3,420 research outputs found
On A Simpler and Faster Derivation of Single Use Reliability Mean and Variance for Model-Based Statistical Testing
Markov chain usage-based statistical testing has proved sound and effective in providing audit trails of evidence in certifying software-intensive systems. The system end-toend reliability is derived analytically in closed form, following an arc-based Bayesian model. System reliability is represented by an important statistic called single use reliability, and defined as the probability of a randomly selected use being successful. This paper continues our earlier work on a simpler and faster derivation of the single use reliability mean, and proposes a new derivation of the single use reliability variance by applying a well-known theorem and eliminating the need to compute the second moments of arc
failure probabilities. Our new results complete a new analysis that could be shown to be simpler, faster, and more direct while also rendering a more intuitive explanation. Our new
theory is illustrated with three simple Markov chain usage models with manual derivations and experimental results
Estimation and model selection in generalized additive partial linear models for correlated data with diverging number of covariates
We propose generalized additive partial linear models for complex data which
allow one to capture nonlinear patterns of some covariates, in the presence of
linear components. The proposed method improves estimation efficiency and
increases statistical power for correlated data through incorporating the
correlation information. A unique feature of the proposed method is its
capability of handling model selection in cases where it is difficult to
specify the likelihood function. We derive the quadratic inference
function-based estimators for the linear coefficients and the nonparametric
functions when the dimension of covariates diverges, and establish asymptotic
normality for the linear coefficient estimators and the rates of convergence
for the nonparametric functions estimators for both finite and high-dimensional
cases. The proposed method and theoretical development are quite challenging
since the numbers of linear covariates and nonlinear components both increase
as the sample size increases. We also propose a doubly penalized procedure for
variable selection which can simultaneously identify nonzero linear and
nonparametric components, and which has an asymptotic oracle property.
Extensive Monte Carlo studies have been conducted and show that the proposed
procedure works effectively even with moderate sample sizes. A pharmacokinetics
study on renal cancer data is illustrated using the proposed method.Comment: Published in at http://dx.doi.org/10.1214/13-AOS1194 the Annals of
Statistics (http://www.imstat.org/aos/) by the Institute of Mathematical
Statistics (http://www.imstat.org
China's Foreign Aid Policy and Architecture
As China's engagement in low?income countries has deepened, particularly in Africa, so has criticism of China's development programmes and practices. New developments in Africa and the international aid architecture warrant a re?examination of China's foreign assistance and development architecture, and its capacity in managing this growing engagement. This article outlines the modes of Chinese foreign assistance, the institutional arrangements and the principles that guide it. It argues that, while China's foreign aid has been characterised by several strengths, including its practical orientation, its consistency of principles, and its focus on high?level exchanges, these same features may also be fault lines for both the future effectiveness of China's aid programmes, and its international reputation as a rising power in development. Evaluation and future reform of China's aid architecture is needed in order to enhance China's aid capacity and effectiveness
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