Bias Reduction for the Maximum Likelihood Estimators of the Parameters in the Half-Logistic Distribution

We derive analytic expressions for the biases of the maximum likelihood estimators of the scale parameter in the half-logistic distribution with known location, and of the location parameter when the latter is unknown. Using these expressions to bias-correct the estimators is highly effective, without adverse consequences for estimation mean squared error. The overall performance of the first of these bias-corrected estimators is slightly better than that of a bootstrap bias-corrected estimator. The bias-corrected estimator of the location parameter significantly out-performs its bootstrapped-based counterpart. Taking computational costs into account, the analytic bias corrections clearly dominate the use of the bootstrap.     

Practice standards in Australia: implications for social work education

In 2003, the Australian Association of Social Work launched Practice Standards that define minimal expectations for all social workers. The Practice Standards are outcome focused and specify what social workers are expected to achieve in the areas of Direct Practice, Service Management, Organizational Development and System Change, Policy, Research, and Education and Professional Development. This paper outlines the rationale for developing the Practice Standards, the process undertaken and the need for social work educators to build a curriculum that prepares students to be competent and effective in a complex working environment.     

The density function and the MSE dominance of the pre-test estimator in a heteroscedastic linear regression model with omitted variables

In this paper, we consider a heteroscedastic linear regression model with omitted variables. We derive the density function of the pre-test estimator consisting of the two-stage Aitken estimator (2SAE) and the ordinary least squares estimator (OLSE) after the pre-test for homoscedasticity. We also derive the first two moments based on the density function and show the sufficient condition for the pre-test estimator to dominate the 2SAE in terms of the MSE. Our numerical evaluations show that when this sufficient condition does not hold and when the magnitude of the specification error is large, the pre-test estimator can be dominated by the 2SAE, and further, the 2SAE can be dominated by the OLSE.     

Ergonomics and Kaizen as strategies for competitiveness: a theoretical and practical in an automotive industry

With increased international competitiveness in the automotive industry, came the concern of the companies save costs and lower production costs. For this purpose many ways are designed to reduce costs and waste of raw materials and reduce activities that do not aggregate value to manufacturing processes. In the early XVII appears the manufacturing system, which processes were hard with little concern for the health and safety of employees and conditions of the workplace. After the advent of the production system called lean manufacturing, a new paradigm in terms of production system capable of providing high levels of productivity and quality. It is based on waste elimination that occur during the production process. After began a new way of thinking, creating a culture of continuous improvement and lean process with no waste and reducing costs, without neglecting the welfare worker and improving the conditions of their work environment. This paper presents a reflection on the application of ergonomics in a lean production system of an automotive industry, using methodology based on the Kaizen (Continuous Improvement) to gain performance and improving the conditions of the workplace, also will be presented with positive and negative points in using this methodology in relation to ergonomics. The research will be conducted by collecting data 'in loco' and interviews with workers. Some studies show that in companies that are lean system and using the methodology of Kaizen, the results of product quality, levels of absenteeism and accidents are better than those obtained in companies that do not apply the same concept.     

Bootstrap bias corrections for ensemble methods

This paper examines the use of a residual bootstrap for bias correction in machine learning regression methods. Accounting for bias is an important obstacle in recent efforts to develop statistical inference for machine learning. We demonstrate empirically that the proposed bootstrap bias correction can lead to substantial improvements in both bias and predictive accuracy. In the context of ensembles of trees, we show that this correction can be approximated at only double the cost of training the original ensemble. Our method is shown to improve test set accuracy over random forests by up to 70% on example problems from the UCI repository.     

Mobility and Nunavut Inuit youth: lessons from Northern Youth Abroad

In this paper we examine the impacts of the mobility that Nunavut Inuit youth experience with Northern Youth Abroad (NYA), a Canadian volunteer travel program that sends northern youth to southern Canada and Botswana. Using a case study approach and drawing from archival research and interviews with 10 former participants, a staff member, and a member of the board of directors, we argue that NYA illustrates how volunteer travel programs for Indigenous youth can yield distinct benefits for its participants in comparison to mainstream volunteer travel programs, particularly with regard to participants' sense of cultural identity and pride. By enabling participants to foster career goals and a deeper sense of cultural pride, NYA presents a counter-example to some of the common critiques of volunteer tourism programs, and illustrates the complex interconnections between cultural pride, well-being, and career advancement for Nunavut Inuit youth.     

Local quadratic estimation of the curvature in a functional single index model

The nonlinear responses of species to environmental variability can play an important role in the maintenance of ecological diversity. Nonetheless, many models use parametric nonlinear terms which pre-determine the ecological conclusions. Motivated by this concern, we study the estimate of the second derivative (curvature) of the link function in a functional single index model. Since the coefficient function and the link function are both unknown, the estimate is expressed as a nested optimization. We first estimate the coefficient function by minimizing squared error where the link function is estimated with a Nadaraya-Watson estimator for each candidate coefficient function. The first and second derivatives of the link function are then estimated via local-quadratic regression using the estimated coefficient function. In this paper, we derive a convergence rate for the curvature of the nonlinear response. In addition, we prove that the argument of the linear predictor can be estimated root-n consistently. However, practical implementation of the method requires solving a nonlinear optimization problem, and our results show that the estimates of the link function and the coefficient function are quite sensitive to the choices of starting values.