice of simply designing for a single variable only. Further, we investigate the robustness of designs with respect to initial parameter estimates and Copula function, and also show the performance of compound criteria within this bivariate binary setting.

Robust designs for Poisson regression models

We consider the problem of how to construct robust designs for Poisson regression models. An analytical expression is derived for robust designs for first-order Poisson regression models where uncertainty exists in the prior parameter estimates. Given certain constraints in the methodology, it may be necessary to extend the robust designs for implementation in practical experiments. With these extensions, our methodology constructs designs which perform similarly, in terms of estimation, to current techniques, and offers the solution in a more timely manner. We further apply this analytic result to cases where uncertainty exists in the linear predictor. The application of this methodology to practical design problems such as screening experiments is explored. Given the minimal prior knowledge that is usually available when conducting such experiments, it is recommended to derive designs robust across a variety of systems. However, incorporating such uncertainty into the design process can be a computationally intense exercise. Hence, our analytic approach is explored as an alternative.

Robust designs for Poisson regression models

We consider the problem of how to construct robust designs for Poisson regression models. An analytical expression is derived for robust designs for first-order Poisson regression models where uncertainty exists in the prior parameter estimates. Given certain constraints in the methodology, it may be necessary to extend the robust designs for implementation in practical experiments. With these extensions, our methodology constructs designs which perform similarly, in terms of estimation, to current techniques, and offers the solution in a more timely manner. We further apply this analytic result to cases where uncertainty exists in the linear predictor. The application of this methodology to practical design problems such as screening experiments is explored. Given the minimal prior knowledge that is usually available when conducting such experiments, it is recommended to derive designs robust across a variety of systems. However, incorporating such uncertainty into the design process can be a computationally intense exercise. Hence, our analytic approach is explored as an alternative.

Efficient solution of two-sided nonlinear space-fractional diffusion equations using fast Poisson preconditioners

We develop a fast Poisson preconditioner for the efficient numerical solution of a class of two-sided nonlinear space fractional diffusion equations in one and two dimensions using the method of lines. Using the shifted Gr¨unwald finite difference formulas to approximate the two-sided(i.e. the left and right Riemann-Liouville) fractional derivatives, the resulting semi-discrete nonlinear systems have dense Jacobian matrices owing to the non-local property of fractional derivatives. We employ a modern initial value problem solver utilising backward differentiation formulas and Jacobian-free Newton-Krylov methods to solve these systems. For efficient performance of the Jacobianfree Newton-Krylov method it is essential to apply an effective preconditioner to accelerate the convergence of the linear iterative solver. The key contribution of our work is to generalise the fast Poisson preconditioner, widely used for integer-order diffusion equations, so that it applies to the two-sided space fractional diffusion equation. A number of numerical experiments are presented to demonstrate the effectiveness of the preconditioner and the overall solution strategy.

Bivariate genome-wide association study of genetically correlated neuroimaging phenotypes from DTI and MRI through a seemingly unrelated regression model

Large multisite efforts (e.g., the ENIGMA Consortium), have shown that neuroimaging traits including tract integrity (from DTI fractional anisotropy, FA) and subcortical volumes (from T1-weighted scans) are highly heritable and promising phenotypes for discovering genetic variants associated with brain structure. However, genetic correlations (rg) among measures from these different modalities for mapping the human genome to the brain remain unknown. Discovering these correlations can help map genetic and neuroanatomical pathways implicated in development and inherited risk for disease. We use structural equation models and a twin design to find rg between pairs of phenotypes extracted from DTI and MRI scans. When controlling for intracranial volume, the caudate as well as related measures from the limbic system - hippocampal volume - showed high rg with the cingulum FA. Using an unrelated sample and a Seemingly Unrelated Regression model for bivariate analysis of this connection, we show that a multivariate GWAS approach may be more promising for genetic discovery than a univariate approach applied to each trait separately.

