Estimation for the general sample selection models

Consider a general regression model with an arbitrary and unknown link function and a stochastic selection variable that determines whether the outcome variable is observable or missing. The paper proposes U-statistics that are based on kernel functions as estimators for the directions of the parameter vectors in the link function and the selection equation, and shows that these estimators are consistent and asymptotically normal.

Induced smoothing for rank regression with censored survival times

Adaptions of weighted rank regression to the accelerated failure time model for censored survival data have been successful in yielding asymptotically normal estimates and flexible weighting schemes to increase statistical efficiencies. However, for only one simple weighting scheme, Gehan or Wilcoxon weights, are estimating equations guaranteed to be monotone in parameter components, and even in this case are step functions, requiring the equivalent of linear programming for computation. The lack of smoothness makes standard error or covariance matrix estimation even more difficult. An induced smoothing technique overcame these difficulties in various problems involving monotone but pure jump estimating equations, including conventional rank regression. The present paper applies induced smoothing to the Gehan-Wilcoxon weighted rank regression for the accelerated failure time model, for the more difficult case of survival time data subject to censoring, where the inapplicability of permutation arguments necessitates a new method of estimating null variance of estimating functions. Smooth monotone parameter estimation and rapid, reliable standard error or covariance matrix estimation is obtained.

Variable Size Population NSGA-II: VPNSGA-II

Multi-objective optimization is an active field of research with broad applicability in aeronautics. This report details a variant of the original NSGA-II software aimed to improve the performances of such a widely used Genetic Algorithm in finding the optimal Pareto-front of a Multi-Objective optimization problem for the use of UAV and aircraft design and optimsaiton. Original NSGA-II works on a population of predetermined constant size and its computational cost to evaluate one generation is O(mn^2 ), being m the number of objective functions and n the population size. The basic idea encouraging this work is that of reduce the computational cost of the NSGA-II algorithm by making it work on a population of variable size, in order to obtain better convergence towards the Pareto-front in less time. In this work some test functions will be tested with both original NSGA-II and VPNSGA-II algorithms; each test will be timed in order to get a measure of the computational cost of each trial and the results will be compared.

Development and Simulation of a Variable Rate Controller for an Air search and Air Sampling Unmanned Aerial System

This report describes the development and simulation of a variable rate controller for a 6-degree of freedom nonlinear model. The variable rate simulation model represents an off the shelf autopilot. Flight experiment involves risks and can be expensive. Therefore a dynamic model to understand the performance characteristics of the UAS in mission simulation before actual flight test or to obtain parameters needed for the flight is important. The control and guidance is implemented in Simulink. The report tests the use of the model for air search and air sampling path planning. A GUI in which a set of mission scenarios, in which two experts (mission expert, i.e. air sampling or air search and an UAV expert) interact, is presented showing the benefits of the method.

Subtypes of psychotic-like experiences are differentially associated with suicidal ideation, plans and attempts in young adults

Psychotic-like experiences (PLEs) have been associated with increased risk of suicidality, but it is unclear whether the level of risk varies with different types of PLE. A cross-sectional online survey was completed by 1610 university students. Respondents completed the Community Assessment of Psychic Experiences-15 (CAPE-P15) assessing PLEs on three subscales: Perceptual Abnormalities (PA), Persecutory Ideation (PI) and Bizarre Experiences (BE). Lifetime suicidal ideation, plans and attempts, cannabis, ecstasy and methamphetamine use and family history of mental disorder were also assessed. Multinomal logistic regression was used to examine unique determinants of lifetime suicidality, defined as any history of (i) suicidal ideation or plans and (ii) any attempt, relative to no lifetime history of suicidality. A lifetime history of PA and PI provided significant unique contributions to the prediction of suicide risk, after control for other significant predictors. BE were not associated with any suicide variable demonstrating the variation in risk of suicidality with different types of PLEs. Perceptual abnormalities and persecutory ideation as measured by the CAPE-P15 are the PLEs associated with a higher risk of lifetime suicidality.

Analytical model of reactive transport processes with spatially variable coefficients

Analytical solutions of partial differential equation (PDE) models describing reactive transport phenomena in saturated porous media are often used as screening tools to provide insight into contaminant fate and transport processes. While many practical modelling scenarios involve spatially variable coefficients, such as spatially variable flow velocity, v(x), or spatially variable decay rate, k(x), most analytical models deal with constant coefficients. Here we present a framework for constructing exact solutions of PDE models of reactive transport. Our approach is relevant for advection-dominant problems, and is based on a regular perturbation technique. We present a description of the solution technique for a range of one-dimensional scenarios involving constant and variable coefficients, and we show that the solutions compare well with numerical approximations. Our general approach applies to a range of initial conditions and various forms of v(x) and k(x). Instead of simply documenting specific solutions for particular cases, we present a symbolic worksheet, as supplementary material, which enables the solution to be evaluated for different choices of the initial condition, v(x) and k(x). We also discuss how the technique generalizes to apply to models of coupled multispecies reactive transport as well as higher dimensional problems.

