Simple and accurate one-sided inference from signed roots of likelihood ratios

The authors propose two methods based on the signed root of the likelihood ratio statistic for one-sided testing of a simple null hypothesis about a scalar parameter in the presence of nuisance parameters. Both methods are third-order accurate and utilise simulation to avoid the need for onerous analytical calculations characteristic of competing saddlepoint procedures. Moreover, the new methods do not require specification of ancillary statistics. The methods respect the conditioning associated with similar tests up to an error of third order, and conditioning on ancillary statistics to an error of second order

Tandem synthesis of alternating polyesters from renewable resources

The vast majority of commodity materials are obtained from petrochemical feedstocks. These resources will plausibly be depleted within the next 100 years, and the peak in global oil production is estimated to occur within the next few decades. In this regard, biomass represents an abundant carbon-neutral renewable resource for the production of polymers. Here we report a new strategy, based on tandem catalysis, to obtain renewable materials. Commercially available complexes are found to be efficient catalysts for alternating polyesters from the cyclization of dicarboxylic acids followed by alternating copolymerization of the resulting anhydrides with epoxides. This operationally simple method is an attractive strategy for the production of new biodegradable polyesters

The role of intracellular trafficking of CdSe/ZnS QDs on their consequent toxicity profile

Nanoparticle interactions with cellular membranes and the kinetics of their transport and localization are important determinants of their functionality and their biological consequences. Understanding these phenomena is fundamental for the translation of such NPs from in vitro to in vivo systems for bioimaging and medical applications. Two CdSe/ZnS quantum dots (QD) with differing surface functionality (NH2 or COOH moieties) were used here for investigating the intracellular uptake and transport kinetics of these QDs.status: publishe

Conditional properties of unconditional parametric bootstrap procedures for inference in exponential families

Summary Higher-order inference about a scalar parameter in the presence of nuisance parameters can be achieved by bootstrapping, in circumstances where the parameter of interest is a component of the canonical parameter in a full exponential family. The optimal test which is approximated is a conditional one, based on conditioning on the sufficient statistic for the nuisance parameter. A bootstrap procedure which ignores the conditioning 1 is shown to have desirable conditional properties, in providing third-order relative accuracy in approximation of p-values associated with the optimal test, in both continuous and discrete models. The bootstrap approach is equivalent to third-order to analytical approaches, and is demonstrated in a number of examples to give very accurate approximations even in very small sample sizes

Conditional properties of unconditional parametric bootstrap procedures for inference in exponential families

Higher-order inference about a scalar parameter in the presence of nuisance parameters can be achieved by bootstrapping, in circumstances where the parameter of interest is a component of the canonical parameter in a full exponential family. The optimal test, which is approximated, is a conditional one based on conditioning on the sufficient statistic for the nuisance parameter. A bootstrap procedure that ignores the conditioning is shown to have desirable conditional properties in providing third-order relative accuracy in approximation of p-values associated with the optimal test, in both continuous and discrete models. The bootstrap approach is equivalent to third-order analytical approaches, and is demonstrated in a number of examples to give very accurate approximations even for very small sample sizes. Copyright 2008, Oxford University Press.

Objective Bayes and conditional inference in exponential families

Objective Bayes methodology is considered for conditional frequentist inference about a canonical parameter in a multi-parameter exponential family. A condition is derived under which posterior Bayes quantiles match the conditional frequentist coverage to a higher-order approximation in terms of the sample size. This condition is on the model, not on the prior, and it ensures that any first-order probability matching prior in the unconditional sense automatically yields higher-order conditional probability matching. Objective Bayes methods are compared to parametric bootstrap and analytic methods for higher-order conditional frequentist inference. Copyright 2010, Oxford University Press.

Simple and Accurate One-Sided Inference From Signed Roots of Likelihood Ratios

The authors propose two methods based on the signed root of the likelihood ratio statistic for one-sided testing of a simple null hypothesis about a scalar parameter in the presence of nuisance parameters. Both methods are third-order accurate and utilise simulation to avoid the need for onerous analytical calculations characteristic of competing saddlepoint procedures. Moreover, the new methods do not require specification of ancillary statistics. The methods respect the conditioning associated with similar tests up to an error of third order, and conditioning on ancillary statistics to an error of second order. R ESUM E Les auteurs proposent deux methodes permettant, a partir de la racine signee du rapport des vraisemblances, d'e#ectuer un test unilateral d'une hypothese nulle simple sur un parametre d'echelle, en presence de parametres nuisibles. Par le biais de simulations, ces methodes permettent d'obtenir une precision du troisieme ordre tout en evitant les calculs analytique..