Investigating the links between muscle strength, sun exposure, dietary vitamin D intake and the vitamin D status of ambulatory older adults in South East Queensland

Vitamin D deficiency and insufficiency are now seen as a contemporary health problem in Australia with possible widespread health effects not limited to bone health1. Despite this, the Vitamin D status (measured as serum 25-hydroxyvitamin D (25(OH)D)) of ambulatory adults has been overlooked in this country. Serum 25(OH)D status is especially important among this group as studies have shown a link between Vitamin D and fall risk in older adults2. Limited data also exists on the contributions of sun exposure via ultraviolet radiation and dietary intake to serum 25(OH)D status in this population. The aims of this project were to assess the serum 25(OH)D status of a group of older ambulatory adults in South East Queensland, to assess the association between their serum 25(OH)D status and functional measures as possible indicators of fall risk, obtain data on the sources of Vitamin D in this population and assess whether this intake was related to serum 25(OH)D status and describe sun protection and exposure behaviors in this group and investigate whether a relationship existed between these and serum 25(OH)D status. The collection of this data assists in addressing key gaps identified in the literature with regard to this population group and their Vitamin D status in Australia. A representative convenience sample of participants (N=47) over 55 years of age was recruited for this cross-sectional, exploratory study which was undertaken in December 2007 in south-east Queensland (Brisbane and Sunshine coast). Participants were required to complete a sun exposure questionnaire in addition to a Calcium and Vitamin D food frequency questionnaire. Timed up and go and handgrip dynamometry tests were used to examine functional capacity. Serum 25(OH)D status and blood measures of Calcium, Phosphorus and Albumin were determined through blood tests. The Mean and Median serum 25-Hydroxyvitamin D (25(OH)D) for all participants in this study was 85.8nmol/L (Standard Deviation 29.7nmol/L) and 81.0nmol/L (Range 22-158nmol/L), respectively. Analysis at the bivariate level revealed a statistically significant relationship between serum 25(OH)D status and location, with participants living on the Sunshine Coast having a mean serum 25(OH)D status 21.3nmol/L higher than participants living in Brisbane (p=0.014). While at the descriptive level there was an apparent trend towards higher outdoor exposure and increasing levels of serum 25(OH)D, no statistically significant associations between the sun measures of outdoor exposure, sun protection behaviors and phenotypic characteristics and serum 25(OH)D status were observed. Intake of both Calcium and Vitamin D was low in this sample with sixty-eight (68%) of participants not meeting the Estimated Average Requirements (EAR) for Calcium (Median=771.0mg; Range=218.0-2616.0mg), while eighty-seven (87%) did not meet the Adequate Intake for Vitamin D (Median=4.46ug; Range=0.13-30.0ug). This raises the question of how realistic meeting the new Adequate Intakes for Vitamin D is, when there is such a low level of Vitamin D fortification in this country. However, participants meeting the Adequate Intake (AI) for Vitamin D were observed to have a significantly higher serum 25(OH)D status compared to those not meeting the AI for Vitamin D (p=0.036), showing that meeting the AI for Vitamin D may play a significant role in determining Vitamin D status in this population. By stratifying our data by categories of outdoor exposure time, a trend was observed between increased importance of Vitamin D dietary intake as a possible determinant of serum 25(OH)D status in participants with lower outdoor exposures. While a trend towards higher Timed Up and Go scores in participants with higher 25(OH) D status was seen, this was only significant for females (p=0.014). Handgrip strength showed statistically significant association with serum 25(OH)D status. The high serum 25(OH)D status in our sample almost certainly explains the limited relationship between functional measures and serum 25(OH)D. However, the observation of an association between slower Time Up and Go speeds, and lower serum 25(OH)D levels, even with a small sample size, is significant as slower Timed Up and Go speeds have been associated with increased fall risk in older adults3. Multivariable regression analysis revealed Location as the only significant determinant of serum 25(OH)D status at p=0.014, with trends (p=>0.1) for higher serum 25(OH)D being shown for participants that met the AI for Vitamin D and rated themselves as having a higher health status. The results of this exploratory study show that 93.6% of participants had adequate 25(OH)D status-possibly due to measurement being taken in the summer season and the convenience nature of the sample. However, many participants do not meet their dietary Calcium and Vitamin D requirements, which may indicate inadequate intake of these nutrients in older Australians and a higher risk of osteoporosis. The relationship between serum 25(OH)D and functional measures in this population also requires further study, especially in older adults displaying Vitamin D insufficiency or deficiency.