1,500 research outputs found
Emergent requirements for supporting introductory programming
The problems associated with learning and teaching first year University Computer Science (CS1) programming classes are summarized showing that various support tools and techniques have been developed and evaluated. From this review of applicable support the paper derives ten requirements that a support tool should have in order to improve CS1 student success rate with respect to learning and understanding
New Zealand's Approach to Parallel Imported Goods and the Copyright Amendment Act 1998
The prohibition against parallel importing has attracted growing international attention, especially given the global trend towards freeing up international trade. This article examines New Zealand's new statutory provisions relating to parallel importing following the Copyright Amendment Act 1998. This Act legalises parallel importing in New Zealand in given circumstances. This article critically assesses the economic arguments for parallel importing to analyse whether these support the rationale behind the amendment. It then questions whether the potential defects in the preamendment law were sufficiently severe to warrant the fundamental policy reversal effected by the Act, and discusses alternative avenues of reform and whether these are viable in New Zealand. Underlying the discussion is a consideration of whether the same law should apply to all types of goods, and whether New Zealand's approach should focus more rigorously on the varying characteristics of individual markets
Looking Deeper at the Reform of New Zealand's Roading System: Driven by Economic Rationalism or an Example of Public Choice Theory in Action
Successive New Zealand governments have investigated reforming New Zealand's road network since 1994. The draft Roads Bill 1998 creates a system of road provision operated according to commercial principles. Roads remain publicly owned, but operational management passes from Transit and 74 territorial authorities to newly incorporated "public road companies". The reforms are examined from "a law and economics" perspective, which finds that while efficiency gains may be generated, the proposed institutional design may prevent these gains from being realised to the proposed extent. The reform proposal is then analysed using public choice theory. This analysis finds that while some elements of the proposal are consistent with legislator and bureaucrat self interest, it contains too many politically sensitive variables to displace the Government's stated "efficiency gains" premise as the overriding motivation for reform.  
General Design Bayesian Generalized Linear Mixed Models
Linear mixed models are able to handle an extraordinary range of
complications in regression-type analyses. Their most common use is to account
for within-subject correlation in longitudinal data analysis. They are also the
standard vehicle for smoothing spatial count data. However, when treated in
full generality, mixed models can also handle spline-type smoothing and closely
approximate kriging. This allows for nonparametric regression models (e.g.,
additive models and varying coefficient models) to be handled within the mixed
model framework. The key is to allow the random effects design matrix to have
general structure; hence our label general design. For continuous response
data, particularly when Gaussianity of the response is reasonably assumed,
computation is now quite mature and supported by the R, SAS and S-PLUS
packages. Such is not the case for binary and count responses, where
generalized linear mixed models (GLMMs) are required, but are hindered by the
presence of intractable multivariate integrals. Software known to us supports
special cases of the GLMM (e.g., PROC NLMIXED in SAS or glmmML in R) or relies
on the sometimes crude Laplace-type approximation of integrals (e.g., the SAS
macro glimmix or glmmPQL in R). This paper describes the fitting of general
design generalized linear mixed models. A Bayesian approach is taken and Markov
chain Monte Carlo (MCMC) is used for estimation and inference. In this
generalized setting, MCMC requires sampling from nonstandard distributions. In
this article, we demonstrate that the MCMC package WinBUGS facilitates sound
fitting of general design Bayesian generalized linear mixed models in practice.Comment: Published at http://dx.doi.org/10.1214/088342306000000015 in the
Statistical Science (http://www.imstat.org/sts/) by the Institute of
Mathematical Statistics (http://www.imstat.org
Ordinal Probit Functional Regression Models with Application to Computer-Use Behavior in Rhesus Monkeys
Research in functional regression has made great strides in expanding to
non-Gaussian functional outcomes, however the exploration of ordinal functional
outcomes remains limited. Motivated by a study of computer-use behavior in
rhesus macaques (\emph{Macaca mulatta}), we introduce the Ordinal Probit
Functional Regression Model or OPFRM to perform ordinal function-on-scalar
regression. The OPFRM is flexibly formulated to allow for the choice of
different basis functions including penalized B-splines, wavelets, and
O'Sullivan splines. We demonstrate the operating characteristics of the model
in simulation using a variety of underlying covariance patterns showing the
model performs reasonably well in estimation under multiple basis functions. We
also present and compare two approaches for conducting posterior inference
showing that joint credible intervals tend to out perform point-wise credible.
Finally, in application, we determine demographic factors associated with the
monkeys' computer use over the course of a year and provide a brief analysis of
the findings
Measurement error caused by spatial misalignment in environmental epidemiology
Copyright @ 2009 Gryparis et al - Published by Oxford University Press.In many environmental epidemiology studies, the locations and/or times of exposure measurements and health assessments do not match. In such settings, health effects analyses often use the predictions from an exposure model as a covariate in a regression model. Such exposure predictions contain some measurement error as the predicted values do not equal the true exposures. We provide a framework for spatial measurement error modeling, showing that smoothing induces a Berkson-type measurement error with nondiagonal error structure. From this viewpoint, we review the existing approaches to estimation in a linear regression health model, including direct use of the spatial predictions and exposure simulation, and explore some modified approaches, including Bayesian models and out-of-sample regression calibration, motivated by measurement error principles. We then extend this work to the generalized linear model framework for health outcomes. Based on analytical considerations and simulation results, we compare the performance of all these approaches under several spatial models for exposure. Our comparisons underscore several important points. First, exposure simulation can perform very poorly under certain realistic scenarios. Second, the relative performance of the different methods depends on the nature of the underlying exposure surface. Third, traditional measurement error concepts can help to explain the relative practical performance of the different methods. We apply the methods to data on the association between levels of particulate matter and birth weight in the greater Boston area.This research was supported by NIEHS grants ES012044 (AG, BAC), ES009825 (JS, BAC), ES007142 (CJP), and ES000002 (CJP), and EPA grant R-832416 (JS, BAC)
Targeted Derepression of the Human Immunodeficiency Virus Type 1 Long Terminal Repeat by Pyrrole-Imidazole Polyamides
The host factor LSF represses the human immunodeficiency virus type 1 long terminal repeat (LTR) by mediating recruitment of histone deacetylase. We show that pyrrole-imidazole polyamides targeted to the LTR can specifically block LSF binding both in vitro and within cells via direct access to chromatin, resulting in increased LTR expression
An Informative Bayesian Structural Equation Model to Assess Source-Specific Health Effects of Air Pollution
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