Ranked set sampling from location-scale families of symmetric distributions

Statistical inference based on ranked set sampling has primarily been motivated by nonparametric problems. However, the sampling procedure can provide an improved estimator of the population mean when the population is partially known. In this article, we consider estimation of the population mean and variance for the location-scale families of distributions. We derive and compare different unbiased estimators of these parameters based on independent replications of a ranked set sample of size n. Large sample properties, along with asymptotic relative efficiencies, help identify which estimators are best suited for different location-scale distributions

Bayes Estimation of a Distribution Function Using Ranked Set Samples

Aranked set sample (RSS), if not balanced, is simply a sample of independent order statistics generated from the same underlying distribution F. Kvam and Samaniego (1994) derived maximum likelihood estimates of F for a general RSS. In many applications, including some in the environmental sciences, prior information about F is available to supplement the data-based inference. In such cases, Bayes estimators should be considered for improved estimation. Bayes estimation (using the squared error loss function) of the unknown distribution function F is investigated with such samples. Additionally, the Bayes generalized maximum likelihood estimator (GMLE) is derived. An iterative scheme based on the EM Algorithm is used to produce the GMLE of F. For the case of squared error loss, simple solutions are uncommon, and a procedure to find the solution to the Bayes estimate using the Gibbs sampler is illustrated. The methods are illustrated with data from the Natural Environmental Research Council of Great Britain (1975), representing water discharge of floods on the Nidd River in Yorkshire, Englan

Nonparametric Estimation of the Survival Function Based on Censored Data with Additional Observations from the Residual Distribution

We derive the nonparametric maximum likelihood estimator (NPMLE) of the distribution of the test items using a random, right-censored sample combined with an additional right-censored, residual-lifetime sample in which only lifetimes past a known, fixed time are collected. This framework is suited for samples for which individual test data are combined with left-truncated and randomly censored data from an operating environment. The NPMLE of the survival function using the combined sample is identical to the Kaplan-Meier product-limit estimator only up to the time at which the test items corresponding to the residual sample were known to survive. The limiting distribution for the NPMLE, discussed in detail, leads to confidence bounds for the survival function. For the uncensored case, we study the relative efficiency for the estimator based on the combined sample with respect to the analogous estimator based only on the simple random sample

Nonparametric Bayes Estimation of Contamination Levels using Observations from the Residual Distribution

A nonparametric Bayes estimator of the survival function is derived for right censored data where additional observations from the residual distribution are available. The estimation is motivated by data on contamination concentrations for chromium from one of the EPA\u27s toxic waste sites. The residual sample can be produced by hot spot sampling, where only samples above a given threshold value are collected. The Dirichlet process is used to formulate prior information about the chromium contamination, and we compare the Bayes estimator of the mean concentration level to other estimators currently considered by the EPA and other sources. The Bayes estimator generally out- performs the other estimators under various cost functions. The limiting distribution is the nonparametric maximum likelihood estimator, which is identical to the Kaplan-Meier estimator for concentration values observed below the residual sample threshold. Robustness of the Bayes estimate is examined with respect to misspecification of the prior and its sensitivity to the censoring distribution

Pore Shape Modification of a Microporous Metal-Organic Framework Using High Pressure:Accessing a New Phase with Oversized Guest Molecules

The authors thank the Royal Society of Edinburgh and the Scottish Government for a fellowship to S.A.M. The authors thank EPSRC (EP/J02077X/1) and Leverhulme Trust for a research project grant (RPG-209) for financial support. They also thank the UK Carr Parinello consortium for allocation of computing time on the EPSRC high performance computing resource ARCHER (managed by the Edinburgh Parallel Computing Centre, the EaSTCHEM Research Computing Facility and the University of Edinburgh ECDF facility).Pressures up to 0.8 GPa have been used to squeeze a range of sterically "oversized" C5-C8 alkane guest molecules into the cavities of a small-pore Sc-based metal?organic framework. Guest inclusion causes a pronounced reorientation of the aromatic rings of one-third of the terephthalate linkers, which act as "torsion springs", resulting in a fully reversible change in the local pore structure. The study demonstrates how pressure-induced guest uptake can be used to investigate framework flexibility relevant to "breathing" behavior and to understand the uptake of guest molecules in MOFs relevant to hydrocarbon separation.PostprintPeer reviewe

Microautophagy of the Nucleus Coincides with a Vacuolar Diffusion Barrier at Nuclear–Vacuolar Junctions

Nuclear-vacuolar (NV) junctions are organelle contact sites in yeast. They exclude nuclear pores from the organelle interface. On the vacuolar side, a lipid-dependent process excludes specific membrane proteins, such as V-ATPase, from the contact site. This suggests that NV junctions establish selective diffusion barriers

A Late Form of Nucleophagy in Saccharomyces cerevisiae

Autophagy encompasses several processes by which cytosol and organelles can be delivered to the vacuole/lysosome for breakdown and recycling. We sought to investigate autophagy of the nucleus (nucleophagy) in the yeast Saccharomyces cerevisiae by employing genetically encoded fluorescent reporters. The use of such a nuclear reporter, n-Rosella, proved the basis of robust assays based on either following its accumulation (by confocal microscopy), or degradation (by immunoblotting), within the vacuole. We observed the delivery of n-Rosella to the vacuole only after prolonged periods of nitrogen starvation. Dual labeling of cells with Nvj1p-EYFP, a nuclear membrane reporter of piecemeal micronucleophagy of the nucleus (PMN), and the nucleoplasm-targeted NAB35-DsRed.T3 allowed us to detect PMN soon after the commencement of nitrogen starvation whilst delivery to the vacuole of the nucleoplasm reporter was observed only after prolonged periods of nitrogen starvation. This later delivery of nuclear components to the vacuole has been designated LN (late nucleophagy). Only a very few cells showed simultaneous accumulation of both reporters (Nvj1p-EYFP and NAB35-DsRed.T3) in the vacuole. We determined, therefore, that delivery of the two respective nuclear reporters to the vacuole is temporally and spatially separated. Furthermore, our data suggest that LN is mechanistically distinct from PMN because it can occur in nvj1Δ and vac8Δ cells, and does not require ATG11. Nevertheless, a subset of the components of the core macroautophagic machinery is required for LN as it is efficiently inhibited in null mutants of several autophagy-related genes (ATG) specifying such components. Moreover, the inhibition of LN in some mutants is accompanied by alterations in nuclear morphology