Marker selection by Akaike information criterion and Bayesian information criterion

We carried out a discriminant analysis with identity by descent (IBD) at each marker as inputs, and the sib pair type (affected-affected versus affected-unaffected) as the output. Using simple logistic regression for this discriminant analysis, we illustrate the importance of comparing models with different number of parameters. Such model comparisons are best carried out using either the Akaike information criterion (AIC) or the Bayesian information criterion (BIC). When AIC (or BIC) stepwise variable selection was applied to the German Asthma data set, a group of markers were selected which provide the best fit to the data (assuming an additive effect). Interestingly, these 25-26 markers were not identical to those with the highest (in magnitude) single-locus lod scores.

A new family of multivariate heavy-tailed distributions with variable marginal amounts of tailweight: Application to robust clustering

We propose a family of multivariate heavy-tailed distributions that allow variable marginal amounts of tailweight. The originality comes from introducing multidimensional instead of univariate scale variables for the mixture of scaled Gaussian family of distributions. In contrast to most existing approaches, the derived distributions can account for a variety of shapes and have a simple tractable form with a closed-form probability density function whatever the dimension. We examine a number of properties of these distributions and illustrate them in the particular case of Pearson type VII and t tails. For these latter cases, we provide maximum likelihood estimation of the parameters and illustrate their modelling flexibility on simulated and real data clustering examples.

The relationship between forward head posture and cervical muscle performance in healthy individuals

Background Forward head postures (FHP) are proposed to adversely load cervical spine structures. Neck muscles provide support for the neck, and thus an imbalance in neck muscle performance could potentially contribute to the development of FHP. Previous studies have not considered the interaction of multiple muscle groups with regard to postural orientation. Given the interdependence of muscles along the cervical spine for optimal orientation and physical support of the vertebral column, the performance of a single muscle group may not accurately reflect the coordinated ability of the muscles to maintain a neutral neck posture. Purpose The purpose of this study was to investigate the relationship between FHP and the balance between the cervical extensor and flexor muscle groups in healthy individuals. We hypothesised that the magnitude of FHP would be associated with the strength and endurance performance ratios between the cervical extensor and flexor muscle groups. Methods Twenty male and 24 female volunteers were photographed in the sagittal plane wearing surface markers. The FHP of each participant was measured via the tragus-sternum marker distance over two conditions: (1)in relaxed standing and (2)during a sustained sitting task. Maximal strength (Nm) and endurance (s) performance of the extensor and flexor muscle groups were recorded at the upper (craniocervical flexion/extension (CCF/CCE)) and lower (cervicothoracic flexion/extension (CTF/CTE)) cervical regions. Muscle performance measures were expressed as extension:flexion ratios and their relation to FHP evaluated. A stepwise multiple regression analysis using backward elimination was utilised to examine the relationship between the postural measures and the muscle performance ratio measures. Separate models were used for the two different postural conditions (standing, sustained sitting). Gender was included as a constant correction factor in all regression models. Where gender was a significant variable in the model, analyses were repeated separately for males and females. Results Greater FHP in standing was significantly associated with reduced proportional CTE to CCF strength in females (R2 = 0.21, P = 0.03) and greater proportional CTE to CTF strength in males (R2 = 0.23, P = 0.03). A greater drift into FHP during sustained sitting was associated with a relative reduction in CCE endurance proportional to CTF endurance in females only (R2 = 0.27, P = 0.017). Conclusion(s) This initial study indicates that the balance in performance between the cervical flexor and extensor muscle groups may impact FHP in healthy individuals. However, the findings were inconsistent across different muscle performance ratios and gender. Larger scale studies are therefore now needed to further clarify the relationship between FHP and muscle performance. Implications The findings suggest that relative performance of the various cervical muscle groups needs to be accounted for when considering postural correction strategies in the clinical setting, as is often recommended.

Land use regression model (LUR) for ultrafine particles in Brisbane

Traffic-related air pollution has been associated with a wide range of adverse health effects. One component of traffic emissions that has been receiving increasing attention is ultrafine particles(UFP, < 100 nm), which are of concern to human health due to their small diameters. Vehicles are the dominant source of UFP in urban environments. Small-scale variation in ultrafine particle number concentration (PNC) can be attributed to local changes in land use and road abundance. UFPs are also formed as a result of particle formation events. Modelling the spatial patterns in PNC is integral to understanding human UFP exposure and also provides insight into particle formation mechanisms that contribute to air pollution in urban environments. Land-use regression (LUR) is a technique that can use to improve the prediction of air pollution.

The assessment of ecological condition in south-east Queensland, Australia: An evaluation of reliability across variable environments and surrogate efficacy for biodiversity values

Multimetric ecological condition assessment has become an important biodiversity management tool. This study was the first to examine the reliability of these ecological surrogates across variable environments, and the implications for surrogate efficacy. It was demonstrated that through strategic application and design of the multimetric ecological condition index, the effects of environmental gradients and disturbance regimes can be mitigated, and that ecological condition assessment may serve as a scientifically rigorous approach for conservation planning.